AI tools are significantly accelerating software development and changing how developers work with code. These tools serve as real-time copilots, automating repetitive tasks, executing tasks, writing documentation, and more. OpenAI Codex, for example, is a coding agent designed to assist developers through tasks like code generation, debugging, and automated pull request (PR) creation.

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Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attributes 30% of merged PRs to agents. Spotify reports 650+ agent-generated PRs per month. Tools like Claude Code and Codex make hundreds of API calls per coding session, each carrying the full conversation history. Behind every one of these workflows is an inference stack under…

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The development of socially acceptable nuclear reactors requires that they are safe, clean, efficient, economical, and sustainable. Meeting these requirements calls for new approaches, driving growing interest in Small Modular Reactors (SMRs) and in Generation IV designs. SMRs aim to improve project economics by standardising designs and shifting construction to controlled manufacturing…

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Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate data pipelines, countless lines of code, and lengthy development cycles. NVIDIA DeepStream 9 removes these development barriers using coding agents, such as Claude Code or Cursor, to help you easily create deployable, optimized code that brings your vision AI applications to…

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Developing real-time vision AI applications presents a significant challenge for developers, often demanding intricate data pipelines, countless lines of code, and lengthy development cycles. NVIDIA DeepStream 9 removes these development barriers using coding agents, such as Claude Code or Cursor, to help you easily create deployable, optimized code that brings your vision AI applications to…

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For decades, computational chemistry has faced a tug-of-war between accuracy and speed. Ab initio methods like density functional theory (DFT) provide high fidelity but are computationally expensive, limiting researchers to systems of a few hundred atoms. Conversely, classical force fields are fast but often lack the chemical accuracy required for complex bond-breaking or transition-state analysis.

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When you’re writing CUDA applications, one of the most important things you need to focus on to write great code is data transfer performance. This applies to both single-GPU and multi-GPU systems alike. One of the tools you can use to understand the memory characteristics of your GPU system is NVIDIA NVbandwidth. In this blog post, we’ll explore what NVbandwidth is, how it works…

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NVIDIA Ising is the world’s first family of open AI models for building quantum processors, launching with two model domains: Ising Calibration and Ising Decoding. Both target the fundamental challenge in quantum computing—qubits are inherently noisy. The best quantum processors make an error roughly once in every thousand operations. To become useful accelerators for scientific and…

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The release of MiniMax M2.7 adds enhancements to the popular MiniMax M2.5 model, built for agentic harnesses, and other complex use cases in fields such as reasoning, ML research workflows, software, engineering, and office work. The open weights release of MiniMax M2.7 is now available through NVIDIA and across the open source inference ecosystem. The MiniMax M2 series is a sparse mixture-of…

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Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling for over 65% of TOP500 systems. Most organizations running large-scale AI training have years of investment in Slurm job scripts, fair-share policies, and accounting workflows. The challenge is getting Slurm scheduling capabilities onto Kubernetes—the standard platform for managing GPU…

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Training LLMs requires periodic checkpoints. These full snapshots of model weights, optimizer states, and gradients are saved to storage so training can resume after interruptions. At scale, these checkpoints become massive (782 GB for a 70B model) and frequent (every 15-30 minutes), generating one of the largest line items in a training budget. Most AI teams chase GPU utilization…

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Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming protein complexes whose structures are described in the hierarchy of protein structure as the quaternary representation. This represents one level of complexity up from tertiary representations, the 3D structure of monomers…

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Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and industrial systems, long before anything ships to the factory floor. At GTC 2026, NVIDIA highlighted physical AI as a key direction for robotics and digital twins, where policies are trained and validated against physically grounded environments.

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The NVIDIA GB200 NVL72 and NVIDIA GB300 NVL72 systems, featuring NVIDIA Blackwell architecture, are rack-scale supercomputers. They’re designed with 18 tightly coupled compute trays, massive GPU fabrics, and high-bandwidth networking packaged as a unit. For AI architects and HPC platform operators, the challenge isn’t just racking and stacking hardware—it’s turning infrastructure into safe…

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Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak chip specifications. Rigorous AI inference performance benchmarks are critical to understanding real-world token output, which drives AI factory revenue. MLPerf Inference v6.0 is the latest in a series of industry benchmarks that measure…

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In vision AI systems, model throughput continues to improve. The surrounding pipeline stages must keep pace, including decode, preprocessing, and GPU scheduling. In the previous post, Build High-Performance Vision AI Pipelines with NVIDIA CUDA-Accelerated VC-6, this was described as the data-to-tensor gap—a performance mismatch between AI pipeline stages. The SMPTE VC-6 (ST 2117-1) codec…

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In algorithmic trading, reducing response times to market events is crucial. To keep pace with high-speed electronic markets, latency-sensitive firms often use specialized hardware like FPGAs and ASICs. Yet, as markets grow more efficient, traders increasingly depend on advanced models such as deep neural networks to enhance profitability. Because implementing these complex models on low-level…

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The Gemmaverse expands with the launch of the latest Gemma 4 multimodal and multilingual models, designed to scale across the full spectrum of deployments, from NVIDIA Blackwell in the data center to Jetson at the edge. These models are suited to meet the growing demand for local deployment for AI development and prototyping, secure on-prem requirements, cost efficiency, and latency-sensitive use…

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CUDA 13.1 introduced CUDA Tile, a next generation next generation tile-based GPU programming paradigm designed to make fine-grained parallelism more accessible and flexible. One of its key strengths is language openness: any programming language can target CUDA Tile, enabling developers to bring tile-based GPU acceleration into a wide range of ecosystems. In response to overwhelming demand…

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Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak chip specifications. Rigorous AI inference performance benchmarks are critical to understanding real-world token output, which drives AI factory revenue. MLPerf Inference v6.0 is the latest in a series of industry benchmarks that measure…

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In today’s AI factory environment, performance is not theoretical. It is economic, competitive, and existential. A 1% drop in usable GPU time can mean millions of tokens lost per hour. Minutes of congestion can cascade into hours of recovery. A rack-level power oversubscription can lead to stranded power and reduced tokens per watt, silently eroding factory output at scale. As AI factories scale…

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Spatial computing is moving from visualization to active collaboration, adding increasingly more GPU demands on XR hardware to render photorealistic, physics-accurate, high-fidelity spatial content in real time. Meanwhile, developers have had to maintain separate codebases for every platform, each with different toolchains, SDKs, and streaming protocols. At NVIDIA GTC 2026, NVIDIA CloudXR 6.0…

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Delivering high-fidelity VR and AR experiences to enterprise users has typically required native application development, custom device management, and complex deployment pipelines. Now, with the new JavaScript SDK NVIDIA CloudXR.js, developers can stream GPU-rendered immersive content directly to a standard web browser—no app store, no installs, no device-specific builds. NVIDIA CloudXR.

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In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition (ASR) or text-to-speech (TTS) models may require only 10 GB of VRAM, yet occupy an entire GPU in standard Kubernetes deployments. Because the scheduler maps a model to one or more GPUs and can’t easily share across GPUs across models…

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In the current state of automotive radar, machine learning engineers can’t work with camera-equivalent raw RGB images. Instead, they work with the output of radar constant false alarm rate (CFAR), which is similar to computer vision (CV) edge detections. The communications and compute architectures haven’t kept pace with trends in AI and the needs of Level 4 autonomy, despite radar being a staple…

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Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or small molecule. The search space for possible amino acid sequence permutations and resulting 3D protein structures for a designed binder is vast, and achieving strong, specific binding requires careful optimization of the interactions between…

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In the AI era, power is the ultimate constraint, and every AI factory operates within a hard limit. This makes performance per watt—the rate at which power is converted into revenue-generating intelligence—the defining metric for modern AI infrastructure. AI data centers now operate as token factories tied directly to the energy ecosystem, where access to land, power…

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Agentic AI is an ecosystem where specialized models work together to handle planning, reasoning, retrieval, and safety guardrailing. As these systems scale, developers need models that can understand real-world multimodal data, converse naturally with users globally, and operate safely across languages and modalities. At GTC 2026, NVIDIA introduced a new generation of NVIDIA Nemotron models…

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Industrial and medical systems are rapidly increasing the use of high-performance AI to improve worker productivity, human-machine interaction, and downtime management. From factory automation cells to autonomous mobile platforms to surgical rooms, operators are deploying increasingly complex generative AI models, more sensors, and higher‑fidelity data streams at the edge.

