vLLM Leads in Inference Serving Benchmarks
A 2026 benchmark of leading LLM inference frameworks concluded that vLLM offers the best token throughput and memory efficiency for models larger than 13B parameters. While HuggingFace's TGI was praised for its ease of use in prototyping, Nvidia's Triton was recommended for its flexibility in serving diverse model types and modalities.
- The core of vLLM's performance is an algorithm called PagedAttention, which manages the memory for attention keys and values similar to how operating systems use virtual memory and paging. This method partitions the KV cache into blocks, allowing for non-contiguous storage and reducing memory waste from a typical 60-80% in other systems to less than 4%. - vLLM originated as a research project at UC Berkeley's Sky Computing Lab and has since evolved into a widely adopted, community-driven open-source project. - Key features relevant for production workloads include continuous batching of incoming requests, optimized CUDA kernels, multi-LoRA support for efficiently serving fine-tuned models, and an OpenAI-compatible API server. - For specific use cases like short input and output sequences, NVIDIA's TensorRT-LLM can deliver up to 1.34 times higher throughput than vLLM due to its deep optimization for NVIDIA hardware. - To handle long prompts without blocking other requests, vLLM implements chunked prefill, which breaks down the processing of large inputs into smaller segments. - The architecture supports disaggregating the prefill and decode stages of inference, allowing engineering teams to scale the compute resources for each phase independently for better GPU utilization and cost-efficiency. - While vLLM is a leader, the inference engine landscape continues to evolve with newer competitors like SGLang and MAX, which some benchmarks show can offer performance advantages in specific scenarios, such as lower tail latency. - Beyond NVIDIA GPUs, vLLM has expanded support to include a variety of hardware such as AMD and Intel GPUs, ARM CPUs, and TPUs, reflecting its broad adoption in the MLOps ecosystem.
