Q4 vs Q8 KV Cache on RTX 5090

Quick verdict

Quick verdict guidance: Q8 KV cache is the recommended default. Q4 KV cache is a capacity tool for long context or more users, not an automatic performance upgrade; use it only after a quality check on retrieval, copying, and reasoning.

Comparison table

Memory / capacity

Memory / capacity guidance: Q4 can roughly halve the K/V representation relative to Q8, but total process memory falls by less because weights, workspaces, graphs, and other buffers do not change.

Observed LLM performance

Observed LLM performance guidance: Cache quantization may permit larger batches and contexts, indirectly increasing throughput. The direct kernel speed effect is less predictable than the memory saving.

Prefill vs decode

Prefill vs decode guidance: Q4 is most valuable as context × sequences grows. On short 512–2K tests, measured memory differences may be too small to justify a decision.

Power

Power guidance: Smaller cache traffic may improve efficiency, while increased enabled concurrency can raise total system utilization.

Current market cost

Current market cost guidance: The feature saves hardware capacity, but the validation effort is real. A silent long-context accuracy regression can cost more than an additional GPU.

Which models fit

Which models fit guidance: Q4 can roughly halve the K/V representation relative to Q8, but total process memory falls by less because weights, workspaces, graphs, and other buffers do not change.

Who each option suits

Who each option suits guidance: Q8 suits general chat, coding, and RAG. Q4 suits memory-bound high-concurrency or very-long-context services that have a representative evaluation set.

What stands out

Deploy Q8 first. If cache pressure blocks a required context/concurrency target, compare Q4 against Q8 on 20–50 real tasks and keep Q4 only when errors remain acceptable.

Cache quantization may permit larger batches and contexts, indirectly increasing throughput. The direct kernel speed effect is less predictable than the memory saving.

Shorten context, retrieve fewer chunks, summarize history, lower concurrency, or quantize model weights before accepting cache-quality risk.

Evidence limitations

• Always report the cache format alongside model quantization and context.
• A cache setting cannot fix a model-weight package that already consumes nearly all VRAM.

Planner CTA

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

Plain-English answer

Plain-English answer guidance: Q8 KV cache is the recommended default. Q4 KV cache is a capacity tool for long context or more users, not an automatic performance upgrade; use it only after a quality check on retrieval, copying, and reasoning.

Why it matters

Why it matters guidance: Q8 suits general chat, coding, and RAG. Q4 suits memory-bound high-concurrency or very-long-context services that have a representative evaluation set. Q4 is most valuable as context × sequences grows. On short 512–2K tests, measured memory differences may be too small to justify a decision.

Real StackBench examples

Common misconception

Common misconception guidance: Q4 can introduce task-specific degradation that a load test or short benchmark will miss. Always report the cache format alongside model quantization and context.

Measured / example table

Decision rule

Decision rule guidance: Deploy Q8 first. If cache pressure blocks a required context/concurrency target, compare Q4 against Q8 on 20–50 real tasks and keep Q4 only when errors remain acceptable.

Related benchmarks

Related benchmarks guidance: Cache quantization may permit larger batches and contexts, indirectly increasing throughput. The direct kernel speed effect is less predictable than the memory saving. Shorten context, retrieve fewer chunks, summarize history, lower concurrency, or quantize model weights before accepting cache-quality risk.

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