Model ID
deepseek-ai/DeepSeek-V4-Pro-0813
Capabilities
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 is the official release of DeepSeek-V4-Pro, superseding the Preview version with enhanced agentic capabilities and improved performance, particularly in production environments. It features 1.6T total parameters with approximately 49B activated, using 384 routed experts with 6 selected per token across a 61-layer architecture with 7,168 hidden size and 128 attention heads. The model incorporates DeepSeek’s compressed long-context architecture with sparse attention, multi-token prediction, and YaRN RoPE scaling to support up to a 1M-token context window. It also includes the DSpark speculative decoding module, with dedicated target layers and a 5-token block size, for faster generation.
About this model
DeepSeek V4 Pro 0813 is the official release of DeepSeek-V4-Pro, superseding the Preview version with enhanced agentic capabilities and improved performance, particularly in production environments. It features 1.6T total parameters with approximately 49B activated, using 384 routed experts with 6 selected per token across a 61-layer architecture with 7,168 hidden size and 128 attention heads. The model incorporates DeepSeek’s compressed long-context architecture with sparse attention, multi-token prediction, and YaRN RoPE scaling to support up to a 1M-token context window. It also includes the DSpark speculative decoding module, with dedicated target layers and a 5-token block size, for faster generation.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (deepseek-ai/DeepSeek-V4-Pro-0813). Released under MIT. Further reading: Paper · Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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