Why Your GPU Isn't the Bottleneck—It's Memory Fragmentation

The real bottleneck in LLM serving isn’t model size—it’s memory fragmentation. Traditional engines pre-allocate contiguous blocks for KV cache based on max sequence length, wasting 60–80% of that space for typical variable-length requests. This directly limits concurrent users per GPU and inflates cloud costs.

VLLM’s paged attention borrows OS virtual memory techniques: it breaks KV cache into small, non-contiguous pages allocated on demand. A block table maps logical addresses to physical memory. Internal and external fragmentation disappear, and shared system prompts are cached once across requests, hitting 95% reuse in RAG workflows.

The operational impact is immediate: tuning three settings—GPU memory utilization, prefix caching, and chunked prefill—can deliver 50% throughput gains. But caution is required. Overly aggressive memory utilization causes OOM errors under load bursts, and chunked prefill adds scheduling overhead. Teams must benchmark carefully.

For engineering leaders, inference memory management is a primary cost lever. These techniques are proven in open-source VLLM but demand deliberate configuration and monitoring. The strategic takeaway: serving optimization often yields more than model improvements when scaling LLM economics.