Nice to see a provider being transparent about KV cache quantisation. I've been suspecting that some providers do this silently whilst heavily promoting their unquantised weights, even though KV quantisation can degrade quality more than weight quantisation.

However, I wish their testing were more detailed. Firstly, some model families are more sensitive to KV quantisation than others (only Kimi K2.6 was tested). Secondly, the evaluation suite they use to claim that FP8 KV quantisation is indistinguishable is noticeably lacking coding benchmarks; in long-running tasks, minor tool call errors compound over time.

vLLM's study also concluded that "FP8 can deliver meaningful latency and capacity gains with small or negligible accuracy loss". Their benchmarks include LiveCodeBench 6.

https://vllm-project.github.io/2026/04/22/fp8-kvcache.html