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AI is moving from experimentation to production. However, most data enterprises need exists outside the public cloud. This includes sensitive information like patient records, market research, and legacy systems containing enterprise knowledge. There’s also a risk of using private data with AI models, and adoption is often slowed or blocked by privacy and trust concerns.

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As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages have fundamentally different compute profiles, yet traditional deployments force them onto the same hardware, leaving GPUs underutilized and scaling inflexible. Disaggregated serving addresses this by splitting the inference pipeline…

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While consumer AI offers powerful capabilities, workplace tools often suffer from disjointed data and limited context. Built with LangChain, the NVIDIA AI-Q blueprint is an open source template that bridges this gap. LangChain recently introduced an enterprise agent platform built with NVIDIA AI to support scalable, production-ready agent development. This tutorial, available as an NVIDIA…

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AI-native services are exposing a new bottleneck in AI infrastructure: As millions of users, agents, and devices demand access to intelligence, the challenge is shifting from peak training throughput to delivering deterministic inference at scale—predictable latency, jitter, and sustainable token economics. NVIDIA announced at GTC 2026 that telcos and distributed cloud providers are…

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Physics forms the foundation of robotic simulation, enabling realistic modeling of motion and interaction. For tasks like locomotion and manipulation, simulators must handle complex dynamics such as contact forces and deformable objects. While most engines trade off speed for realism, Newton—a GPU-accelerated, open source simulator—is designed to do both. Newton 1.0 GA…

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Autonomous AI agents are driving the next wave of AI innovation. These agents must often manage long-running tasks that use multiple communication channels and background subprocesses simultaneously to explore options, test solutions, and generate optimal results. This places extreme demands on local compute. NVIDIA DGX Spark provides the performance necessary for autonomous agents to execute…

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Reasoning models are growing rapidly in size and are increasingly being integrated into agentic AI workflows that interact with other models and external tools. Deploying these models and workflows in production environments requires distributing them across multiple GPU nodes, which demands careful orchestration and coordination across GPUs. NVIDIA Dynamo 1.0—available now—addresses these…

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AI‑native organizations increasingly face scaling challenges as agentic AI workflows drive context windows to millions of tokens and models scale toward trillions of parameters. These systems rely on agentic long‑term memory for context that persists across turns, tools, and sessions so agents can build on prior reasoning instead of starting from scratch on every request.

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NVIDIA Groq 3 LPX is a new rack-scale inference accelerator for the NVIDIA Vera Rubin platform, designed for the low-latency and large-context demands of agentic systems. Co-designed with the NVIDIA Vera Rubin NVL72, LPX equips the AI factory with an engine optimized for fast, predictable token generation, while Vera Rubin NVL72 remains the flexible, general-purpose workhorse for training and…

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AI has evolved from assistants following your directions to agents that act independently. Called claws, these agents can take a goal, figure out how to achieve it, and execute indefinitely—while leaving you out of the loop. The more capable claws become, the harder they are to trust. And their self-evolving autonomy changes everything about the environment in which they operate.

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Building AI factories is complex and requires efficient integration across compute, networking, security, and storage systems. To achieve rapid Time to AI and strong ROI, the new NVIDIA DSX Air is enabling organizations to simulate their entire AI factory infrastructure in the cloud—covering compute, networking, storage, and security. Being able to design, test, and optimize systems before…

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AI is evolving, and reasoning models are increasing token demand, placing new requirements on every layer of AI infrastructure. More than ever, compute must scale efficiently to maximize token production and improve productivity for model creators and users. Modern GPUs operate at peak capacity, pushing throughput higher every generation, but system performance is increasingly gated by the…

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Artificial intelligence is token-driven. Every prompt, reasoning step, and agent interaction generates tokens. Over the past year, token consumption has grown multifold and now exceeds 10 quadrillion tokens per year. And while the majority of tokens have been generated from humans interacting with AI, the new era is one in which most tokens will be generated from AI interacting with AI.

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The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware training data. Without diverse and representative datasets, these systems don’t get proper training and face testing risks due to poor generalization, limited exposure to real-world variations, and unpredictable behavior in edge cases. Collecting massive real-world datasets for…

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Computer-Aided Engineering (CAE) is shifting from human-driven workflows toward AI-driven ones, including physics foundation models that generalize across geometries and operating conditions. Unlike LLMs, these models depend on large volumes of high-fidelity, physics-compliant data. Recent scaling-law work on computational fluid dynamics (CFD) surrogates indicates that simulation-generated…

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Every AI cluster running on Kubernetes requires a full software stack that works together, from low-level driver and kernel settings to high-level operator and workload configurations. You get one cluster working, and spend days getting the next one to match. Upgrade a component, and something else breaks. Move to a new cloud and start over. AI Cluster Runtime is a new open-source project designed…

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Physical AI is rapidly evolving, from next-generation software-defined autonomous vehicles (AVs) to humanoid robots. The challenge is no longer how to run a large language model (LLM), but how to enable high-fidelity reasoning, real-time multimodal interaction, and trajectory planning within strict power and latency envelopes. NVIDIA TensorRT Edge-LLM, a high-performance C++ inference runtime…

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Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning, coding, and long-context analysis, while remaining efficient enough to run continuously at scale. Multi-agent systems generate up to 15x the tokens of standard chats, re-sending history, tool outputs, and reasoning steps at every turn. Over long tasks…

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NVIDIA RTX ray tracing and AI-powered neural rendering technologies are redefining how games are made, enabling a new standard for visuals and performance. At GDC 2026, NVIDIA unveiled the latest path tracing innovations elevating visual fidelity, on-device AI models enabling players to interact with their favorite experiences in new ways, and enterprise solutions accelerating game development…

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Agentic code assistants are moving into daily game development as studios build larger worlds, ship more DLCs, and support distributed teams. These assistants can accelerate development by helping with tasks like generating gameplay scaffolding, refactoring repetitive systems, and answering engine-specific questions faster. This post outlines how developers can build reliable AI coding…

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CUDA 13.2 arrives with a major update: NVIDIA CUDA Tile is now supported on devices of compute capability 8.X architectures (NVIDIA Ampere and NVIDIA Ada), as well as 10.X and 12.X architectures (NVIDIA Blackwell). In an upcoming release of the CUDA Toolkit, all GPU architectures starting with Ampere will be fully supported. If you’re using Ampere, Ada, or Blackwell GPU architectures…

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In the rapidly evolving landscape of large language model (LLM) development, NVIDIA Megatron Core has emerged as the foundational framework for training massive transformer models at scale. The open source library offers industry-leading parallelism and GPU-optimized performance. Now developed GitHub-first in the NVIDIA/Megatron-LM repo, Megatron Core is increasingly shaped by contributions from…

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Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and request handling across many GPUs and nodes to scale to more users while reducing latency. Distributed inference frameworks use techniques such as disaggregated serving, KV cache loading, and wide expert parallelism. In disaggregated serving environments…

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Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming engineering problem. The ideal configuration for any given workload (such as hardware, parallelism, and prefill/decode split) resides in a massive, multi-dimensional search space that is impossible to explore manually or through exhaustive testing. AIConfigurator…

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Large language models (LLMs) are revolutionizing the financial trading landscape by enabling sophisticated analysis of vast amounts of unstructured data to generate actionable trading insights. These advanced AI systems can process financial news, social media sentiment, earnings reports, and market data to predict stock price movements and automate investment strategies with unprecedented…

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In this post, we dive into one of the most critical workloads in modern AI: Flash Attention, where you’ll learn: Environment requirements: See the quickstart doc for more information on installing cuTile Python. The attention mechanism is the computational heart of transformer models. Given a sequence of tokens, attention enables each token to “look at” every other…

