World's Fair 2024
From model weights to API endpoint with TensorRT LLM: Philip Kiely and Pankaj Gupta
Overview
This talk introduces TensorRT-LLM, an NVIDIA SDK designed for high-performance deep learning inference on NVIDIA GPUs. It focuses on optimizing large language models (LLMs) to achieve higher throughput and lower latency, crucial for production environments. The presentation covers building, configuring, benchmarking, and deploying TensorRT-LLM engines, emphasizing practical application through live coding and detailed explanations.
Who should watch
- AI Engineers looking to optimize LLM inference performance.
- Product Managers and Builders evaluating tools for deploying LLMs.
- Developers seeking to understand the trade-offs in LLM deployment.
- Anyone interested in maximizing GPU utilization for AI workloads.
Key takeaways
- TensorRT-LLM builds upon TensorRT by providing specific optimizations and plugins for LLMs, enabling efficient execution of computation graphs on GPUs.
- Key optimizations include in-flight batching, optimized kernels for varying sequence lengths, and support for various precisions like FP16, INT8, and FP8.
- Building a TensorRT-LLM engine requires specific knowledge of the production environment, including target GPUs, sequence lengths, and batch sizes, as engines are not universally portable.
- FP8 quantization, particularly when applied to both weights and the KV cache, offers significant performance gains, especially for compute-bound phases like the context phase in LLMs.
- While TensorRT-LLM offers substantial performance benefits, it can have a steeper learning curve, and some underlying components are not fully open-source.
- Tools like Truss can automate the engine building and deployment process, abstracting away some of the complexity of TensorRT-LLM.
- Benchmarking is critical to validate performance in production, focusing on metrics like time to first token and total tokens per second, and understanding the impact of batch sizes.
- Deploying TensorRT-LLM engines typically involves using an inference server like Triton, with platforms like Base 10 offering managed solutions for auto-scaling, fast cold starts, and optimized infrastructure.
Notable quotes
*TensorRT is a SDK for high-performance deep learning inference on Nvidia GPUs.*
*TensorRT LLM is a mechanism on top of that that's going to give us a ton of plugins and a ton of optimization specifically for large language models.*
*The performance gains are worth it.*
Unofficial community note. Prefer the recording for nuance.