Why LLMs Run Out of VRAM: KV Cache Fragmentation and How PagedAttention Fixes It
You deploy a 13-billion parameter model quantized to 4-bit weights. The static model weights consume roughly 7.5 GB of VRAM. You put it on an NVIDIA RTX 4090 with 24 GB of memory, confident you have more than 16 GB of headroom for traffic. Then you run a batch of 8 concurrent requests with 4,000-to…
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- 2026-10-01 12:34 · DEV Community — Machine Learning
Why LLMs Run Out of VRAM: KV Cache Fragmentation and How PagedAttention Fixes It