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NVIDIA CUDA Tile is one of the most significant additions to NVIDIA CUDA programming and unlocks automatic access to tensor cores and other specialized hardware. Earlier this year, NVIDIA released cuTile for Python, giving Python developers a natural way to write high-performance GPU kernels. Now, the same programming model is available in Julia through cuTile.jl. In this blog post…

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NVIDIA ACE is a suite of technologies for building AI agents for gaming. ACE provides ready-to-integrate cloud and on-device AI models for every part of in-game characters, from speech to intelligence to animation. To run these models alongside the game engine efficiently, the NVIDIA In-Game Inferencing (NVIGI) SDK includes a set of performant libraries that developers can integrate into C++…

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Autonomous networks are quickly becoming one of the top priorities in telecommunications. According to the latest NVIDIA State of AI in Telecommunications report, 65% of operators said AI is driving network automation, and 50% named autonomous networks as the top AI use case for ROI. Yet many telcos still report gaps in AI and data science expertise. This makes it difficult to scale safe…

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To make 6G a reality, the telecom industry must overcome a fundamental challenge: how to design, train, and validate AI-native networks that are too complex to be tested in the physical world. The NVIDIA Aerial Omniverse Digital Twin (AODT) solves this by enabling a continuous integration/continuous development (CI/CD)-style workflow where Radio Access Network (RAN) software is trained…

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Alibaba has introduced the new open source Qwen3.5 series built for native multimodal agents. The first model in this series is a ~400B parameter native vision-language model (VLM) with reasoning built with a hybrid architecture of mixture of experts (MoE) and Gated Delta Networks. Qwen3.5 can understand and navigate user interfaces, which improves on the previous generation of VLMs. Qwen3.5…

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Organizations deploying LLMs are challenged by inference workloads with different resource requirements. A small embedding model might use only a few gigabytes of GPU memory, while a 70B+ parameter LLM could require multiple GPUs. This diversity often leads to low average GPU utilization, high compute costs, and unpredictable latency. The problem isn’t just about packing more workloads onto…

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LLM context lengths are exploding, and architectures are moving toward complex attention schemes like Multi-Head Latent Attention (MLA) and Grouped Query Attention (GQA). As a result, AI ”speed of thought” is increasingly governed not by the massive throughput of matrix multiplications, but by the transcendental math of the softmax function. Transcendentals refer to functions that cannot be…

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As the sizes of AI models and datasets continue to increase, relying only on higher-precision BF16 training is no longer sufficient. Key challenges such as training throughput expectations, memory limits, and rising costs are becoming the primary barriers to scaling transformer models. Using lower-precision training can address these challenges. By reducing the numeric precision used during…

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NVIDIA flagship data center GPUs in the NVIDIA Ampere, NVIDIA Hopper, and NVIDIA Blackwell families all feature non-uniform memory access (NUMA) behaviors, but expose a single memory space. Most programs therefore do not have an issue with memory non-uniformity. However, as bandwidth increases in newer generation GPUs, there are significant performance and power gains to be had when taking into…

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As AI workloads scale, achieving high throughput, efficient resource usage, and predictable latency becomes essential. NVIDIA Run:ai addresses these challenges through intelligent scheduling and dynamic GPU fractioning. GPU fractioning is wholly delivered by NVIDIA Run:ai in any environment—cloud, NCP, and on-premises. This post presents the joint benchmarking effort between NVIDIA and AI…

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Python dominates machine learning for its ergonomics, but writing truly fast GPU code has historically meant dropping into C++ to write custom kernels and to maintain bindings back to Python. For most Python developers and researchers, this is a significant barrier to entry. Frameworks like PyTorch address this by implementing kernels in CUDA C++—either handwritten or by leveraging libraries…

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As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost requirements. Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents, demands high throughput, low latency, and predictable service-level performance.

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Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms, and embedded metadata. Financial reports carry critical insights in tables, engineering manuals rely on diagrams, and legal documents often include annotated or scanned content. Retrieval-augmented generation (RAG) was created to ground…

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Building robust, intelligent robots requires testing them in complex environments. However, gathering data in the physical world is expensive, slow, and often dangerous. It is nearly impossible to safely train for real-world critical risks, such as high-speed collisions or hardware failures. Worse, real-world data is usually biased toward “normal” conditions, leaving robots unprepared for the…

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Scientists and engineers who design and build unique scientific research facilities face similar challenges. These include managing massive data rates that exceed current computational infrastructure capacity to extract scientific insights and driving the experiments in real time. These challenges are obstacles to maximizing the impact of scientific discoveries and significantly slow the pace of…

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NVIDIA TensorRT LLM enables developers to build high-performance inference engines for large language models (LLMs), but deploying a new architecture traditionally requires significant manual effort. To address this challenge, today we are announcing the availability of AutoDeploy as a beta feature in TensorRT LLM. AutoDeploy compiles off-the-shelf PyTorch models into inference-optimized…

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The latest AI models continue to grow in size and complexity, demanding increasing amounts of compute performance for training and inference—far beyond what Moore’s Law can keep up with. That’s why NVIDIA engages in extreme codesign. Designing across multiple chips and a mountain of software cohesively enables large generational leaps in AI factory performance and efficiency.

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Specialized AI models are built to perform specific tasks or solve particular problems. But if you’ve ever tried to fine-tune or distill a domain-specific model, you’ve probably hit a few blockers, such as: These challenges often prevent promising AI projects from progressing beyond the experimental phase. This post walks you through how to remove all four of these blockers using a…

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Painkiller RTX sets a new standard for how small teams can balance massive visual ambition with limited resources by integrating generative AI. By upscaling thousands of legacy textures into high-quality Physically Based Rendering (PBR) materials—a process that would have traditionally taken years—the team dramatically reduced the burden of repetitive work. This approach was especially…

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Kimi K2.5 is the newest open vision language model (VLM) from the Kimi family of models. Kimi K2.5 is a general-purpose multimodal model that excels in current high-demand tasks such as agentic AI workflows, chat, reasoning, coding, mathematics, and more. The model was trained using the open source Megatron‑LM framework. Megatron-LM provides accelerated computing for scalability and GPU…

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What if your AI agent could instantly parse complex PDFs, extract nested tables, and “see” data within charts as easily as reading a text file? With NVIDIA Nemotron RAG, you can build a high-throughput intelligent document processing pipeline that handles massive document workloads with precision and accuracy. This post walks you through the core components of a multimodal retrieval pipeline…

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Large language models (LLMs) are rapidly expanding their context windows, with recent models supporting sequences of 128K tokens, 256K tokens, and beyond. However, training these models with extended context lengths presents significant computational and communication challenges. As context lengths grow, the memory and communication overhead of attention mechanisms scale quadratically…

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In LLM training, Expert Parallel (EP) communication for hyperscale mixture-of-experts (MoE) models is challenging. EP communication is essentially all-to-all, but due to its dynamics and sparseness (only topk experts per AI token instead of all experts), it’s challenging to implement and optimize. This post details an efficient MoE EP communication solution, Hybrid-EP, and its use in the…

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NVIDIA CUDA Tile is a GPU-based programming model that targets portability for NVIDIA Tensor Cores, unlocking peak GPU performance. One of the great things about CUDA Tile is that you can build your own DSL on top of it. This post shares the work NVIDIA is doing to integrate CUDA Tile as a backend for OpenAI Triton, an open source Python DSL designed to write DL kernels for GPUs.

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Sparse tensors are vectors, matrices, and higher-dimensional generalizations with many zeros. They are crucial in various fields such as scientific computing, signal processing, and deep learning due to their efficiency in storage, computation, and power. Despite their benefits, handling sparse tensors manually or through existing libraries is often cumbersome, error-prone, nonportable…

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AI coding agents enable developers to work faster by streamlining tasks and driving automated, test-driven development. However, they also introduce a significant, often overlooked, attack surface by running tools from the command line with the same permissions and entitlements as the user, making them computer use agents, with all the risks those entail. The primary threat to these tools is…

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NVIDIA Run:ai v2.24 introduces time-based fairshare, a new scheduling mode that brings fair-share scheduling with time awareness for over-quota resources to Kubernetes clusters. This capability, built on the open source KAI Scheduler that powers NVIDIA Run:ai, addresses a long-standing challenge in shared GPU infrastructure. Consider two teams with equal priority sharing a cluster.

