AI article
Why LLMs Run Out of VRAM: KV Cache Fragmentation and How PagedAttention Fixes It
Community description: Why does a 7GB quantized model crash a 24GB GPU? Here is how the KV cache grows, why naive allocation wastes 80% of VRAM, and how PagedAttention solves it.
Dev.to | Oct 1, 2026 | Syed Anzar
Automated excerpt
Most developers assume GPU memory during inference is dominated by model weights. Centralized Physical Block Pool: At startup, vLLM pre-allocates all available GPU memory into a pool of physical blocks. Block size 16: Lowest internal fragmentation.
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