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DeepSeek-V4.1-Flash-1017 (Hugging Face Repo)
DeepSeek-V4.1-Flash-1017 introduces Compressed Sparse Attention 2 and FP4 KV caching to achieve a compact 890-byte-per-token footprint.
What: This model variant improves performance in reasoning and agent tasks while reducing its KV cache footprint to roughly 1/8th the size of the previous V4-Flash model.
Why it matters: Optimizing the KV cache is critical for making large models practical for real-time agentic workflows that require long context and low latency.
Decoder
- KV Cache (Key-Value Cache): A mechanism in transformer models that stores previously computed key and value states to avoid recomputing them at every step of text generation.
- FP4 (4-bit Floating Point): A low-precision data format that drastically reduces memory usage, allowing larger models to fit into less VRAM.
Original article
DeepSeek-V4.1-Flash-1017 delivers substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. It also reduces task execution time and output length, advancing the performance-cost Pareto frontier across multiple agent harnesses. DeepSeek-V4.1-Flash combines cross-layer KV cache reuse in Compressed Sparse Attention 2 with FP4 KV caching, reducing its global KV cache footprint to 890 bytes per token. Its persistent KV cache footprint is roughly 1/8 of that of DeepSeek-V4-Flash. The model delivers substantially better performance than the baseline despite its much smaller KV cache footprint.
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