F16 vs Q8 KV Cache on RTX 5090

Quick verdict

Quick verdict guidance: Use Q8/FP8 KV cache for most inference services and F16 only when precision testing shows a benefit and context is modest. F16 doubles the cache footprint without improving base model weights.

Comparison table

Memory / capacity

Memory / capacity guidance: Moving from 8-bit to 16-bit cache roughly doubles the K/V allocation. On a 32 GB GPU that can turn a healthy long-context service into an out-of-memory configuration.

Observed LLM performance

Observed LLM performance guidance: Q8 often preserves output quality while enabling more context or sequences. F16 can be useful for sensitive workloads but may reduce batching and increase memory traffic.

Prefill vs decode

Prefill vs decode guidance: Use F16 for short controlled prompts; use Q8 for 16K+ or concurrent serving unless evaluation demonstrates otherwise.

Power

Power guidance: The larger F16 cache increases memory traffic and can reduce efficient batching, though exact energy impact depends on kernels and context.

Current market cost

Current market cost guidance: F16 may require a higher-memory card for no visible benefit. Q8 usually offers the better capacity-per-dollar operating point.

Which models fit

Which models fit guidance: Moving from 8-bit to 16-bit cache roughly doubles the K/V allocation. On a 32 GB GPU that can turn a healthy long-context service into an out-of-memory configuration.

Who each option suits

Who each option suits guidance: Q8 is appropriate for production chat, RAG, and agent services. F16 is for controlled evaluation, short prompts, or demonstrated quality-sensitive applications.

What stands out

Start Q8. Compare F16 only when a task regression is traceable to cache precision; otherwise spend the saved memory on context, concurrency, or a higher-quality weight quant.

Q8 often preserves output quality while enabling more context or sequences. F16 can be useful for sensitive workloads but may reduce batching and increase memory traffic.

Calibrated FP8, reduced context, and prompt compression are safer capacity choices than immediately moving to Q4.

Evidence limitations

• Cache precision and weight precision are separate decisions.
• Compare output quality with identical prompts, seeds/sampling, model revision, and runtime.

Planner CTA

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

Plain-English answer

Plain-English answer guidance: Use Q8/FP8 KV cache for most inference services and F16 only when precision testing shows a benefit and context is modest. F16 doubles the cache footprint without improving base model weights.

Why it matters

Why it matters guidance: Q8 is appropriate for production chat, RAG, and agent services. F16 is for controlled evaluation, short prompts, or demonstrated quality-sensitive applications. Use F16 for short controlled prompts; use Q8 for 16K+ or concurrent serving unless evaluation demonstrates otherwise.

Real StackBench examples

Common misconception

Common misconception guidance: F16 consumes scarce memory quickly, while Q8 still requires validation on sensitive long-range tasks. Cache precision and weight precision are separate decisions.

Measured / example table

Decision rule

Decision rule guidance: Start Q8. Compare F16 only when a task regression is traceable to cache precision; otherwise spend the saved memory on context, concurrency, or a higher-quality weight quant.

Related benchmarks

Related benchmarks guidance: Q8 often preserves output quality while enabling more context or sequences. F16 can be useful for sensitive workloads but may reduce batching and increase memory traffic. Calibrated FP8, reduced context, and prompt compression are safer capacity choices than immediately moving to Q4.

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