RTX 5090 KV Cache: F16 vs Q8 vs Q4

Plain-English answer

Plain-English answer guidance: Use Q8 KV cache as the default quality/memory compromise on an RTX 5090. Use Q4 KV cache only when long context or concurrency is the actual bottleneck, and keep F16 for short-context quality-sensitive work with ample headroom.

Why it matters

Why it matters guidance: This setting matters most for RAG, long documents, agent histories, and multi-user serving. Short personal chats rarely need the risk of the most aggressive cache quantization. Q8 is a practical default for 16K–64K experiments. Q4 becomes attractive for very long windows or many simultaneous sequences when the model weights already fit comfortably.

Real StackBench examples

Common misconception

Common misconception guidance: A cache format may load successfully yet degrade retrieval, copying, or long-range reasoning in subtle ways. Use calibrated scales when the runtime supports them and quality matters.

Measured / example table

Decision rule

Decision rule guidance: Start Q8. Move to Q4 only after measuring memory pressure and confirming answer quality; move to F16 only when cache is small and precision-sensitive output justifies it.

Related benchmarks

Related benchmarks guidance: Quantized cache usually changes maximum context/concurrency more than raw decode speed. Q8 often preserves quality well; Q4 can save substantial memory but deserves a task-specific regression set. Reduce maximum context, cap concurrency, enable prefix caching, or choose a smaller weight quantization before adopting an untested cache format.

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
• NVIDIA GeForce RTX 5090 specifications
• StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816