AI article
KV Cache Quantization in LLM Serving: FP8 and INT8 Tradeoffs, the Silent config.json Trap, and How to Measure It Fairly
Community description: FP8 and INT8 KV cache quantization can double your context budget, but a silent config.json setting can change your accuracy without warning. Here is
Dev.to | Oct 6, 2026 | AI Tech News
Automated excerpt
FP8 vs INT8 for the KV cache: what is the real tradeoff? Does KV cache quantization quantize the model weights too? A config. json KV dtype or baked-in KV scales override your intent silently.
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