Your GPU Is Lying to You About Its Capacity

You have 80GB of VRAM. Your 70B model weights eat 35GB in fp16. So you have 45GB free for inference, right? Wrong. Here's why production LLM serving is a memory management problem masquerading as a compute problem and what the best systems in the world do about it.

I spent three months fighting a GPU cluster that kept OOM crashing on requests that by every napkin calculation should have fit in memory. Requests with 2K input tokens, 512 output tokens, a batch of 8. The math checked out. The system didn't. What I discovered reshaped how I think about every layer of the inference stack.

The culprit: KV cache fragmentation. And the lesson: GPU memory in LLM inference is not a static resource it is a dynamic, highly contested one, and if you're not managing it like an OS manages RAM, you're leaving 30–60% of your throughput on the floor.

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