Comparison of Technology Routes and Business Models of China's Top Four GPU Players Under CUDA Ecosystem Constraints

Based strictly on the context you provided, I will focus on the ‘Top Four Domestic GPU Players’ and compare their technology routes and business models under the constraints of NVIDIA’s CUDA ecosystem, providing judgments and verifiable next-step research paths.

1. Technology Routes and Ecosystem Compatibility (Based on Provided Information)

• Moore Threads: Full-function route (similar to NVIDIA’s full-stack route, covering graphics + AI). Advantages: Wide scene coverage, applicable to general markets; Disadvantages: Heavy ecosystem burden, high migration costs, reliance on CUDA compatibility layers or multi-stack parallel maintenance, greatest difficulty in building long-term moats.
• Muxi: Vertical domains such as government/finance (replicating AMD’s toB model), focusing on inference and general computing, taking a relatively specialized scene path. Advantages: In-depth optimization for specific industry workloads, concentrated software stack, compatibility layers can be simplified for industry scenes, controllable migration costs.
• Biren: Targeting high-end training and supercomputing centers, emphasizing computing power scale and single-point peak performance. Highest dependence on ecosystems, greatest pressure on migration and optimization of CUDA/mature frameworks, and increased procurement concentration is vulnerable to policy and supply chain fluctuations.
• Suyuan: Deeply integrated with Tencent Cloud services, taking the ‘cloud-native computing power’ path. Advantages: High coupling between hardware and deployment environment, enabling end-to-end optimization; Disadvantages: High customer concentration, relatively closed ecosystem path.

2. Commercial Feasibility and Ecosystem Breakthrough Probability (Based on the Above Route Comparison)

• Path Comparison:
  • Moore Threads: Targeting mass/general markets, clear short-term monetization path, but greatest difficulty in achieving ‘replacement/surpassing’ under the CUDA system, requiring huge long-term ecosystem investment.
  • Muxi: Focusing on strong compliance and scenario-based needs in government/finance industries, through in-depth industry customization and end-to-end optimization, it is expected to achieve a business closed loop in a ‘limited ecosystem’, which is the closest path to ‘differentiated breakthrough under the CUDA moat’.
  • Biren: High-end training and supercomputing tracks rely heavily on mature ecosystems, with high migration costs and risks, and commercialization rhythm is easily interrupted by ‘chokepoint’ issues due to macro policies and supply chain constraints.
  • Suyuan: Deep integration with Tencent Cloud is conducive to early order implementation, but high customer concentration and limited ecosystem boundaries require more cloud vendors and diversified customer structures for subsequent expansion.
• Breakthrough Probability (Based on Path Characteristics):
  • First Tier (Most Practical Under Strong CUDA Constraints): Muxi (Industry Vertical + toB Customization, low ecosystem burden, high customer stickiness).
  • Second Tier (Potential but Difficult): Suyuan (Cloud-coupled Path, needs to expand customers and ecosystems); Moore Threads (Full-function but heavy ecosystem investment).
  • Third Tier (Highest Breakthrough Difficulty Under CUDA Ecosystem): Biren (High-end training and supercomputing have extremely high ecosystem dependence, greatest uncertainty).

3. Verification and Next-step Evidence (Operable Research List)

• Key Points to Verify:
  • Muxi: Large-scale deployment cases in vertical domains such as government/finance, compatibility and stability of software stacks in mainstream frameworks (e.g., PyTorch/TensorFlow), migration cost assessment and customer renewal/expansion status.
  • Suyuan: Tencent Cloud’s computing power procurement and usage efficiency data, cross-cloud/multi-tenant adaptation status, order progress of other cloud vendors/operators besides Tencent.
  • Moore Threads: Performance loss and stability of CUDA compatibility layers, proportion structure of game/graphics and AI inference/training, gross margin and cash flow health.
  • Biren: Actual throughput and energy efficiency comparison of BR100/BR104 in supercomputing centers, compatible versions and support breadth of software stacks with mainstream training frameworks, supply chain and advanced process alternative solutions.
• Recommended Next-step Research Paths:
  • In deep research mode, the following data can be pulled for verification: Recent bidding/winning information of each vendor, industry POC and online delivery records, core customer repurchase rate, software stack version and compatibility reports, energy efficiency/computing power utilization comparison calculations.

Conclusion (Based on Provided Information): Under the realistic constraints of NVIDIA’s CUDA ecosystem moat, Muxi’s ‘government/finance vertical + toB customization’ path achieves the best balance among ecosystem costs, customer stickiness, and commercialization implementation, and is most likely to achieve a replicable business closed loop within a limited ecosystem. If you agree, I can quantify each of the above verification points in ‘deep research mode’ and output a comparative report including benchmarking and valuation frameworks.