Inference · REFERENCE GUIDE

Understanding FP8 KV Cache in vLLM

Understand what KV cache quantization changes and build a compatibility and quality check before enabling it.

By Inference Lab Editorial
Published · Updated · 2 min read
vLLMKV CacheFP8
Reference material, not a completed lab report. Benchmark data pending real-world testing.

Problem

Long-context or concurrent requests can put pressure on cache memory even when the model weights fit on the GPU.

Conclusion

FP8 KV cache changes the precision used for stored key and value tensors. It is separate from weight quantization. Check the attention backend, hardware and scale handling supported by your exact vLLM release before enabling it.

Environment

Record the vLLM release, attention backend, GPU, CUDA runtime, model revision and cache dtype. Compare against the same model and workload with the baseline cache configuration.

Symptoms

Cache allocation can limit the available request capacity. A change in cache dtype may also change numerical behavior or backend selection. No error output from an actual run is presented here.

Cause

The cache retains attention state for previously processed tokens. Lower-precision storage reduces the bytes used for eligible tensors, but the overall process also needs weights, activations and other allocations.

Solution

Read the quantized-cache documentation for your pinned release. The following fragment illustrates the option name; it requires a compatible installed environment and a model you have validated.

# Replace MODEL_ID with your validated checkpoint.
vllm serve "$MODEL_ID" --kv-cache-dtype fp8

Review how cache scales are obtained. Do not assume every checkpoint supplies appropriate scales. Run a short correctness test first, then compare representative long prompts, generation quality and memory behavior against the baseline.

Verification

Benchmark data pending real-world testing.

Record peak memory, accepted request capacity and application-specific quality checks. A smaller cache representation does not by itself prove lower latency or higher useful throughput.

Caveats

Supported FP8 formats, scale options and attention behavior vary by backend and release. Avoid treating an old compatibility restriction as current without checking the versioned documentation.

References

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