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This post introduces Dynamic Context Parallelism (Dynamic-CP), a scheduling approach in NVIDIA Megatron-Core used for LLM post-training or DiT pre-training. It dynamically selects the CP size per microbatch to efficiently handle variable-length sequences, achieving up to 1.48x speedup on real-world datasets. In large-scale model training, an often-overlooked bottleneck arises from the…

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Advances in AI architectures have unlocked multimodal functionality, enabling transformer models to process multiple forms of data in the same context. For instance, vision language models (VLMs) can generate output from combined image and text input, enabling developers to build systems that interpret graphs, process camera feeds, or operate with traditionally human interfaces like desktop…

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Recent advances in large-scale diffusion models have revolutionized generative AI across multiple domains, from image synthesis to audio generation, 3D asset creation, molecular design, and beyond. These models have demonstrated unprecedented capabilities in producing high-quality, diverse outputs across various conditional generation tasks. Despite these successes…

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Deploying AI applications across diverse consumer hardware has traditionally forced a trade-off. You can optimize for specific GPU configurations and achieve peak performance at the cost of portability. Alternatively, you can build generic, portable engines and leave performance on the table. Bridging this gap often requires manual tuning, multiple build targets, or accepting compromises.

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Global climate models are good at the big picture—but local climate extremes, like hurricanes and typhoons, often disappear in the details. Those patterns are still there—you just need the right tools to unlock them in high-resolution climate data. Using NVIDIA Earth‑2, this blog post shows you how to downscale coarse climate projections into higher-resolution, bias‑corrected fields—revealing…

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NVIDIA cuOpt is a GPU-accelerated optimization engine designed to deliver fast, high-quality solutions for large, complex decision-making problems. Mixed integer programming (MIP) is a technique for solving problems. It can be modeled by a set of linear constraints, with some of the variables able to assume only integer values. The types of problems that can be modeled as MIP are numerous and…

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NVIDIA DLSS 4 with Multi Frame Generation has become the fastest-adopted NVIDIA gaming technology ever. Over 250 games and apps use it to make real-time path tracing possible—and upcoming titles for 2026, including PRAGMATA and Resident Evil Requiem, also plan to incorporate the software. At CES 2026, the technology became even more powerful. NVIDIA introduced DLSS 4.5…

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This blog post is part of a series designed to help developers learn NVIDIA CUDA Tile programming for building high-performance GPU kernels, using matrix multiplication as a core example. In this post, you’ll learn: Before you begin, be sure your environment meets the following requirements (see the quickstart for more information): Environment requirements: Install…

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What if your computer-use agent could learn a new Command Line Interface (CLI)—and operate it safely without ever writing files or free-typing shell commands? In Part 1 of our series on building a computer use agent, we built a custom Bash computer-use agent using NVIDIA Nemotron in just one hour. In this sequel, we’ll take it further by teaching the same reasoning model with no prior…

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In 2025, NVIDIA partnered with Black Forest Labs (BFL) to optimize the FLUX.1 text-to-image model series, unlocking FP4 image generation performance on NVIDIA Blackwell GeForce RTX 50 Series GPUs. As a natural extension of the latent diffusion model, FLUX.1 Kontext [dev] proved that in-context learning is a feasible technique for visual-generation models, not just large language models (LLMs).

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The C++ template library CUB is a go-to for high-performance GPU primitive algorithms, but its traditional “two-phase” API, which separates memory estimation from allocation, can be cumbersome. While this programming model offers flexibility, it often results in repetitive boilerplate code. This post explains the shift from this API to the new CUB single-call API introduced in CUDA 13.1…

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We’re proud to keep XINKER PRO accessible to everyone. Despite adding the powerful XINKER Cards feature, our pricing remains the same:

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XINKER is more than just a platform — it’s a community. It’s your ultimate destination for mastering business strategies, discovering passive income opportunities, and learning the principles of success. With XINKER PRO, you gain access to tools and resources that empower you to:

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Complexity and Precision

Beef Wellington is renowned for its complexity and the precision required to perfect it. Similarly, mastering business strategies involves intricate planning and execution. The dish represents:

• Attention to Detail: Just as each step in making Beef Wellington must be carefully executed, successful business strategies require meticulous planning and attention to detail.
• Layered Excellence: The layers of flavors in Beef Wellington mirror the layers of skills and knowledge needed in business.

Craftsmanship and Mastery

Creating Beef Wellington demands culinary craftsmanship, akin to the expertise needed to excel in the business world. Both require:

• Skill and Patience: Achieving financial success is a journey that demands skill, patience, and dedication—qualities reflected in crafting this gourmet dish.
• Innovation and Tradition: The fusion of traditional and innovative elements in Beef Wellington parallels how XINKER blends time-tested principles with cutting-edge strategies.

The Song's Design

The song is crafted to inspire and motivate, using the metaphor of Beef Wellington to convey:

• The Pursuit of Excellence: Encouraging users to strive for excellence in their entrepreneurial endeavors, just as a chef aims for perfection with each Wellington.
• Strategic Layering: Highlighting the importance of building a solid foundation and layering skills and knowledge to achieve success.

Bridging Culinary Art and Business

The connection between Beef Wellington and business is a celebration of mastery, whether in the kitchen or the boardroom. Both require:

• Dedication: A commitment to continuous improvement and learning.
• Community and Sharing: Just as a delicious Beef Wellington is shared among friends, XINKER fosters a community where ideas and successes are shared.

Full Lyrics:

Start with the beef, a delicious quest,

Salt and pepper, for the very best.

A culinary art we're set to unfold,

With rich flavors, bold and gold.

Beef tenderloin, seared to gold,

Olive oil in, let the story unfold.

Season well, then let it cool,

This is our first flavor rule.

Rich in taste, with a crust divine,

Capturing flavors, oh so fine.

Precise in measure, cooking's grace,

The heart of the dish, setting the pace.

Mushrooms chopped, four hundred grams,

Garlic, thyme, in the pan.

Sauté them until they're dry,

Rich in flavor, let them lie.

Savory blend, oh what a treat,

Melded together, a flavor feat.

A bouquet of aromas fills the air,

A symphony of taste, beyond compare.

Lay out bacon, eight slices flat,

Puff pastry ready for the wrap.

Beat one egg, for the glaze,

Prepare it all for baking's blaze.

Tender bacon, a smoky note,

Wrapped around like a loving coat.

Golden layers, hold secrets tight,

Creating a dish of pure delight.

Spread the mushroom, bacon's embrace,

Wrap it tight, no time to waste.

Into the fridge for a little rest,

While pastry waits for its test.

Chill it well, let flavors blend,

Awaiting the warmth of the oven's send.

Patience here is virtue true,

In every step, perfection pursue.

Preheat the oven, two hundred strong,

Bake till the color is golden and long.

Rest it briefly, slice and see,

A perfect dish for you and me.

Crusty magic, a golden glow,

Inside the layers, flavor flows.

A masterpiece served with pride,

Taste sensations, side by side.

Careful slicing reveals the art,

Layer by layer, a work of heart.

Accompanied by sides, fresh and bright,

An orchestra of tastes, pure delight.

Wine to pair, enhance the taste,

A dining experience, none to waste.

Sharing moments, joy and cheer,

This culinary treasure, brings us near.

The Wellington's ready, a meal divine,

A classic flavor, for every time.

Slice it gently, serve with grace,

A culinary journey you'll embrace.

Hearts content, with every bite,

A feast of memories in the night.

Conclusion

XINKER’s theme song uses the art of cooking Beef Wellington as a metaphor for the intricate and rewarding journey of mastering business strategies. It’s a creative way to illustrate the dedication, precision, and layered approach necessary for achieving entrepreneurial excellence.

