vLLM - AI Inference Platforms Tool

Overview

vLLM is an open-source, high-throughput inference library designed to serve large language models efficiently on GPU clusters. According to the project GitHub repository, vLLM focuses on memory-efficient execution and request scheduling to maximize GPU utilization for many concurrent users and batched token generation. It targets production inference and model serving scenarios where throughput, latency, and memory footprint are critical. vLLM implements a token-level scheduler and dynamic batching to aggregate work across requests, plus memory-saving techniques (including model parameter and activation offloading) to fit larger models on limited GPU memory. The library integrates with the Hugging Face model ecosystem and supports parallelism schemes (tensor and pipeline parallelism) for multi-GPU setups. The project provides both a Python API and a production-ready server interface for deployment; the GitHub repository contains example deployments, CLI tools, and notes about integration with quantization and offloading backends. According to the repository, vLLM is actively developed and commonly used to serve models like LLaMA/LLAMA2, OPT, and other HF-format models.

Key Features

• Token-level dynamic scheduler: batches token generation across concurrent requests for higher GPU utilization.
• Memory-efficient execution: activation and parameter offloading to reduce GPU memory footprint.
• Tensor and pipeline parallelism: scale across multiple GPUs for very large models.
• Hugging Face model compatibility: run HF-format checkpoints (LLaMA/OPT/etc.) with minimal conversion.
• Production server and CLI: HTTP/gRPC-compatible serving and command-line tooling for deployments.
• Support for sampling algorithms and beam search with configurable generation parameters.

Example Usage

Example (python):

from vllm import LLM, SamplingParams

# Create an LLM instance (model name should be a HF model id or local path)
llm = LLM(model="meta-llama/Llama-2-7b-chat-hf")

# Configure sampling/generation parameters
sampling_params = SamplingParams(temperature=0.2, max_tokens=128)

# Generate text synchronously; generate() yields generation results
for result in llm.generate("Write a short professional email confirming a meeting.", sampling_params=sampling_params):
    # Each result contains the generated text for the request
    print(result.text)

# Clean up resources when done
llm.shutdown()

# NOTE: This example is a minimal illustration. See the project's GitHub README for server
# startup, advanced configuration (tensor/pipeline parallelism, offloading, quantization), and best practices.

Key Information

• Category: Inference Platforms
• Type: AI Inference Platforms Tool