How Concurrency Changes LLM Inference Performance

Plain-English answer

Plain-English answer guidance: Concurrency increases aggregate throughput until KV cache, batching delay, or compute saturation raises per-user latency too far. Start with 1, 4, and 8 sequences; pick the highest level that meets p95 TTFT and decode targets.

Why it matters

Why it matters guidance: Internal APIs and agent platforms need this measurement; a personal local assistant usually values concurrency one latency instead. Test the real context distribution; short synthetic prompts can overstate safe concurrency by a large margin.

Real StackBench examples

Common misconception

Common misconception guidance: An aggregate tok/s headline can conceal an unusably slow individual experience. Report per-user decode, TTFT, total throughput, and queue delay together.

Measured / example table

Decision rule

Decision rule guidance: Benchmark C1, C4, C8, and the expected peak with fixed prompt/output lengths. Stop increasing concurrency when p95 latency violates the product target.

Related benchmarks

Related benchmarks guidance: Aggregate throughput may rise from tens to over 100 tok/s as concurrency increases, while each user receives fewer tokens per second and waits longer for the first token. Add replicas, route small tasks to a smaller model, shorten context, use prefix caching, or scale out rather than deepening one queue.

Planner / Explore CTA

Compare this recommendation against your model, context, concurrency, latency, and budget in the ComputeSage Planner.

Sources / methodology

Recommendations combine official specifications, public model/runtime documentation, adjacent benchmark observations, and clearly labeled engineering estimates. Review the ComputeSage evidence methodology.

• vLLM installation and hardware documentation
• vLLM quantized KV-cache documentation
• StackBench controlled lab approved source ninfer-qwen38-27b-nvfp4-rtx5090-owner-20260821