DeepSeek V4.1 Flash Throughput: Fix the Real Bottleneck

DeepSeek V4.1 Flash inference optimization requires defining whether the goal is aggregate output capacity, per-request decode speed, prompt-processing rate, time to first token, or end-to-end latency, since higher batching can improve total tokens per second while worsening individual responsiveness. Meaningful benchmarks should reproduce production-like prompt and output lengths, concurrency, cache behavior, warm-up state, hardware, checkpoint revision, runtime version, and latency objectives, while changing only one serving variable at a time and tracking quality, failures, and cost. The model’s compressed KV-cache design and bounded replay can reduce memory pressure, but reported cache savings do not imply fixed end-to-end throughput gains. vLLM and SGLang reports demonstrate potentially significant improvements under their respective configurations, but their differing hardware and workloads prevent direct comparison without matched tests. Operators should diagnose whether bottlenecks arise from prefill, decoding, cache capacity, scheduling, communication, or queueing; tune concurrency, precision, parallelism, caching, and speculative decoding cautiously; and retain only changes that meet service-level objectives. Choosing between self-hosting and a managed API should similarly be based on comparable real-request tests of latency, reliability, total cost per completed task, and operational effort rather than peak throughput alone.

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