Published · Phase 1
KV Cache Quantization: Q4 vs Q8 vs F16
KV-cache quantization reduces the memory used by stored attention keys and values, usually trading numerical precision and kernel behavior for more context or concurrency. Q4, Q8, and F16 should be compared on the same model, runtime, prompt, output, and hardware.
Editorial review: complete · Updated 2026-08-30
Plain-English answer
F16 keeps more precision and consumes more cache memory; Q8 uses less; Q4 compresses further. The practical benefit is additional room for tokens or simultaneous sequences, provided the runtime supports the format reliably.
Why it matters
On a 32 GB GPU, cache growth can determine whether a long-context request fits after the model loads. Reducing cache precision may preserve a deployment that would otherwise run out of memory.
Real StackBench examples
| Configuration | Evidence state | Metric | Source |
|---|---|---|---|
| Qwen3.8-27B · RTX 5090 owner-collected public lab · llama.cpp | Measured · Grade A | 22452109312 bytes | StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816 |
| Qwen3.8-27B · RTX 5090 owner-collected public lab · llama.cpp | Measured · Grade A | 22642950144 bytes | StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816 |
| Qwen3.8-27B · RTX 5090 owner-collected public lab · llama.cpp | Measured · Grade A | 22898802688 bytes | StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816 |
Common misconception
KV-cache quantization does not shrink the model weights. It changes the growing attention state, so the savings depend on context length, layers, dimensions, and active sequence count.
Measured / example table
| Configuration | Evidence state | Metric | Source |
|---|---|---|---|
| Qwen3.8-27B · RTX 5090 owner-collected public lab · llama.cpp | Measured · Grade A | 22452109312 bytes | StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816 |
| Qwen3.8-27B · RTX 5090 owner-collected public lab · llama.cpp | Measured · Grade A | 22642950144 bytes | StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816 |
| Qwen3.8-27B · RTX 5090 owner-collected public lab · llama.cpp | Measured · Grade A | 22898802688 bytes | StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816 |
Decision rule
Begin with the highest cache precision that leaves safe headroom; move to Q8 or Q4 when a measured capacity need justifies it; then validate latency and quality on the exact workload.
Related benchmarks
Use the RTX 5090 Qwen3.8 cache matrix for controlled performance behavior, the VRAM guide for capacity budgeting, and the context-length guide for the dimension that drives cache growth.
Planner / Explore CTA
Carry this workload into the ComputeSage Planner. It keeps missing evidence visible rather than inventing a recommendation.
Sources / methodology
Review the ComputeSage evidence methodology. Bound selector: RTX5090 Grade-A KV data. Eligible source: StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816; StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816; StackBench controlled lab approved source qwen38-rtx5090-owner-matrix-20260816.