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NVIDIA’s optimized runtime for accelerating large language model inference on GPUs.
DESCRIPTION
TensorRT-LLM is an open-source library and runtime that accelerates inference for large language models on NVIDIA GPUs. It provides optimized kernels, graph-level fusions, and execution plans for modern transformer architectures. The stack supports FP8, INT8, and other quantization modes, tensor/pipeline parallelism, and multi-GPU scaling. Integration with Triton Inference Server enables production deployments with model ensembles. Its focus is maximum throughput, minimal latency, and cost-efficient serving for enterprise workloads.
ALTERNATIVE DEFINITIONS
NVIDIA
TensorRT-LLM is an open-source library that accelerates inference of large language models on NVIDIA GPUs.
Vendor statement emphasizing acceleration and NVIDIA GPU focus.
NVIDIA
TensorRT-LLM is an open-source library for optimizing and deploying LLMs with high-performance inference on NVIDIA GPUs.
Documentation emphasizing optimization and deployment for high-performance inference.
RELATED TERMS
NVIDIA TensorRT CUDA FasterTransformer Quantization KV Cache Speculative Decoding Pipeline Parallelism NVIDIA GPU Inference Engine
COMPETING TERMS
vLLM OpenVINO GenAI
KNOWLEDGE AREAS
AI MLOps Infrastructure Platform & Tooling
TARGET AUDIENCE
AI Engineer MLOps Engineer Hardware Engineer Cloud Architect Software Developer
EXTERNAL SOURCES
- Wikipedia - Nvidia TensorRT
- NVIDIA - Product overview and resources
- NVIDIA - Official docs and examples
- NVIDIA - Source code and deployment guides
TOP QUESTIONS
What models are supported by TensorRT-LLM?
Popular transformer families like Llama, GPT, and Mistral are supported with optimized kernels and reference configurations.
Category: Capabilities
How does TensorRT-LLM reduce latency?
It uses kernel fusions, quantization, parallelism, and graph optimizations to reduce overhead and maximize GPU utilization.
Category: Mechanism
Can TensorRT-LLM scale across multiple GPUs?
Yes. It supports tensor and pipeline parallelism, as well as multi-node deployments through NVIDIA’s ecosystem.
Category: Scalability
How does TensorRT-LLM compare to vLLM?
Both target high-throughput inference. TensorRT-LLM tightly integrates with NVIDIA accelerators and Triton; vLLM focuses on paged attention and broad framework compatibility.
Category: Comparison
Does TensorRT-LLM support quantization?
Yes. FP8, INT8, and other modes are supported to reduce memory and boost throughput with minimal accuracy loss.
Category: Capabilities
What is required to deploy TensorRT-LLM?
NVIDIA GPUs, compatible drivers, CUDA and TensorRT components, and model conversion or build steps tailored to the target architecture.
Category: Requirements
Is TensorRT-LLM open source?
Yes. It’s available on GitHub with examples and integration guides for production use.
Category: Licensing
How does TensorRT-LLM integrate with Triton Inference Server?
It provides backends and artifacts that Triton can host, enabling load balancing, ensembles, and observability.
Category: Integration
What are common mistakes when adopting TensorRT-LLM?
Skipping calibration for quantization, misconfiguring batch sizes, and underestimating memory fragmentation can hurt performance.
Category: Mistakes