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

תקציר מקורי באנגליתLong-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, about 1/4 of DeepSeek-V4-Flash and roughly 437x smaller than DeepSeek-V1. Is it deployable? Yes. Open weights ship under an MIT license with vLLM, SGLang, and Transformers paths on Hugging Face, and the research team describes a public API with low