Every LLM inference engineer hits this wall eventually. You deployed a model, it works in testing,...
Every LLM inference engineer hits this wall eventually.
You deployed a model, it works in testing, then production traffic arrives. Suddenly your 80GB A100 is OOM on a 70B model that "should fit."
The culprit is almost always the KV Cache. But most discussions stop at "it caches the Key and Value matrices" — which doesn't help you predict when you'll run out of memory.
This post gives you a quick estimator formula, explains when to worry, and what levers actually help.
The One-Number Formula
An explanation of the KV cache memory problem in production LLM serving and how PagedAttention (the technique behind vLLM) solves…
FP8 and INT8 KV caches cut attention state ~50%, but they shift the target model's logit distribution — and that can quietly…
KV caches are one of the most critical techniques for efficient inference in LLMs in production.
🔗 Try it — free, no signup: fitllm.run ⭐ Open source (MIT, one file):...
Compare TurboQuant, OSCAR, and EpiCache: three 2026 methods compressing the LLM KV cache to cut long-context memory cost
How transformer inference actually works under the hood — and why KV cache is the single most...
