DeepSeek Releases V4-Flash-0731: 4x Radeon AI PRO R9700 Inference with 79 tokens/s Performance
By Mr.Xu Community Post
Published:
Summary:DeepSeek has released the latest version of its model, DeepSeek-V4-Flash-0731, which supports inference on four Radeon AI PRO R9700 GPUs. By optimizing the multi-GPU collaboration mechanism, the model achieves an inference speed of 79 tokens/s in a four-card configuration, representing a 119% improvement over the two-card setup. This advancement highlights DeepSeek's breakthrough in multi-GPU inference optimization, providing AI developers with a more efficient deployment solution for large-scal
Technical Mechanism Analysis
The DeepSeek-V4-Flash-0731 model achieves multi-GPU inference performance improvements through the following technical mechanisms:
- Multi-GPU Collaboration Optimization: The model leverages the P2P (Peer-to-Peer) communication capability of Radeon AI PRO R9700 GPUs to enable efficient data transmission between GPUs, reducing data transmission latency.
- Expert Parallelization: The model distributes 11,008 experts across four GPUs, ensuring that all experts reside in the GPU memory, avoiding frequent data exchanges between host memory and GPU memory.
- Static Placement Strategy: By adopting a static placement strategy, the model avoids the performance bottleneck caused by the default heat placement strategy in a multi-GPU configuration due to insufficient resources.
Engineering Trade-offs and Actual Performance
- Performance Improvement: In a four-GPU configuration, the model achieves a greedy decoding speed of 79 tokens/s, representing a 119% improvement over the 36 tokens/s achieved in a two-GPU configuration.
- Memory Utilization: All experts reside in the GPU memory, eliminating the need for a host memory pool, further enhancing inference efficiency.
- Stability: Thanks to stability fixes, the model runs stably in a multi-GPU configuration without any degenerate output.
Developer Implementation and Deployment Recommendations
- Hardware Configuration Recommendations: It is recommended to use GPUs that support P2P communication and ensure sufficient bandwidth between each GPU to support efficient data transmission.
- Software Configuration Recommendations: Use the stability-fixed branch of the affinity engine and adopt a static placement strategy to avoid performance bottlenecks.
- Quantization Scheme: Use the pre-built 2.875 bpw quantized model to reduce download and quantization time and improve deployment efficiency.
Conclusion
The release of the DeepSeek-V4-Flash-0731 model marks a significant advancement in the field of multi-GPU inference optimization by DeepSeek. The model's efficient multi-GPU inference capability provides AI developers with a more powerful tool, especially in scenarios that require large-scale model deployment.
— END —Source: Reddit r/LocalLLaMA (2026-10-15)
Tags: #DeepSeek #Multi-GPU Inference #GPU Optimization #Large Language Model
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