vLLM: High-Throughput LLM Serving With PagedAttention
PagedAttention makes vLLM the fastest open-source LLM inference server - here is how to deploy it with Docker, tune quantization, and scale across GPUs.
When you naively serve an LLM, the KV (key-value) cache for each request is allocated as a contiguous memory block. Because request lengths vary wildly, up to 60 - 80% of that memory is wasted on internal fragmentation. The result: low GPU utilisation and throughput that barely beats a single user.
PagedAttention: KV Cache as Virtual Memory
vLLM solves this with PagedAttention, described in the 2023 paper. Inspired by OS virtual memory, PagedAttention divides the KV cache into fixed-size pages that can be stored non-contiguously. Pages are allocated on demand and freed immediately when a request finishes - near-zero fragmentation. Combined with continuous batching (new requests slot into the batch mid-flight rather than waiting for the whole batch to drain), vLLM achieves 10 - 24x higher throughput than HuggingFace TGI in head-to-head benchmarks.
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On an A100 80GB with Llama 3.1 70B (Q4 AWQ), vLLM delivers roughly 18 requests/sec at 512 output tokens versus ~4 req/sec for TGI with the same setup. The gap widens further at higher concurrency because continuous batching prevents head-of-line blocking.
Consult the vLLM docs for the full benchmark suite and deployment recipes including Ray Serve integration for auto-scaling clusters.
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Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
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