Which GPUs are best for TensorRT-LLM?

It is designed for modern NVIDIA GPUs, specifically architecture generations like Ampere (A100), Hopper (H100), and Blackwell (GB200) to take advantage of specialized hardware acceleration.

How does TensorRT-LLM compare to vLLM?

TensorRT-LLM is optimized specifically for NVIDIA hardware, often offering higher throughput and lower latency through deep hardware integration and C++ kernels, whereas vLLM focuses on ease of use and Python-first APIs.

Can I use this for multi-GPU setups?

Absolutely. It includes configurations for Tensor Parallelism (splitting layers across GPUs) and Pipeline Parallelism (distributing layers across GPUs) for massive models.

Does this skill support model quantization?

Yes, it provides implementation patterns for FP8, INT4, and FP4 quantization, which can double inference speed and reduce memory requirements by 50% or more.

TensorRT-LLM Optimization

Name: TensorRT-LLM Optimization
Author: zechenzhangAGI

byzechenzhangAGI

•

384

Ciencia de Datos y ML

Accelerates Large Language Model inference on NVIDIA GPUs using state-of-the-art optimization techniques for maximum throughput and minimal latency.

Acerca de

This skill equips Claude with the expertise to implement NVIDIA's TensorRT-LLM library, enabling high-performance inference serving for production-grade AI applications. It provides guidance on optimizing LLM performance through advanced techniques such as FP8/INT4 quantization, in-flight batching, and Paged KV caching. Whether you are deploying on single H100s or scaling across multi-node GPU clusters, this skill helps configure tensor and pipeline parallelism to achieve up to 100x speedups compared to standard PyTorch implementations, making it essential for high-scale AI research and engineering.

Características Principales

High-throughput inference with in-flight batching and Paged KV cache
Advanced quantization support including FP8, INT4, and FP4 formats
Performance benchmarking for Llama 3, DeepSeek, and Mixtral models
Ready-to-use patterns for Triton Inference Server and trtllm-serve
384 GitHub stars
Multi-GPU scaling via Tensor, Pipeline, and Expert parallelism

Casos de Uso

Deploying real-time chatbots with sub-second latency on NVIDIA H100 GPUs
Optimizing memory footprint of 70B+ parameter models using FP8 quantization
Scaling high-volume inference endpoints for enterprise production environments

Acerca de

Características Principales

High-throughput inference with in-flight batching and Paged KV cache
Advanced quantization support including FP8, INT4, and FP4 formats
Performance benchmarking for Llama 3, DeepSeek, and Mixtral models
Ready-to-use patterns for Triton Inference Server and trtllm-serve
384 GitHub stars
Multi-GPU scaling via Tensor, Pipeline, and Expert parallelism

Casos de Uso

Deploying real-time chatbots with sub-second latency on NVIDIA H100 GPUs
Optimizing memory footprint of 70B+ parameter models using FP8 quantization
Scaling high-volume inference endpoints for enterprise production environments