DeepSeek V4.1 Flash Is an Architecture Reset, Not Just a Cheaper Model Despite having more total and active parameters than its predecessor, V4.1 Flash cuts working KV cache to one quarter and persistent cache storage to one eighth. Zhihu contributor Exhalation explains how it does this by deleting old modules, sharing KV states, and recomputing local context. 1️⃣ DeepSeek removed its own previous ideas 🔹 MTP: external draft models such as DSpark weakened its speculative-decoding value, while the auxiliary loss no longer justified its memory cost. 🔹 Heavily Compressed Attention: its global-summary role was ambiguous and difficult to combine with FP4 storage. 🔹 Dense warmup: V4.1 trains sparse attention from scratch rather than starting with one trillion dense-attention tokens. 2️⃣ Store less, reuse more Non-SWA KV cache moves from FP8 to FP4, while the more sensitive SWA portion remains FP8. DeepSeek no longer persists SWA cache. When a conversation forks from an earlier point, the system rebuilds only a small local window. Post-training simulated this process to limit numerical drift. A modified YOCO design provides the other major saving. Upper layers reuse the same lower-layer source representation, adding only a layer-specific projection. The result is roughly half the KV storage and close to 50% less historical prefill computation in the idealized case. 3️⃣ Sparse attention reuses its search Sparse attention lowers attention cost from O(n²) to O(kn), but finding the top-k tokens can still retain an O(n²) component. V4.1 Flash either reuses an earlier layer’s top-k result or selects a smaller candidate-block pool before re-indexing. This prevents token selection from becoming the bottleneck at long context lengths. 4️⃣ Engram and mHC were streamlined Engram replaces an expensive second-order optimizer state with a Sinkhorn-style update, removes causal convolution, and extends matching from 3-grams to 4-grams. mHC reorders residual mixing across layers, reducing estimated I/O from (4n+4)d to (3n+2)d. ✅ The larger pattern DeepSeek is not merely compressing an existing model. It is willing to discard its own previous components when a cheaper system-level design emerges. V4.1 Flash is less a smaller V4 than a new answer to one question: how much intelligence can be delivered per byte of memory and unit of inference cost? 🔗 Full analysis: #DeepSeek# #DeepSeekV41# #LLMArchitecture# #AIInfra# #KVCache# #SparseAttention#