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vLLM is an open-source LLM serving engine with 50K+ GitHub stars achieving 14-24x higher throughput than HuggingFace Transformers through PagedAttention memory management. Serves LLaMA, Mistral, Qwen, and 100+ architectures with continuous batching, tensor parallelism for multi-GPU, and prefix caching. Provides an OpenAI-compatible API server for drop-in replacement. Used in production by major AI companies for serving models at scale with optimal GPU utilization.
vLLM is the leading LLM serving engine. PagedAttention manages KV cache like virtual memory, eliminating fragmentation for 14-24x throughput gains.
Continuous batching, tensor parallelism across GPUs, and prefix caching. Supports 100+ model architectures.
OpenAI-compatible API for drop-in replacement. Used by major AI companies and cloud providers in production.
Free and open-source
Python, CUDA, Docker, Kubernetes
Fast serving framework for LLMs and vision models
SGLang is an open-source serving framework for large language and vision-language models, designed for low latency and high throughput. It features RadixAttention for automatic KV cache reuse, compressed finite state machines for fast structured output generation, continuous batching, and tensor parallelism. With over 25,000 GitHub stars, it supports models like LLaMA, Mistral, Qwen, and Gemma on NVIDIA and AMD GPUs.
LoRAX and vLLM both serve LLM inference workloads but optimize for different deployment scenarios. LoRAX specializes in serving hundreds of fine-tuned LoRA adapters from a single base model, enabling cost-effective multi-tenant model serving. vLLM provides the highest-throughput single-model inference through PagedAttention memory management, continuous batching, and speculative decoding optimizations.
Ollama and vLLM both serve LLMs but target completely different stages of the AI workflow. Ollama is the developer's go-to tool for running models locally with a simple CLI and instant setup. vLLM is a high-throughput inference engine designed for production serving with PagedAttention and continuous batching. This comparison helps you understand when local simplicity matters and when production performance takes priority.
NVIDIA's LLM inference optimization and acceleration library
TensorRT-LLM is NVIDIA's open-source library for optimizing LLM inference on NVIDIA GPUs. It provides kernel fusion, quantization (FP8, INT4, INT8), KV cache optimization, and in-flight batching to maximize throughput. Supports multi-GPU and multi-node setups with tensor and pipeline parallelism, and integrates with Triton Inference Server for production deployment of models like LLaMA, GPT, Mistral, and Qwen.
