DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
DeepSeek releases V4.1-Flash, a 552B-parameter multimodal MoE with 1M-token context and 890-byte-per-token KV cache for cheaper agentic workloads; weights on Hugging Face.
DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters, activating 16B parameters per token during decode and 8B during prefill, supporting contexts up to one million tokens. It combines cross-layer KV cache reuse in Compressed Sparse Attention 2 (CSA2) with FP4 KV caching, reducing global KV cache to 890 bytes per token (about one-quarter of DeepSeek-V4-Flash), while SWA Bounded Replay cuts the persistent SSD/host-memory cache to roughly one-eighth. Pretrained on 45T multimodal tokens, it outperforms the baseline across text and multimodal agentic scenarios, with checkpoints released on Hugging Face.
- 552B-parameter multimodal MoE activating 16B parameters per decode token and 8B during prefill.
- CSA2 cross-layer KV reuse plus FP4 caching cuts KV footprint to 890 bytes per token.
- SWA Bounded Replay reduces persistent SSD/host-memory KV cache to roughly one-eighth of DeepSeek-V4-Flash.
- Pretrained on 45T multimodal tokens; checkpoints released on Hugging Face under deepseek-ai.
- Supports contexts up to one million tokens, targeting input-heavy agentic workloads.
- Products
- Hugging Face
- Organizations
- DeepSeek
- AI models
- DeepSeek-V4.1-FlashDeepSeek-V4-Flash
From the original article
1 min read at huggingface.co
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth.
By DeepSeek-AI, Anyi Xu, B. Li · huggingface.co
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