Model ID
deepseek-ai/DeepSeek-V4.1-Flash-1017
Capabilities
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 is an updated multimodal Mixture-of-Experts model for agentic coding, reasoning, and visual tasks, with shorter outputs and faster task execution. It has 763B total parameters with 8B active during prefill and 16B during decode, using a 40-layer Causal Encoder-Decoder with 5,120 hidden size, 64 attention heads, and 1 KV head. The architecture pairs Compressed Sparse Attention 2 with 384 routed experts and a shared expert, activating 6 per token. Supporting a 1M-token context, it adds a 32-layer vision encoder and DSpark speculative decoding.
About this model
DeepSeek V4.1 Flash 1017 is an updated multimodal Mixture-of-Experts model for agentic coding, reasoning, and visual tasks, with shorter outputs and faster task execution. It has 763B total parameters with 8B active during prefill and 16B during decode, using a 40-layer Causal Encoder-Decoder with 5,120 hidden size, 64 attention heads, and 1 KV head. The architecture pairs Compressed Sparse Attention 2 with 384 routed experts and a shared expert, activating 6 per token. Supporting a 1M-token context, it adds a 32-layer vision encoder and DSpark speculative decoding.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (deepseek-ai/DeepSeek-V4.1-Flash-1017). Released under MIT. Further reading: Blog · Paper
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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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