That’s right, this brand is called “Chagee” and it is the hottest tea brand in the catering industry recently.

And it took only 6 years to open more than 3,500 stores. What does this mean? It took Heytea 12 years to break through 3,000 stores.

Although some brands can open stores at this speed, this is in the tea beverage market. Tea beverages are the most competitive market in the catering industry . In 2017, it was difficult for tea beverages to have a chance to become a phenomenal brand. However, Chagee was able to carve out a path when it was founded in 2017. Moreover, it opened overseas less than two years after its establishment, and currently has more than 100 stores in Southeast Asia and other countries.

The Overlord Tea Princess was like holding a big knife and riding a dark horse, charging into this fiercely competitive battlefield and quickly seizing one territory after another.

But generally, when opening stores quickly, the profitability of a single store is ignored. It is said online that the monthly turnover of a single store of Chagee can exceed one million. I also asked my friends who opened Chagee directly and indirectly, and they said that there are indeed million-dollar stores, but it also depends on the location of the store in the business district. Not all stores are like this.

Even so, what is even more surprising is that most of Chagee's store menus only have a dozen products , and they are basically a simple combination of tea and milk. This product efficiency is very enviable. You know, most milk tea shops can't achieve a monthly turnover of 200,000 with 100 products.

(Screenshot of the mini program menu at the Chagee store in Guangzhou)

In recent years, we have been getting deeper and deeper into the catering industry. I understand that the success of a brand is caused by many factors , and it is difficult to copy and learn directly. But when I learned about the Chagee brand, I found that there are many underlying logics and many brand strategies behind it, which are worth learning from for catering people.

Moreover, the brand strategy and development of Bawang is also a classic case of a restaurant brand combining capitalization, digitalization and branding, and it also reflects many current consumption trends . The business logic of the restaurant industry is no longer the same as it was in the past, and many things have undergone earth-shaking changes.

When we study a brand, we don’t learn its superficial actions, but the brand and business logic behind it, and think about how to apply effective practices to our own brand.

So today I plan to use an article to carefully analyze the brand strategy of Chagee, hoping to give some inspiration to friends who are in the catering industry (a few days ago, I also shared the brand strategy of Chagee in the form of a short video on my video account, but short videos are difficult to show a lot of thoughts and more content in detail, so it is more convenient for everyone to think about it by writing articles. Next, I will share whatever I think of, the full text without deletions).

This article will analyze and share the logic of Chagee from the perspective of the brand, and will also include some of my personal thoughts. It will mainly share from 0 to 1, 1 to 10, 10 to 1000 to 10000:

1. Strategic positioning: brand positioning that starts from the end

2. Model design: From 0 to 1, learn from the proven successful single-store profit model

3. Brand leverage: From 1 to 10, leverage and leverage to quickly increase brand power

4. Fission expansion: from 10 to 10,000 nationwide and global layout

(Special note: I have no vested interest in Overlord Tea Princess. This article is just for sharing and does not provide any investment or other reference advice. If there is anything wrong, please correct me.)

1. Strategic positioning: brand positioning that starts from the end

We have been focusing on brand consulting in the catering industry for many years and found that there are generally two ways to do catering branding in China:

One is that for some reason, you open a store with your family, and then you find it is quite profitable, so you open branches. As you have more stores, you start to build teams, supply chains and other standardized operations. The Mixue Ice City and Wallace that you see belong to this method. I summarize this method as the "from low to high" method.

Another approach is to go from high to low - first select products and tracks, then formulate appropriate strategic positioning to compete with differentiation. Then, it is necessary to build and optimize the profit model of a single store, build a team, attract investment, build a store opening rhythm and supply chain, introduce capital and other professional operations . This is also called the brand approach of starting from the end . Chagee belongs to this approach, and so does Luckin Coffee.

With the development of the catering industry, there will be more and more catering brands that have top-level design and an end-to-end approach from the very beginning.

1.

Okay, let’s first take a look at why Zhang Junjie, the founder of Chagee, chose the tea beverage track.

We often say that choosing products is like choosing your life . If you don’t choose the right products, even the best people can only make a little money. If you choose the right track, a little effort may bring you dozens or even hundreds of times the return of others.

If you ask what kind of stores are the most common on the street, they must be tea shops, which are opened next to each other and are basically chain stores.

Mixue Bingcheng is the representative of brands with a price of less than 10 yuan, and brands with a price of 10-20 yuan include Cha Baidao, Shuyi Shaoxiancao, Guming, Yidiandian, etc., all of which have more than 1,000 stores. Heytea, which represents the mid-to-high-end price range, also opened for franchising last year, and has blossomed all over the country, with more than 3,000 stores.

Tea beverages are the most competitive sector in the catering industry, but they are also large in scale, having reached a market value of 300 billion by 2023.

However, the tea beverage industry is a track where the head effect is relatively obvious, and it is basically the same few who are fighting each other. What they compete for is the supply chain and limited business district resources . The rest can only be the boss in the region, or open stores in some places where competition is not fierce to avoid competition from big brands.

It can be said that before Chagee was established in 2017, the tea beverage market seemed to have little chance of producing another phenomenal brand.

Then why did founder Zhang Junjie choose the tea beverage sector when he founded Chagee in 2017?

From public information and personal analysis, I think there are two main aspects:

1) Familiar with and good at the tea industry

Zhang Junjie had been working in the tea industry before. He started working in a milk tea shop when he was 17 years old, and later became the regional director of a milk tea brand. Before founding Bawang, he had been working in this industry for nearly 10 years and had rich experience and knowledge in the tea industry.

I think this is the main reason. Most people who start a business will generally choose something they are good at and have an advantage in as their entry point, and the success rate will be higher.

2) Tea drinks are in line with the goal of becoming a global brand

Before establishing Chagee, Zhang Junjie went abroad for inspection and exchange. Seeing that Starbucks had opened stores all over the world, he thought to himself: Why can Starbucks, which sells coffee, open stores all over the world, but our Chinese brands can’t open stores all over the world?

So from the very beginning, our brother Jie had a strategic goal of globalization.

But if a category wants to be accepted by people all over the country or the world, it must have two basics:

First, the category itself must be addictive and acceptable to any human being.

Starbucks sells coffee, and coffee is addictive. And if you look at the top five brands in China with 10,000 stores, Mixue Ice City's milk tea, Wallace's fried chicken, Zhengxin Chicken's fried chicken, Juewei Duck Neck's spicy marinated food, and Luckin's coffee, they are all basically addictive products, and no matter where you are from, you can accept them.

Many regional specialties are difficult to sell across the country because their taste is not universal and addictive.

The second is the output of cultural potential.

When McDonald's and KFC opened in China, people didn't even know what a hamburger was. People didn't come to McDonald's for the hamburgers, but because they admired European and American culture at the time. This behavior of consuming because of admiration or pursuit of a certain culture is very common.

Behind the brand is the category, behind the category is the culture, and behind the culture is the economy. Only when the economy is good will people admire and consume your culture.

Sometimes I think some so-called fashion shows abroad are ugly, but the public still likes this kind of aesthetic. Aesthetics are transmitted from high to low, and this "high" is economic development. For example, more than ten or twenty years ago, Hong Kong's economy was developed, and Hong Kong movies were popular in the mainland. Although the taste of Hong Kong-style tea restaurants was not very addictive, they were also popular throughout the country.

If you open an African restaurant in China, even Africans in China may feel embarrassed to go in and consume.

With the rise of China's economy, Chinese catering brands have already possessed this cultural potential to go global . Tea is an element that can best represent Chinese culture, and tea is also addictive. Therefore, tea brands have the opportunity to open all over the world.

Bawang’s current brand slogan, “Meet friends from all over the world with Oriental tea”, directly states the purpose of the global brand.

2.

Okay, here comes the second question. The competition in the tea beverage market is so fierce, what brand direction should we take in order to stand out?

When we look at a brand, we should not only look at its current success, but also look at how it got started, what problems it encountered, how it made decisions, why it did so, and what inspiration can we get from it?

