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 is an open-source library developed by NVIDIA to optimize large language model (LLM) inference for maximum throughput and low latency. It leverages the powerful capabilities of NVIDIA's TensorRT technology to accelerate model serving, making it ideal for production environments requiring high-performance inference on NVIDIA GPUs (such as A100, H100, and GB200). The library is designed to handle various LLM tasks with significant performance improvements, allowing up to 10-100 times faster inference compared to standard frameworks like PyTorch, especially when using quantized models and in-flight batching. Whether you're deploying on a single GPU or scaling across multiple GPUs or nodes, TensorRT-LLM enables smooth and efficient model inference deployment at scale.
You can install TensorRT-LLM using Docker by running `docker pull nvidia/tensorrt_llm:latest`, or via pip with `pip install tensorrt_llm==1.2.0rc3`. Note that you will need CUDA 13.0.0, TensorRT 10.13.2, and Python 3.10-3.12 for proper functionality.
TensorRT-LLM is optimized for use on NVIDIA GPUs, specifically the A100, H100, and GB200 models. It supports multi-GPU scaling and can be deployed across multiple nodes for large-scale inference workloads.
No, TensorRT-LLM is designed specifically for NVIDIA GPUs and is not compatible with non-NVIDIA hardware, such as AMD GPUs or CPUs. For non-NVIDIA hardware, consider alternatives like vLLM or llama.cpp.
TensorRT-LLM includes several optimizations such as in-flight batching, a paged key-value (KV) cache for memory management, Flash Attention for optimized attention kernels, and support for quantized models (FP8, INT4, FP4) to accelerate inference speed and reduce memory usage.
Yes, TensorRT-LLM supports multi-GPU deployment. You can configure tensor parallelism to split models across GPUs, as well as leverage pipeline parallelism and expert parallelism for advanced model distribution and scaling.
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