vLLM Inference Server Delivers Up to 24x Throughput
The production-grade inference server vLLM is delivering a 2x to 24x throughput increase for LLM inference, according to a recent analysis. The performance gains are attributed to its PagedAttention algorithm and optimized memory management. Stripe reported a 73% reduction in its inference costs after deploying vLLM, highlighting its potential for financial firms migrating AI workloads to production environments.
- The PagedAttention algorithm virtualizes the GPU's KV cache memory, similar to how operating systems use virtual memory and paging for CPUs. This allows vLLM to store continuous logical blocks of the KV cache in non-contiguous physical memory blocks, overcoming the memory fragmentation that plagues traditional inference systems and achieving near-optimal memory usage with less than 4% waste. - A key feature contributing to vLLM's high throughput is continuous batching, which processes requests at the iteration level instead of waiting for the entire batch to complete. As soon as a sequence in a batch finishes, a new request is inserted, maximizing GPU utilization and significantly improving throughput in high-concurrency scenarios. - The vLLM project originated at UC Berkeley's Sky Computing Lab and is now a vibrant open-source project with over 800 contributors from both academia and industry, including IBM, which uses it as the core inference engine for its WatsonX.ai products. It is part of the broader ecosystem of the Large Model Systems Organization (LMSYS), a research group focused on making large models more accessible. - While vLLM excels in throughput under high concurrency, benchmarks show that for low-concurrency workloads or single-user scenarios, other systems like NVIDIA's TensorRT-LLM may offer higher raw compute speed and lower latency for the first token. vLLM's strength lies in scaling interactive, multi-user applications efficiently. - PagedAttention's architecture enables efficient memory sharing not just for the initial prompt but across different decoding branches in complex sampling methods like beam search. This significantly reduces the memory footprint for generating multiple outputs from a single prompt, a common task in quantitative analysis and scenario modeling. - Compared to Hugging Face's Text Generation Inference (TGI), vLLM is generally favored for high-throughput, large-batch offline workloads due to its memory efficiency. TGI, with its deep integration into the Hugging Face ecosystem, is often considered more straightforward for deploying a wide variety of models, especially in latency-sensitive, real-time applications with moderate traffic. - vLLM supports a wide range of hardware beyond just NVIDIA GPUs, including AMD and Intel GPUs, as well as specialized AI accelerators like AWS Neuron, Google TPUs, and Intel Gaudi. This flexibility is crucial for fintech developers looking to avoid vendor lock-in and optimize for different cloud or on-premise hardware stacks. - For quantized models, which use lower-precision arithmetic to reduce memory and speed up computation, vLLM's performance can be less optimal compared to solutions like LMDeploy or TensorRT-LLM that have more mature optimizations for quantization. This is a key consideration for deploying very large models on memory-constrained hardware.
