GoKawiilDeepSeek unveils V4.1 Flash with 4x KV cache compression and 420 tokens/sec speed
DeepSeek released V4.1 Flash, a model initially mistaken for a minor update but revealed via its technical report to be a significant architectural overhaul, effectively a V5-class release. It achieves near 420 tokens/second throughput while compressing KV cache by 4x through techniques including cross-layer compression, sparse attention indexing optimizations, and FP4 precision, alongside a YOCO-inspired prefill design that only activates 8B parameters during prefill versus 16B during decode across its 40 layers.
GoKawiil's interpretation of the reporting above, not reported fact.
As AI agents run longer, multi-step workflows involving repeated tool calls, the memory and compute cost of storing conversation context (KV cache) becomes a major bottleneck limiting how far these systems can scale. By aggressively compressing this cache without sacrificing task quality, DeepSeek is tackling one of the core infrastructure constraints on long-horizon agentic AI, potentially enabling cheaper, faster deployment of models that maintain extended context.
- DeepSeek-V4.1 Flash compresses KV cache 4x using cross-layer compression, sparse attention, and FP4 precision.
- The model reportedly reaches near 420 tokens/second, prompting DeepSeek to retire older V4 Pro models.
- A YOCO-inspired design cuts prefill computation by activating only 8B of 16B decode parameters during prefill.
Source: zartbot.github.io, 2026-09-17
Published there as: “DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compression”
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