In 2017, tea drinks were divided into two categories: Chinese tea drinks and modern tea drinks. Modern tea drinks are divided into three categories:

The first category is "pearl milk tea" . Many brands originally came from Taiwan, such as COCO, Yidiandian, etc.

The second category is called "drinks turned into desserts ", and the representative brand is Shuyi Grass Jelly.

The third category is "fruit tea" , which was also the hottest direction at the time. Because fruits have the advantages of "health" and high value, many brands that are not specialized in fruit tea have also launched fruit tea related products. Among them, Gu Ming and Cha Baidao are the representatives of the 10-20 yuan price range, and Heytea and Nayuki are priced at 30 yuan. Some social space scenes are also added.

There is also a category called new Chinese tea drinks, which is fresh milk tea , which is mainly milk and tea. Then some traditional Chinese elements are added, which is also called traditional Chinese fresh milk tea. In 2017, the most representative one was Cha Yan Yue Se, which has been opening stores in Changsha. With the trend of the traditional Chinese concept and Changsha's status as an internet celebrity city, it quickly became popular on social media, but it only opened stores in Changsha.

(Data source: DT Business Observation "Number and proportion of milk tea SKUs of different categories in 2023")

Seeing this, if it were you, which direction would you choose?

We all know now that Bawang has chosen the direction of traditional Chinese style fresh milk tea .

Many people on the Internet are saying that Chagee imitates the Chinese style fresh milk tea positioning of Cha Yan Yue Se. But when Bawang was first established in 2017, the most popular was the fruit tea represented by Heytea and Nayuki.

Why doesn’t Chagee make fruit tea but instead imitate the Chinese style fresh milk tea which has a relatively small market share?

Here we can see the ambition and strategic analysis ability of Zhang Junjie, the founder of Chagee.

First of all, the fruit tea market has already become a leader, and it will be difficult for you to surpass them if you try to compete.

There is a very important principle in our brand positioning.

Rather than being better, it’s better to be different.

Only by making a differentiated positioning can you have a chance to stand out.

Cha Yan Yue Se was founded just two years ago and has been in Changsha. So at that time, there was no national leading brand in the field of traditional Chinese milk tea. This was a potential market that had not been fully occupied, and there was an opportunity for a leading brand of traditional Chinese milk tea to emerge.

But I don’t think this is the most important reason. I think the more important reason is that as mentioned earlier, Ba Wang Cha Ji had the goal of becoming a global brand from the beginning.

Zhang Junjie hopes that Bawang can be opened all over the world like Starbucks. But for restaurants to be opened all over the world, there is another very critical issue, which is the standardization and stability of the supply chain.

Look at Starbucks, it is mainly coffee beans, and the supply chain is very simple, stable and standardized. The same is true for McDonald's chicken.

One of the important reasons why the Yu Ni Zai Yi Qi restaurant I worked for was able to open more than 2,000 stores in just a few years was the standardization of the Basa fish supply chain and the popularization of cold chain logistics, which ensured a stable supply of food ingredients to stores across the country and even the world.

Let's go back to the several directions of tea drinks. First of all, pearl milk tea does not conform to the overall changes in consumption trends. The raw materials of tea dessert products are relatively complex, and the standardized production efficiency and supply chain efficiency at the store end are not high.

Let's look at fruit tea. It is difficult to standardize the fruit supply chain, and the management cost is high. Although fruit is healthy and valuable, it has to take into account seasonality, logistics and transportation losses, and storage conditions. If it is not done well, there will be no profit, and it is not stable enough.

Just think about it, a box of mangoes is shipped from Yunnan to Singapore, and the whole process needs to be cold-chained and delivered, and then peeled and processed in various ways when it arrives at the store. How high is the cost and loss? This is also why fruit tea brands dare not expand blindly and quickly, because the fruit supply chain cannot keep up.

The core of the fresh milk tea supply chain is tea and milk. If you ship a box of tea to different parts of the world, the cost is relatively low and there won’t be much loss. Moreover, China’s tea supply chain is very mature and stable. Not to mention milk. This provides the supply chain foundation for Chagee’s global layout.

This is also why the hot pot market is so large. Everyone likes to make hot pot instead of stir-frying because the hot pot supply chain is simple and stable, and the store does not need a chef to ensure a stable taste.

So, if you want to become a national chain brand, you cannot just consider whether your product is popular, you also have to consider the back-end supply chain issues.

Some categories achieve relatively stable store-side products by solving the problem of kitchen staff and business model. For example, Xiaocaiyuan, a stir-fry restaurant, has opened 500 to 600 stores. It does not rely on the standardization of the supply chain, but relies on talent organization to solve the problem of stable products. However, it is difficult to quickly replicate the opening of stores. However, it is already very impressive for such a Chinese restaurant to open hundreds of stores.

3.

After solving the general problem, we need to solve another problem:

There are so many tea brands on the street now, why should customers choose Chagee?

You have to give customers reasons to buy from you.

Whether this reason for purchase is strong or not depends on the difference between customer value and price.

I think there is no such thing as expensive or cheap products in this world, only whether they are worth it or not. The more valuable a product is, the easier it is to impress customers and make repeat purchases.

Either you provide greater customer value than other competitors at the same price. Or if your meat filling is fresher and larger, I will choose you first.

Or you can offer a lower price for the same value. If you sell a cup of fresh fruit tea for 12 yuan and Mixue Ice City sells it for 6 yuan, many customers will naturally choose Mixue Ice City, which has a better price-performance ratio.

Ba Wang Cha Ji’s strategy is to set price first and then determine value .

After all, positioning comes before pricing, and pricing determines the world.

The higher the price, the narrower the customer base. The lower the price, the wider the customer base .

Bawang's price range is 15-20 yuan, which is a mid-range price. Although the customer base is not as large as that of the 15 yuan and below, the base of this price range is not small , which is enough to open 10,000 stores in China . This pricing also provides a certain gross profit to create a Starbucks-style social space experience.

Okay, now that the pricing is determined, let’s take a look at what customer value Chagee provides and see if it’s worth it.

The size of customer value depends on whether customer needs are better met.

Value that does not satisfy customer needs is not called value, it can only be called "self-satisfaction".

This has to do with Ba Wang Cha Ji’s accurate grasp of consumer demand.

What do today’s consumers fear most when drinking milk tea?

"China's New Tea Drinks Big Data Research and Consumer Behavior Survey Data" shows that 49.4% of consumers are worried that consuming new tea drinks is bad for their health, and 42.2% of consumers are afraid of gaining weight.

Concerns about gaining weight, too much sugar, high calories, etc. Behind this is the " healthification " that everyone has been discussing recently.

Consumers' health concerns are also forcing the tea industry to make a healthy transformation. This is not only true for milk tea, but many other categories are now pursuing this health trend.

But in fact, customers’ real demands are often to have more and more!

Drinking a cup of milk tea should be healthy, delicious, and satisfy the need to take photos and share, show off, and have emotional value.

Why has Dongfang Leaf not been popular for several years? It is healthy but not tasty. However, Yuanqi Forest has become popular all of a sudden. It has 0 sugar and 0 fat, is healthy and tasty. Of course, the word "healthy" is in quotation marks.

Therefore, whoever can better meet consumers' multiple demands for milk tea that must be healthy, delicious, and pretentious, consumers will choose you first.

It is not difficult to say that it is delicious, and the sweetness of milk tea itself is addictive. The key is who can make the health concept more perfect and more attractive to customers.

Let’s see how Ba Wang Cha Ji has taken health awareness to the extreme.

First of all, Chagee’s initial product development direction was: tea + milk, refreshing and non-greasy, which is in line with the health characteristics of the product.

At the same time, this healthy awareness is constantly strengthened.

For example, while others are still using creamer-like ingredients, Bawang has taken the lead in using a new base product, "Ice Brown Non-Hydrogenated Base Milk", which achieves 0 creamer, 0 non-dairy creamer, and 0 hydrogenated vegetable oil, allowing customers to drink healthily without burden.

