vLLM
An open-source, high-throughput serving engine for large language models that uses PagedAttention to efficiently manage GPU memory and maximize inference performance.
vLLM is an open-source library for fast and efficient large language model (LLM) inference and serving. Developed initially at UC Berkeley, it has become one of the most widely adopted LLM serving engines due to its exceptional throughput performance and ease of use. vLLM supports a broad range of model architectures including LLaMA, Mistral, GPT-NeoX, Falcon, and many others.
The key innovation in vLLM is PagedAttention, an attention algorithm inspired by virtual memory paging in operating systems. Traditional LLM serving engines allocate contiguous blocks of GPU memory for each request's key-value cache, leading to significant memory fragmentation and waste. PagedAttention instead stores the KV cache in non-contiguous memory blocks (pages), which are dynamically allocated as needed. This eliminates fragmentation and allows near-optimal memory utilization.
PagedAttention's memory efficiency enables vLLM to serve 2-4x more concurrent requests compared to naive implementations with the same GPU hardware. This translates directly to higher throughput and lower per-request cost. The technique also enables efficient memory sharing between requests, which is particularly beneficial for workloads using beam search, parallel sampling, or shared prompt prefixes.
Beyond PagedAttention, vLLM includes continuous batching, which dynamically adds new requests to running batches without waiting for all requests in a batch to complete. This eliminates the head-of-line blocking problem that affects static batching approaches, where a single long-running request holds up an entire batch of shorter requests. Continuous batching keeps the GPU continuously utilized regardless of variation in output sequence lengths.
vLLM integrates seamlessly with serverless GPU platforms like Cumulus, where it serves as the inference runtime for many LLM deployments. Its support for quantized models, speculative decoding, tensor parallelism across multiple GPUs, and OpenAI-compatible API endpoints makes it a production-ready choice for teams deploying large language models at scale.
Related Terms
KV Cache
A memory buffer that stores previously computed key and value tensors during autoregressive language model inference to avoid redundant recalculation for each new token.
GPU Inference
The process of running a trained machine learning model on a GPU to generate predictions or outputs from new input data.
Model Serving
The infrastructure and process of deploying trained machine learning models as accessible endpoints that can receive inputs and return predictions in real time.
Batch Inference
The technique of grouping multiple inference requests together and processing them simultaneously on a GPU to maximize throughput and hardware utilization.
Model Quantization
The process of reducing the numerical precision of a model's weights and activations (e.g., from 32-bit to 8-bit or 4-bit) to decrease memory usage and increase inference speed.