How to Read LLM Tokens/sec Benchmarks

Plain-English answer

Plain-English answer guidance: Read inference benchmarks by separating prompt processing, single-stream decode, aggregate throughput, TTFT, memory, power, and quality. A result missing runtime, precision, context, and concurrency is a lead—not a buying answer.

Why it matters

Why it matters guidance: This framework helps buyers interpret vendor claims, community posts, and StackBench rows without discarding useful approximate evidence. Prompt and cache sizes must be comparable. A 512-token benchmark cannot predict a 32K-document service without adjustment.

Real StackBench examples

Common misconception

Common misconception guidance: No normalization can repair missing model identity or a fundamentally different workload. Never compare aggregate and single-stream rates as if they were the same metric.

Measured / example table

Decision rule

Decision rule guidance: Use a benchmark when at least model, runtime, precision, and workload shape are close to your deployment. Apply a range and validate locally instead of demanding an impossible exact match.

Related benchmarks

Related benchmarks guidance: Single-stream tok/s describes one user's answer speed; aggregate tok/s describes service capacity; prefill tok/s describes prompt ingestion; TTFT describes perceived responsiveness. When exact data is missing, combine official specs, adjacent model results, weight math, and a short rental/cloud test to create a bounded estimate.

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
• llama.cpp project and backend documentation
• NVIDIA GeForce RTX 5090 specifications
• NVIDIA DGX Spark specifications
• StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816