Technical Advantages and Performance Analysis of Kunlunxin P800 Chip

Based on public information, the technical advantages of Kunlunxin P800 chip are mainly reflected in the following aspects:

Kunlunxin P800 adopts an independently developed AI chip architecture, achieving significant breakthroughs in architectural design. Its

concentrates 64 AI accelerator cards in the same cabinet, replacing part of the inter-machine communication with high-speed backplane or direct connection technology, increasing the inter-card interconnect bandwidth by [1]. This architectural innovation brings two key performance improvements:

• 10x improvement in single-machine training performance
• 13x improvement in single-card inference performance
  [2]

For the current mainstream MoE (Mixture of Experts) large model architecture, P800 shows unique advantages:

Advantage Item	Specific Performance
Memory Specification	20%-50% better than similar mainstream GPUs, more friendly to MoE architecture [1]
Training Efficiency	Only 32 units are needed to support full-parameter training of 671B models [1]
Inference Deployment	First to support 8-bit inference ; a single machine with 8 cards can run 671B models [1]
Feature Support	Fully supports key features such as MLA and multi-expert parallelism [1]

P800 supports

, featuring high throughput and low latency. It also supports high-bandwidth memory (HBM) and DDR4 memory, providing strong data processing capabilities [3].

• Ecosystem Compatibility
  : Compatible with PyTorch ecosystem, supporting large model training scenarios
• Fast Deployment
  : Based on a complete software stack ecosystem, DeepSeek-V3/R1 inference deployment can be completed in
  two steps
  [4]
• One-Click Deployment
  : Provides out-of-the-box images and complete dependency environments to achieve plug-and-play functionality [4]

• Reduced Network Costs
  : Reduces reliance on expensive inter-machine network devices (e.g., InfiniBand switches)
• Energy Consumption Optimization
  : A single cabinet can replace multiple traditional servers, significantly reducing machine room space and overall energy consumption
• Improved Hardware Utilization
  : Through efficient inter-card collaboration, reduces waiting time and increases the effective utilization rate of AI accelerator cards [2]

Kunlunxin has completed

, including DeepSeek MoE models and their distilled small-scale dense models such as Llama and Qwen. It has achieved stable operation of various large model tasks in actual business scenarios [4].

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[1] Supplycase - “DeepSeek: Helping Chinese Chips Break Through” (https://cn.supplyframe.com/article/8309.html)
[2] EET China - “Core of Baidu Smart Cloud: Kunlunxin P800 30,000-Card Cluster” (https://www.eet-china.com/mp/a400929.html)
[3] Kunlunxin Official Website - “Domestic AI Card DeepSeek Full-Version Adaptation for Training and Inference, Excellent Performance” (https://www.kunlunxin.com/news/4477.html)
[4] Kunlunxin Official Website News (https://www.kunlunxin.com/news/4477.html)