Benchmarks Pit DFlash Against Qwen Stack

Hot take: this is not a clean “which backend is faster” question so much as “which stack is faster for your exact interaction pattern.”

• –DFlash is explicitly built for speculative decoding and parallel drafting, so it is optimized for low-latency generation in the direct-chat path.
• –The vLLM recipe is a broader serving stack with patches, long-context support, and tool-use plumbing; the Medium write-up claims very high throughput on a single 3090, but also calls out acceptance variance and prompt-dependent cliffs.
• –The “direct prompting was super fast, opencode was slow” symptom strongly points to client-side orchestration overhead, tool-call round trips, warmup/CUDA-graph effects, and different sampling/prefill behavior, not just model inference speed.
• –For coding, the better stack is the one with the lowest end-to-end latency and the least jitter in your IDE loop; raw tok/s is a weak proxy if your agent spends time in tool calls, retries, or long-context prefill.
• –If you care about consistency, benchmark first-token latency, time-to-first-tool-call, and sustained tokens/sec separately; that is where these two systems are most likely to diverge.