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DeepSeek-V4.1-Flash addresses the HBM and bandwidth bottlenecks caused by million-token contexts through a novel cross-layer attention reuse strategy that significantly shrinks KV cache footprints. By deriving decoder states from encoder hidden states and dynamically reusing cached indices across layers, the model reduces prefill compute and memory strain without sacrificing multimodal capabilities. This architectural shift offers a practical pathway for deploying long-horizon agents on existing hardware constraints.

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DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse

MarkTechPostSeptember 10, 20264 min read

Long-horizon agents have turned LLM serving into an input-heavy workload. Repeated prefills and million-token contexts leave KV caches that strain HBM, SSD capacity, and bandwidth. DeepSeek AI built its newest release around that exact bottleneck. DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters, 196B additional Engram parameters, and a 1M-token context window. It […] The post DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse appeared first on MarkTechPost.

Long-horizon agents have turned LLM serving into an input-heavy workload. Repeated prefills and million-token contexts leave KV caches that strain HBM, SSD capacity, and bandwidth. DeepSeek AI built its newest release around that exact bottleneck. DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters, 196B additional Engram parameters, and a 1M-token context window. It activates 8B parameters per token during prefill and 16B during decode. The main number is a global KV cache footprint of 890 bytes per token,…

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