DeepSeek-V4.1 Flash achieves nearly 420 Tokens/s inference speed through extreme KVCache compression, prompting speculation it should be labeled DeepSeek-V5 Flash. The technical report reveals architectural optimizations targeting Long-horizon Agent Workflows where growing context lengths create unsustainable storage and compute pressure on HBM and SSD.

Key innovations include a 40-layer model where only 20 layers activate during Prefill (8B parameters), reducing to 16B during Decode. KVCache compression employs multiple dimensions: head count compression similar to GQA, block-based CSA compression, and novel cross-layer CSA2 compression. Sparse Attention indexer computation is optimized alongside FP4 KVCache precision reduction, achieving 4x further compression while maintaining task quality.

The architecture functions as a Recursive Transformer, modifying queries and reusing keys/values during recursion. DeepSeek-V4's hybrid Sparse Attention plus Sliding Window Attention reduced compute costs, but persistent Global KV Cache storage became the new bottleneck — dominating host memory, SSD capacity, and interconnect bandwidth. This limited agent deployment for longer tasks. The report positions KVCache management as a fundamental deployment capability.