The company also launched a tea product ID card . Starting from the ingredients of tea drinks, the company launched the first "tea product ID card" to disclose the product formula, nutritional information, and product flavor list in detail, directly addressing consumers' health concerns and allowing consumers to understand what they are drinking.

(Picture screenshot from Chagee official mini program)

Later, other brands also followed suit and launched product ID cards, such as Heytea and other brands.

We often say that first-class brands set standards, and second-class brands follow the standards. Bawang Tea Princess is on its way to becoming a first-class brand, taking the lead in proposing and setting standards for the industry.

Later, a calorie calculator was added to let customers know exactly how many calories a cup of milk tea contains.

(Picture screenshot from Chagee official mini program)

You see, it is delicious and healthy. You don’t have to worry about getting fat after drinking it. You can control your calories at any time, and you don’t have to feel guilty about drinking milk tea.

You say, if customers give priority to Ba Wang Cha Ji when selling milk tea for the same price of more than ten yuan?

At this point the product value seems to be almost there, but it’s not enough!

Because customers always want more and more, we also need to understand what other needs of consumers are not met. The more you can meet the needs of customers, the more you will be chosen by customers.

When we build brands, we need to pay attention to and understand the changes in consumer demand at any time. If your brand products cannot meet consumer demand, how can you make money? (Even if you are doing TO B business, you must pay attention to the changes in B-side demand. The nature of the demand is different, but the essence is the same)

What changes have taken place in consumers’ demand for milk tea?

Nowadays, consumers view a cup of milk tea not only as a drink, but also as a kind of social currency, which they use to take photos and share on WeChat Moments to show off, as well as to satisfy certain emotional values.

Therefore, your milk tea is not only delicious and healthy, but also needs to be packaged beautifully and convey a brand concept that can meet some emotional needs. For example, you can have a cup of Bawang Tea to kill time while slacking off at work, or have a cup of milk tea as a companion after get off work to watch TV series.

The packaging design and store design of Bawang Tea Princess combine elements of traditional Chinese culture with elements of international luxury goods, allowing customers to take photos and share the feeling of being tasteful and high-end.

It also collaborates with various brands representing young people and trends and launches various social activities, making consumers feel that Chagee understands young people and creates an emotional resonance.

Selling emotions and social value is one of the secrets to selling a brand at a high price.

The reason to buy is not a single point, but a combination of values.

This combination of measures can basically differentiate itself from most tea brands and give customers a strong enough reason to buy.

This is also the logic behind why we often say that "cost-effectiveness" is not as good as "quality-price ratio".

After reading this, you may also want to think about this question: With so many choices now available to customers, why should they choose your brand? Are the reasons you provide for purchase strong?

If you are running a social restaurant with a dine-in model, you cannot just think about your reasons for purchase based on the product value. You also have to design your differentiated positioning and customer value from multiple dimensions such as your service experience, environmental experience, business district location, store model, etc.

Just like when we do brand positioning for a skewers hotpot brand, we not only say that the product is fresh and delicious, but also how is the service and scene experience. After all, customers come to a hotpot restaurant to eat not only to eat hotpot, but also to socialize.

Different product categories have different customer demands.

OK, after solving the positioning direction and customer value problems, if you want to replicate the store and build a chain brand, the next step is to create a replicable chain single store profit model.

2. Model design: creating a replicable chain store profit model from 0 to 1

Why is it that many people become successful after opening one store, but start to lose money after opening the second or third store?

Why is it okay to open a business in a shopping mall, but you can’t make money in a community?

Why do others need 500,000 to open a store, but you need 1 million?

Why can others make back their investment in half a year, but it takes you three years?

……

This is mainly due to the different profit models of single stores.

No matter how big your restaurant business is, a single store is always the smallest competitive unit. No matter how big a brand is, if your single store cannot compete with the stores next to it within a few kilometers of the nearby business district, it will die.

If a single store is not profitable, the chain will also go bankrupt.

This is why it is difficult for the catering industry to be monopolized by the leading brands . The catering industry is a relatively fair industry with low barriers to entry among many industries. It does not matter your background and resources. As long as you have the ability, you can make money in the catering industry and realize your dreams!

Therefore, if you want to be a chain brand, you need to create a replicable chain single-store profit model.

Polishing a single store's profit model usually takes time and effort. Is there a faster and more efficient way?

Here we have to talk about the strategy of the Overlord.

The single-store model directly borrowed from the then proven successful Chinese-style fresh milk tea brand Cha Yan Yue Se, and quickly completed the chain single-store profit model from 0 to 1. No trial and error, just one step.

But I think Bawang's goal is to become the Starbucks of the East, and the global brand is just a direct reference to Cha Yan's many models that just happened to match at the current stage. If you look at it, you will find that Cha Yan is just a passer-by in Bawang's goal of achieving a global brand. After all, before they grow up, the eagle and the dove look similar.

Now Bawang’s single-store profit model is different from Cha Yan’s.

So what is a good chain single-store profit model?

Everyone has different perspectives and standards. From the perspective of chain stores, I will look at it more from the perspective of customer value and investment efficiency - whether your single-store profit model can better meet customer needs and provide better value to achieve a higher return on investment. To put it bluntly, both C-end consumers and B-end partners benefit more, and that is a good profit model.

The single-store profit model involves a lot of content, including product structure, business district location, pricing, cost structure, customer selection, investment cost, etc. I will have the opportunity to write an article to explain it in detail in the future.

Here I will first talk about the product strategy of Chagee.

The catering industry is generally divided into two product strategy directions: fast fashion and classic.

Have you noticed that McDonald's, KFC, Starbucks, Nanchengxiang and other brands have not changed their core products for decades. This is the product strategy of choosing classic models. New products are only used to stimulate and awaken old customers or for marketing promotion to tell them that there are new products to try, but most of the profits come from classic models.

Recently, I visited Chengdu to inspect a hotpot brand called "Wuliguan", which also has this kind of product strategy.

Among them, the most representative brand is Uniqlo in the clothing industry. They sell the same type of clothes for several years, but their profit margin is higher than other fast fashion brands that sell trendy items.

The advantage of the " fast fashion" product strategy is that there is a constant influx of new traffic, but the disadvantage is that there is no supply chain accumulation and it requires continuous new product development capabilities. Once it fails to keep up with customers' demand for new products, it will be eliminated.

For example, the hot pot brand "Zhu Guangyu" which has a relatively high potential online recently adopts this product strategy, which requires launching various new products to attract customers to check in and try them out. Once customers have nothing new to attract them, they will no longer choose you.

According to statistics, the profit margin generated by the classic strategy is almost twice that of innovative products.

The "big single product" strategy chosen by Bawang Tea Princess from the beginning is this classic strategy - focus on quality rather than quantity, and make classic products based on common tea and milk bases.

Most of Bawang's store menus mainly have three series: original leaf fresh milk tea, snow top series, original leaf fresh brewed tea,

There are about a dozen flavors in total (Yunnan stores have some fruit teas and other products).

(Screenshot from Chagee Guangzhou store mini program)

However, the top three products account for about 70% of the total sales. The sales of the signature product Boya Juexian account for about 20-30% of the total sales.

After all, the more concentrated the products, the more streamlined the supply chain , which improves the efficiency of front-end stores and back-end supply chains.

So does it mean that the fewer products you make, the better?

uncertain.

The product structure design should match your single-store profit model and strategic positioning.

The key is that the store can make money and improve overall efficiency.

I have to say a little more here: in the end, when it comes to branding, we all hope to achieve economies of scale - the more stores we have, the lower the total cost.

But we are not afraid of not reducing costs, but we are most afraid of increasing costs . This is also what Zhang Junjie said about anti-scale gravity - the bigger the scale, the higher the cost.

The three major costs of catering are: ingredients, labor and rent.

As the scale increases, the cost of ingredients will indeed decrease. As the brand power increases, the rent may be a little lower, but not much.

The key point is that labor costs will not necessarily decrease as the number of stores increases, but may increase instead.

Because the labor cost of a single store cannot be reduced, if you need 10 people for a store, you will still need 10 people for the 1,000th store. However, the personnel management cost of each store will increase, and the human factor is the most uncertain, which will also affect the stability of the overall customer experience.

