DeepSeek V4.1-Flash cuts KV cache overhead for million-token contexts
DeepSeek's V4.1-Flash model tackles a critical infrastructure bottleneck in long-context AI deployment: KV cache overhead. The 552B-parameter mixture-of-experts system handles million-token contexts while dramatically reducing memory and bandwidth strain through asymmetric parameter activation, 16B during decode but only 8B during prefill. This architectural shift matters because prefill cost and cache capacity have become the primary constraint limiting cost-effective scaling of agent workloads. The causal encoder-decoder design signals a strategic pivot toward efficiency-first model design, directly addressing the economics of production inference rather than benchmark performance alone.72
