Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

TensorRT LLM NVIDIA's open source library for optimizing LLM inference with state of the art performance on NVIDIA GPUs. When to use TensorRT LLM Use TensorRT LLM when: Deploying on NVIDIA GPUs (A100, H100, GB200) Need maximum throughput (24,000+ tokens/sec on Llama 3) Require low latency for real time applications Working with quantized models (FP8, INT4, FP4) Scaling across multiple GPUs or nodes Use vLLM instead when: Need simpler setup and Python first API Want PagedAttention without TensorRT compilation Working with AMD GPUs or non NVIDIA hardware Use llama.cpp instead when: Deploying on CPU or Apple Silicon Need edge deployment without NVIDIA GPUs Want simpler GGUF quantization format Quick start Installation Basic inference Serving with trtllm serve Key features Performance optimizations In flight batching : Dynamic batching during generation Paged KV cache : Efficient memory management Flash Attention : Optimized attention kernels Quantization : FP8, INT4, FP4 for 2 4× faster inference CUDA graphs : Reduced kernel launch overhead Parallelism Tensor parallelism (TP) : Split model across GPUs Pipeline parallelism (PP) : Layer wise distribution Expert parallelism : For Mixture of Experts models Multi node : Scale beyond single machine Advanced features Speculative decoding : Faster generation with draft models LoRA serving : Efficient multi adapter deployment Disaggregated serving : Separate prefill and generation Common patterns Quantized model (FP8) Multi GPU deployment Batch inference Performance benchmarks Meta Llama 3 8B (H100 GPU): Throughput: 24,000 tokens/sec Latency: ~10ms per token vs PyTorch: 100× faster Llama 3 70B (8× A100 80GB): FP8 quantization: 2× faster than FP16 Memory: 50% reduction with FP8 Supported models LLaMA family : Llama 2, Llama 3, CodeLlama GPT family : GPT 2, GPT J, GPT NeoX Qwen : Qwen, Qwen2, QwQ DeepSeek : DeepSeek V2, DeepSeek V3 Mixtral : Mixtral 8x7B, Mixtral 8x22B Vision : LLaVA, Phi 3 vision 100+ models on HuggingFace References [Optimization Guide](references/optimization.md) Quantization, batching, KV cache tuning [Multi GPU Setup](references/multi gpu.md) Tensor/pipeline parallelism, multi node [Serving Guide](references/serving.md) Production deployment, monitoring, autoscaling Resources Docs : https://nvidia.github.io/TensorRT LLM/ GitHub : https://github.com/NVIDIA/TensorRT LLM Models : https://huggingface.co/models?library=tensorrt llm

tensorrt-llm