深度解读 DeepSeek V4.1 Flash 全新架构，如何成为显存杀手
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Merged summary
TL;DR - DeepSeek V4.1-Flash introduces CED and CSA2 architectures to cut long-context inference compute and memory costs while retaining strong reasoning and agent performance. The reported reductions could make million-token agent workloads substantially more practical to deploy.
- CED splits the 40-layer network into causal encoder and decoder halves, using compressed encoder states to generate global KV caches and reportedly halving prefill compute.
- CSA2 reuses KV caches and Top-K indexes across layers; combined with FP4 global-cache quantization and FP8 short-window caches, it reduces global KV size to one-quarter of V4-Flash.
- Disposable short-term caches and compressed long-term memory reportedly shrink persistent KV storage to one-eighth of the previous generation, while runtime VRAM use falls by about 75%.
- Despite activating only 8B parameters during prefill and 16B during decoding, the 552B-parameter model reportedly matches or exceeds the prior 1.6T-parameter V4-Pro on several internal and agent evaluations.
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深度解读 DeepSeek V4.1 Flash 全新架构，如何成为显存杀手
Public signals
N/A
TL;DR - DeepSeek V4.1-Flash introduces CED and CSA2 architectures to cut long-context inference compute and memory costs while retaining strong reasoning and agent performance. The reported reductions could make million-token agent workloads substantially more practical to deploy.
- CED splits the 40-layer network into causal encoder and decoder halves, using compressed encoder states to generate global KV caches and reportedly halving prefill compute.
- CSA2 reuses KV caches and Top-K indexes across layers; combined with FP4 global-cache quantization and FP8 short-window caches, it reduces global KV size to one-quarter of V4-Flash.
- Disposable short-term caches and compressed long-term memory reportedly shrink persistent KV storage to one-eighth of the previous generation, while runtime VRAM use falls by about 75%.
- Despite activating only 8B parameters during prefill and 16B during decoding, the 552B-parameter model reportedly matches or exceeds the prior 1.6T-parameter V4-Pro on several internal and agent evaluations.