Automating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy

​​Lucas Liebenwein

NVIDIA TensorRT LLM enables developers to build high-performance inference engines for large language models (LLMs), but deploying a new architecture…

NVIDIA

•

Lucas Liebenwein

•8 min read•advanced•

--

•View Original

Hugging FacePyTorchTransformersV

Overview

The article discusses NVIDIA TensorRT LLM AutoDeploy, a beta feature that automates the inference optimization process for large language models (LLMs). It highlights how AutoDeploy simplifies the deployment of new architectures by transforming PyTorch models into optimized inference graphs, reducing manual effort and deployment time.

What You'll Learn

1

How to automate inference optimizations for large language models using AutoDeploy

2

Why a compiler-driven approach is beneficial for model deployment

3

When to use AutoDeploy for new or experimental model architectures

Prerequisites & Requirements

Basic understanding of PyTorch and large language models
Familiarity with NVIDIA TensorRT and CUDA(optional)

Key Questions Answered

How does AutoDeploy optimize inference for large language models?

AutoDeploy optimizes inference by automatically extracting computation graphs from PyTorch models and applying transformations like sharding, quantization, and caching. This allows for a streamlined deployment process without the need for manual reimplementation of inference logic, enabling faster and more efficient model deployment.

What types of models does AutoDeploy support?

AutoDeploy currently supports over 100 text-to-text LLMs and offers early support for vision language models (VLMs) and state space models (SSMs). It is particularly effective for new research architectures and internal variants, allowing for quick deployment and performance optimization.

What performance improvements can be expected with AutoDeploy?

Using AutoDeploy, the NVIDIA Nemotron 3 Nano model achieved up to 350 tokens per second per user throughput and up to 13,000 output tokens per second. This performance is comparable to manually optimized models, demonstrating the effectiveness of the automated approach.

Key Statistics & Figures

Tokens per second per user throughput

up to 350

Achieved by the NVIDIA Nemotron 3 Nano model using AutoDeploy

Output tokens per second

up to 13,000

For latency and high-throughput applications using AutoDeploy

Technologies & Tools

Some links below are affiliate links. We may earn a commission if you make a purchase.

Backend

Nvidia Tensorrt

Used for optimizing inference performance of large language models

Framework

Pytorch

Serves as the base framework for model development and integration with AutoDeploy

Key Actionable Insights

1
Utilize AutoDeploy to streamline the deployment process for new LLM architectures, reducing the time and effort needed for manual optimizations.
This is particularly useful for teams working with rapidly evolving models or those developing novel architectures, as it allows for quicker iterations and faster time-to-market.

2
Leverage the inference optimization features of AutoDeploy, such as sharding and quantization, to enhance model performance without extensive manual tuning.
These optimizations can significantly improve throughput and latency, making it easier to meet performance requirements in production environments.

Common Pitfalls

1

Relying solely on manual optimizations can lead to increased deployment times and missed performance opportunities.

This often occurs when teams do not leverage automated tools like AutoDeploy, which can streamline the optimization process and allow for quicker iterations.

Related Concepts

Large Language Models

Inference Optimization Techniques

Compiler-driven Workflows

Nvidia Nemotron Models