KV Cache Quantization: Q4 vs Q8 vs F16

Plain-English answer

Plain-English answer guidance: KV-cache quantization is worth using when cache—not weights—is limiting context or concurrency. Start at Q8/FP8, validate quality, and move to Q4 only when the additional capacity has a clear product benefit.

Why it matters

Why it matters guidance: Useful for RAG, agents with long histories, and shared servers. Usually unnecessary for short single-user prompts with ample memory. The feature pays off most beyond 16K–32K or with many active sequences, not on a short one-request benchmark.

Real StackBench examples

Common misconception

Common misconception guidance: Quality effects can be task-specific and difficult to notice without targeted tests. Use calibrated scales when the runtime supports them and the workload is sensitive to long-context accuracy or exact copying.

Measured / example table

Decision rule

Decision rule guidance: Enable Q8/FP8 once F16 cache limits the target. Enable Q4 only after long-context correctness tests pass and the saved memory is actually used.

Related benchmarks

Related benchmarks guidance: The main gain is more sequences/context. Direct speed may improve, remain similar, or regress depending on conversion and kernel efficiency. Reduce context, lower concurrency, cache prefixes, or choose smaller weight precision before taking a more aggressive cache-quality risk.

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 quantized KV-cache documentation
• llama.cpp project and backend documentation
• StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816