Your 7B LLM Doesn’t Need More VRAM. It Needs a Better

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_The mathematics of transformer memory, Grouped-Query Attention, PagedAttention, FlashAttention, and 2-bit KV cache quantization. A deep engineering investigation into why relatively small language models can consume gigabytes of GPU memory during inference._

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A developer downloads an 8-billion-parameter language model, loads it into a GPU with 24 GB of VRAM, and expects to have enough memory to run several inference requests simultaneously. The model loads successfully, and a short prompt produces an answer without any obvious problem. However, when the developer increases the context length, introduces concurrent requests, or attempts to serve multiple users, GPU memory consumption increases unexpectedly. Eventually, CUDA reports an out-of-memory error.

The immediate reaction is often to blame the model’s parameter count. Perhaps an 8B model is too large for the available hardware. Perhaps the weights need to be quantized further. Perhaps it is time to purchase a GPU with more memory.

But there is another component that deserves considerably more attention: the Key-Value (KV) cache.

During autoregressive generation, a transformer repeatedly uses information from previously processed tokens. Recomputing all historical key and value projections at…

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