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TensorRT-LLM

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.

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About TensorRT-LLM

TensorRT-LLM is NVIDIA's purpose-built library for squeezing maximum inference performance out of large language models on NVIDIA GPUs. It takes models from frameworks like PyTorch and Hugging Face Transformers and compiles them into highly optimized TensorRT engines with kernel fusion, mixed-precision execution, and advanced memory management. The library supports FP8 inference on H100 and Blackwell GPUs for significant throughput improvements, along with INT4 and INT8 quantization for reducing memory footprint without severe quality loss.

For production-scale deployment, TensorRT-LLM provides tensor parallelism and pipeline parallelism to distribute models across multiple GPUs and nodes. Its in-flight batching system dynamically groups inference requests for maximum GPU utilization, while KV cache management with paged attention reduces memory waste. The library works with a wide range of model architectures including LLaMA, GPT, Mistral, Mixtral, Falcon, Qwen, Baichuan, and many others, with pre-built optimization profiles for common configurations.

TensorRT-LLM is open-source under Apache 2.0 and integrates natively with NVIDIA Triton Inference Server for serving, as well as with NVIDIA NIM for containerized deployment. While it requires NVIDIA GPU hardware, it delivers state-of-the-art inference throughput that justifies the hardware specificity for organizations running LLMs at scale. The library receives regular updates aligned with new GPU architectures and model releases from the open-source community.

Pricing & Platform Specs

Pricing Summary

Free and 100% open source under the Apache-2.0 license. Developed by NVIDIA, TensorRT-LLM has no software licensing costs or commercial subscription fees; users only pay for their underlying NVIDIA GPU hardware and cloud compute.

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Supported Platforms

Python/C++ library — Linux with NVIDIA GPUs

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Alternatives

All TensorRT-LLM alternatives →
vLLM logo

vLLM

High-throughput LLM serving engine

vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.

Open Source
SGLang logo

SGLang

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.

Open Source
Hugging Face logo

Text Generation Inference

Hugging Face's production LLM serving framework

Text Generation Inference (TGI) is Hugging Face's production-ready serving framework for large language models. It features flash attention, continuous batching, tensor parallelism, quantization via GPTQ/AWQ/EETQ, and Safetensors support. Powers Hugging Face's Inference API and Inference Endpoints, with an OpenAI-compatible API and Docker deployment. Supports LLaMA, Mistral, Falcon, and other popular model architectures.

Open Source

Side-by-Side Comparisons

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SGLang
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TensorRT-LLM

SGLang vs TensorRT-LLM: Structured Agent Serving or NVIDIA-Optimized Inference?

SGLang and TensorRT-LLM both serve performance-sensitive LLM workloads, but they answer different production questions. SGLang is a fast serving framework for language and vision-language models with RadixAttention, structured output support and agent-friendly runtime features. TensorRT-LLM is NVIDIA's acceleration library for teams optimizing hard around NVIDIA GPUs. Choose SGLang for dynamic agent workloads; choose TensorRT-LLM for tightly tuned NVIDIA inference fleets.

SGLangTensorRT-LLM
vLLM logo
vLLM
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NVIDIA logo
TensorRT-LLM

vLLM vs TensorRT-LLM: Open-Source Serving Flexibility or NVIDIA-Optimized Throughput?

vLLM and TensorRT-LLM both target high-throughput LLM inference, but they optimize for different teams. vLLM is the flexible open-source serving engine with broad model support, OpenAI-compatible APIs and a fast path from research to production. TensorRT-LLM is NVIDIA's GPU-optimized stack for teams willing to tune around NVIDIA hardware for maximum performance. Choose vLLM as the default serving layer; choose TensorRT-LLM when peak NVIDIA throughput matters more than portability.

vLLMTensorRT-LLM

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FAQ

What is TensorRT-LLM?

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.

Is TensorRT-LLM free?

Yes — TensorRT-LLM is open source and free to use. Free and 100% open source under the Apache-2.0 license. Developed by NVIDIA, TensorRT-LLM has no software licensing costs or commercial subscription fees; users only pay for their underlying NVIDIA GPU hardware and cloud compute.

Is TensorRT-LLM open source?

Yes — TensorRT-LLM is open source.

Is TensorRT-LLM still maintained?

Yes — TensorRT-LLM is active. Its listing was last verified on September 6, 2026.

What are the best TensorRT-LLM alternatives?

The first editor-selected TensorRT-LLM alternatives are vLLM, SGLang, Text Generation Inference.