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Baidu Kunlunxin R200 (XPU-R)

Product Overview​

Kunlunxin R200 is the second-generation AI accelerator card launched by Kunlunxin (Beijing) Technology Co., Ltd., a Baidu affiliate, released in 2025. Based on the self-developed XPU-R architecture and built on a 7nm process, it delivers multi-precision compute ranging from 256 TOPS (INT8) to 32 TFLOPS (FP32), equipped with 16GB/32GB GDDR6 memory (512GB/s bandwidth) and a typical power draw of 150W.

Product positioning: focused on AI inference tasks while also offering a degree of training capability, suited to data center inference, video analytics, scientific research, and other scenarios.

Core Specifications​

ParameterValue
ArchitectureXPU-R (second-generation Kunlunxin architecture)
Process7nm
FP3232 TFLOPS
FP16128 TFLOPS
INT8256 TOPS
Memory16GB / 32GB GDDR6 (optional)
Memory Bandwidth512 GB/s
TDP150W (typical power)
InterfacePCIe Gen4 x16 (backward compatible with Gen3/2/1)
Video Decoding108 channels of 1080P@30fps
Video Encoding27 channels of 1080P@30fps
ECCECC memory protection supported
CoolingPassive cooling design
Operating Temperature0-55°C
Board FormFull-height, full-length, dual-slot
Release2025
Mass Production2025 Q2

Data notes:

  • ✅ All compute, memory, and power figures are official or verified by reliable third parties
  • Full specifications are subject to Kunlunxin's official datasheet

Product Highlights​

1. Multi-Precision Compute Configuration​

  • INT8: 256 TOPS — focused on high-performance inference scenarios
  • FP16: 128 TFLOPS — also covers training needs
  • FP32: 32 TFLOPS — supports high-precision computing
  • Flexible precision: supports multiple precisions from INT8 to FP32, adapting to the differing precision requirements of different algorithms

2. High-Bandwidth GDDR6 Memory​

  • 16GB/32GB options: flexibly chosen based on model size and batch processing needs
  • 512GB/s bandwidth: the high-bandwidth memory configuration helps reduce data access bottlenecks
  • ECC protection: supports ECC memory protection, improving system reliability

3. Dedicated Video Processing Capability​

  • Decoding: 108 channels of 1080P@30fps video streams
  • Encoding: 27 channels of 1080P@30fps video streams
  • Application scenarios: video analytics, real-time processing, and other edge computing and cloud media processing scenarios

4. Passive Cooling Design​

  • 150W typical power: a mid-range level among comparable products
  • Passive cooling: simplifies thermal system design for data center deployment
  • Deployment considerations: chassis airflow planning must be taken into account

Software Stack​

Kunlunxin provides a complete software development kit supporting mainstream deep learning frameworks.

ComponentFunction
Deep learning frameworksPyTorch, TensorFlow, PaddlePaddle adaptation
Inference engineHigh-performance inference optimization
Development toolsCompiler, math libraries, management tools
Migration toolsMigration of existing software stacks to the Kunlunxin platform

Use Cases​

1. Data Center AI Inference​

  • Large-scale inference deployment: 150W low power suits large-scale deployments
  • Multi-precision support: adapts to the precision needs of different inference algorithms
  • High-bandwidth memory: reduces inference latency

2. Video Analytics and Processing​

  • 108-channel decoding: suits large-scale video analytics scenarios
  • 27-channel encoding: video transcoding, live streaming, and other applications
  • Edge computing: the passive cooling design suits edge facility deployment

3. Scientific Research​

  • FP32 support: supports high-precision scientific computing
  • PCIe Gen4: ample CPU-GPU data transfer bandwidth
  • ECC protection: improves computing reliability

Technology Selection Considerations​

For users considering the Kunlunxin R200, the following technical dimensions deserve attention:

  1. Compute-precision match: evaluate compute effectiveness against the precision requirements of the target workload
  2. Memory capacity needs: evaluate the 16GB/32GB configuration choice based on model size and batch processing needs
  3. Video processing needs: if large-scale video encoding/decoding is required, pay particular attention to its dedicated processing capability
  4. Deployment compatibility: the passive cooling design has specific server airflow requirements; confirm infrastructure compatibility
  5. Ecosystem adaptation cost: evaluate the cost of migrating existing software stacks to the Kunlunxin platform

Roadmap​

ProductArchitectureProcessFP32INT8MemoryRelease
Kunlunxin K100XPU v114nm???2018
Kunlunxin K200XPU v27nm???2020
Kunlunxin R200XPU-R7nm32 TFLOPS256 TOPS16/32GB GDDR62025
Kunlunxin R300 (planned)XPU-R+5nm/4nm??HBM?2026?

References​

  • Zhihu: "Kunlunxin R200 AI Accelerator Card Technical Specifications Analysis" (2025-12-14)
  • CSDN: "Kunlunxin R200 AI Accelerator Card Technical Specifications Analysis" (2025-12-14)
  • Official website of Kunlunxin (Beijing) Technology Co., Ltd.

Last updated: July 3, 2026