So what can you do? Either improve labor efficiency through organizational management, or minimize reliance on manpower in your single-store profit model design to reduce uncertainty and management costs.

Therefore, Chagee simplifies its product structure by making classic models, reduces the difficulty of store operations by standardizing the upstream supply chain, and uses intelligent milk tea making machinery and equipment, etc. These are all aimed at reducing dependence on people and reducing store management costs.

You see, the chain brand that has done the most extreme in this regard is Juewei Duck Neck. The store employees do almost the same job as cashiers. The store does not require any processing. Products are delivered to the store and put on display for sale, which greatly reduces the cost of human management.

The opposite example is Chinese food. Restaurant products rely heavily on chefs and manpower, which is why few Chinese cooking brands can quickly open thousands of stores. Many Chinese food brands are working on product standardization and robot cooking to solve this standardization and labor cost problem.

What we need to note here is that in the single-store profit model, if you do less in one aspect, you will have to do more in other aspects.

So where should we do more? We should do more and invest more in areas that can increase customer value.

There is another important strategy in Chagee's single-store profit model: opening large stores

Most mid-range and lower-end milk tea shops are less than 50 square meters, but Chagee stores were between 50 and 100 square meters at the beginning. Although this will increase costs to a certain extent, the tea brand in the price range of 15-20 yuan has a better experience , and the price is lower than Starbucks and it also enjoys a certain "third space" scene experience.

By opening large stores in this price range, Bawang Tea Princess has been able to quickly differentiate itself from other brands in unfamiliar markets, which is also very helpful in enhancing its brand power.

(Pictured is the overseas concept store of Chagee)

Just like we ask some of our fast food chain brand clients to provide fruit buffets in their stores. Because fruit brings high value to customers, the cost has to be increased, and then the corresponding cost is offset by reducing and cutting out areas that do not generate much value.

Moreover, the single-store profit model is not static and needs to be adjusted according to the brand development needs and market changes.

At the current stage, an extreme single-store profit model like Bawang can better meet the needs of rapid brand development.

However, this model will encounter problems in the future. For example, as Bawang has more and more stores, there will be a diversion between stores. In addition, the single product will also lead to a single demand being met, resulting in a decrease in the repurchase rate.

As competition becomes increasingly fierce in the future, the Overlord team needs to continuously iterate and optimize to balance efficiency and market demand.

I believe that friends who can see this really want to build their own brand.

When you have no problems with brand positioning and single-store profit model, then everything is ready except the east wind, which is brand leverage.

3. Brand Leverage: From 1 to 10, use brand leverage to increase brand potential

The higher the brand potential, not only will customers and franchisees be more willing to choose you, but when you go to select a location and get a shop space, those property investment people will see you as if you were the God of Wealth and may even waive rent for you.

If you don’t have brand power, you won’t be able to command a premium and you won’t get a good store location.

What brand leverage does Bawang use? Some people say it is because it has money and capital. That’s right, capital is a kind of leverage .

Today's catering brands are increasingly combining capital to accelerate brand development.

Whether or not to join capital depends on the individual. After all, capital has its pros and cons, and everyone’s goals are different.

The addition of capital also shows that the competition in the catering industry is no longer limited to single-store competition and brand competition. Catering has become an industry with comprehensive competition . The capitalization of brands such as Chagee, Luckin Coffee and Kudi has shown that the catering industry has fully entered an era of professional, branded and data-based competition. In the future, more and more catering companies will be listed.

The era when you could just rent a shop, hire a chef and open a restaurant to make money is over.

In addition, there are cultural elements, such as celebrity endorsements, various brand collaborations and other levers to quickly increase brand awareness and enhance brand power.

You see, the brand name Chagee has been leveraging from the very beginning - leveraging the potential energy of the name of the classic Chinese story "Farewell My Concubine". Then, in the design of the brand symbol, we refer to cultural elements such as Chinese opera characters, the charm of Buddha statues and Western geometric lines, leveraging the leverage of the cultural matrix.

There are also collaborations with Grave Robbers' Chronicles and Super Monkey Fitness Platform, and the organization of Frisbee activities, all of which are constantly strengthening the healthy and youthful brand tone of Chagee.

This series of operations has made Chagee's brand potential increasingly stronger, and its premium rights in franchisee screening and business district site selection have also become increasingly higher.

What many people are talking about now as the cultural matrix is ​​actually leveraging the collective subconscious and elements that are already known to everyone to leverage the brand.

But it should be noted that it depends on who your target customers are. Some so-called cultural matrices are not sensitive to them. Just like if you want to do business with post-00s, and your decoration style is like Hong Kong style or 90s style, these post-00s will at most try it out and check in, but will not repurchase, so it is difficult to resonate with them.

Well, in the end, it is a rhythmic and rapid fission expansion. From 10 to 10,000 global layout

4. Fission expansion: from 10 to 10,000 nationwide and global layout

I looked at the brand store opening path of Chagee. Its store expansion rhythm is mainly:

First, he created the world in Yunnan.

Then Chengdu stood tall and proud.

Then the whole country was flooded with

Finally, the world launched a comprehensive effort.

The most critical moment was that the company acquired a shop with an annual rent of 7 million yuan in Chunxi Road, the most prosperous business district in Chengdu . It became famous overnight and had the brand potential to go nationwide.

Later, the entire headquarters was moved to Chengdu. After all, Chengdu is a city with high catering potential in the country. Occupying Chengdu means occupying the national potential, which further increases the brand's premium and attracts many franchisees and cooperation resources.

But it costs money to build brand potential. So Bawang Tea Princess’ strategy is to build the brand in core cities and make profits by expanding the scale in other cities.

The same logic applies to opening stores in a city. Flagship stores are opened in core business districts to build brand potential, while other business districts quickly expand scale and reap profits through franchising and joint ventures.

In 2024, Bawang's focus may be on opening stores overseas quickly. The Bawang team may be selecting locations in Europe and the United States.

(A side note: The globalization of Chagee has also given many domestic catering brands great confidence to go overseas. So I also wish that Chagee's global development will get better and better, and I hope to see Chagee's stores and products in different countries)

The Overlord strategy is a high-profile strategy, which involves first penetrating the region, then increasing the potential, and finally rolling out across the board . If you have money, resources, and strength, you can adopt this strategy.

But if the resources are not enough, you can try another strategy.

For example, the region is king, and resources are concentrated on a certain region, and it is steady and cautious . For example, Chengdu's Kaojiang Grilled Fish and Fujian's Xiaojiaotian brand in Fuzhou use this approach.

Or they can use the strategy of surrounding the cities from the countryside, starting with the surrounding cities or counties where the competition is not fierce, and then slowly penetrate into the core market . Mixue Bingcheng and Wallace used this strategy in their early days.

Let me summarize at last.

Looking at the six-year brand development history of Chagee, we can find that building a chain brand is not as simple as giving it a name, thinking of a slogan, and creating a super symbol.

Instead, it includes various aspects such as strategic positioning, creation of chain store profit model, brand building and store opening rhythm.

Either optimize and upgrade on the existing basis, or learn to think about your development path from the end to the beginning, and then optimize as you go.

No matter what you do, some things remain constant.

First, you need to understand consumer needs and develop or optimize your brand strategic positioning

Consider the competition in the market, whether it is a stock market or an incremental market, what is the market trend, what are my own advantages, and how can I differentiate myself from my competitors?

How can my core advantages be prevented from being imitated by competitors, such as your brand power, supply chain and organizational management.

Then create a replicable chain single-store profit model, and then use various levers to enhance your own brand value, increase brand premium, and avoid getting involved in price wars.

The brand is the result of this series of complex operations. There is still a lot of room for development in China's restaurant chain, which is also an opportunity for many restaurant people.

Of course, the process of brand development is ever-changing and requires specific analysis based on specific circumstances.

But what never changes is that we keep advancing with the times——

Only by keeping up with the changes can you avoid being eliminated.

Only by adapting to changes better and improving yourself can you be welcomed by the market and gain more rewards.