vLLM
by vLLM community / Sky Computing Lab (origins)
High-performance open-source inference and serving engine for large language models
See https://vllm.ai and the project GitHub for code and docs.
One-line summary
vLLM is an open-source LLM inference engine optimized for high throughput, low latency, and efficient GPU/accelerator utilization. It provides production-ready serving features (OpenAI-compatible API, streaming, batching, quantization support) and advanced runtime optimizations such as PagedAttention and continuous batching.
Features
- PagedAttention: OS-style memory paging for attention KV caches to reduce memory usage and enable larger context or more concurrent requests.
- Continuous (dynamic) batching: build batches on-the-fly to maximize GPU utilization while keeping latency low.
- Optimized kernels: integrations with FlashAttention / FlashInfer and custom CUDA kernels for faster prefill and decode.
- Quantization & compression: supports GPTQ, AWQ, INT4/INT8, FP8 workflows (enables cheaper inference while preserving quality).
- Speculative decoding & parallel sampling: speed up autoregressive decoding by speculatively generating tokens and verifying.
- Prefix caching & multi-LoRA: efficient handling of repeated prefixes and multiple LoRA adapters for inexpensive fine-tuning switches.
- OpenAI-compatible API server: drop-in compatibility for systems expecting an OpenAI-like API surface.
- Streaming outputs and server-side decoding options (beam search, top-k/top-p sampling).
- Multi-GPU & distributed inference: tensor/pipeline/data/expert parallelism for scaling across GPUs and nodes.
- Broad hardware plugin support: NVIDIA GPUs (CUDA), AMD, Intel, TPUs (via emerging backends), and some cloud accelerators supported via plugins.
Superpowers
- Exceptional throughput per GPU for conversational and batch inference workloads due to PagedAttention + continuous batching.
- Lower total cost of inference: real-world adopters report serving more concurrent users with fewer GPUs (often 2x or more efficiency gains vs naive servlets; 50–70% cost reductions are commonly cited in community benchmarks).
- Flexibility: runs many HF/PyTorch models with minimal code changes and offers an OpenAI-compatible API, making adoption straightforward for teams.
- Extensible: supports modern quantization flows and LoRA-based adaptors, enabling efficient production workflows.
Hardware & Model Support
- Works with mainstream transformer LLMs available on Hugging Face: Llama-family, Mistral, Qwen, and many open architectures.
- Growing support for Mixture-of-Experts models and multi-modal/sketchy large models in the ecosystem.
- Accelerators: primarily NVIDIA GPUs (CUDA); community/backed plugins extend support to AMD, Intel, Google Cloud TPUs (notably work during 2025 to improve TPU backend performance), and other vendor accelerators via adapters.
Benchmarks & Performance (summary)
- vLLM emphasizes throughput and concurrent request density. Community/partner benchmarks show large improvements in GPU utilization and requests/sec versus straightforward PyTorch serving setups.
- Notable optimizations that drive benchmarks: reduced KV cache memory pressure (PagedAttention), reduced CPU pre/post-processing overheads, and speculative decoding.
- Vendors and cloud partners have published performance comparisons; results vary by model, quantization level, batch shapes, and hardware. Always benchmark with your models and expected request patterns.
Typical use cases
- High-concurrency chat and assistant backends
- Low-latency production APIs that must maximize cost-efficiency
- On-prem or hybrid deployments where control over inference stack and telemetry is required
- Research and development where reproducible, high-throughput inference is needed for evaluation at scale
Licensing & OSS status
- vLLM is published as an open-source project. Check the official GitHub repository for the exact license text and contributor guidelines before embedding in proprietary products or commercial redistributions.
Adoption & ecosystem
- Integrate via HTTP OpenAI-compatible calls from existing clients — minimal code changes required if your client already expects the OpenAI chat/completions endpoints.
- Rapid community adoption; integrated by cloud teams and infrastructure providers. In 2025 vLLM gained additional attention for TPU backend work and tighter cloud integrations.
- Ecosystem tools: adapters for quantization toolchains (GPTQ, AWQ), monitoring/telemetry integrations, and community-contributed plugins for specific hardware.
Risks & considerations
- Production behavior depends on workload shape: latency-sensitive single-token workloads vs batched throughput scenarios will differ in how much benefit vLLM provides.
- Plugin and backend maturity: newer hardware backends (e.g., TPUs, non-NVIDIA accelerators) may offer different stability/performance depending on community maturity.
- Always test quantized models for accuracy/regression relative to the FP16/FP32 baseline before production rollout.
Where to read more / references
- vLLM official site: https://vllm.ai
- vLLM GitHub: (search for the project repository to check README, license, and install/usage docs)
- Community posts, cloud partner announcements, and benchmark blog posts (search for recent 2024–2025 writeups for up-to-date performance notes).