vLLM’s PagedAttention reveals memory fragmentation’s hidden cost in A100 inference scaling

When deploying LLMs on A100s, even minor memory inefficiencies in KV caching can cripple throughput, yet vLLM’s PagedAttention claims to eliminate such waste. The real impact becomes clear only when comparing benchmarks: while traditional systems reserve unused memory slots for maximum sequence lengths—like reserving 2048 tokens even for a 10-token prompt—the fragmented blocks of PagedAttention allow handling far more concurrent requests without triggering OOM errors. The result is throughput gains of 2x to 4x over vanilla HuggingFace Transformers, but at a cost: compute limits now dictate performance instead of memory.

The core mechanism mimics OS virtual memory by splitting KV cache into non-contiguous pages, but its efficiency depends on balancing GPU memory usage. While batching more requests reduces per-token latency spikes, excessive memory pressure can crash systems during sudden prompt-length increases. The command below illustrates the optimal setup for Llama-3-70B, where gpu_memory_utilization must be calibrated precisely:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3-70B \
    --tensor-parallel-size 4 \
    --gpu-memory-utilization 0.90 \
    --max-model-len 8192

The shift in bottleneck dynamics also alters cost models. Instead of relying solely on high-end GPUs for high traffic, tighter KV caching reduces hardware requirements while demanding careful management of inter-GPU communication—such as NVLink—otherwise, the gains from PagedAttention can vanish. For teams prioritizing pipeline efficiency, Continuous Batching further smooths GPU utilization by dynamically replacing completed sequences, preventing idle cycles that once dominated performance. The trade-off remains: while vLLM’s architecture lowers the bar for high-throughput inference, the new constraints lie in optimizing prompt lengths and RAG workflows to avoid excessive memory spikes.