Scaling LLMs with NVIDIA Triton and NVIDIA TensorRT-LLM Using Kubernetes

Scaling Large Language Models with NVIDIA Triton and NVIDIA TensorRT-LLM Using Kubernetes

Summary

This article explores how to scale large language models (LLMs) using NVIDIA Triton and NVIDIA TensorRT-LLM within a Kubernetes environment. It provides a step-by-step guide on optimizing LLMs with TensorRT-LLM, deploying them with Triton Inference Server, and autoscaling the deployment using Kubernetes.

Introduction

Large language models (LLMs) have become indispensable in AI applications such as chatbots, content generation, summarization, classification, and translation. These models, including Llama, Gemma, GPT, and Nemotron, have demonstrated human-like understanding and generative abilities. However, deploying these models efficiently and scaling them to handle real-time inference requests can be challenging.

Optimizing LLMs with TensorRT-LLM

NVIDIA TensorRT-LLM is a Python API that provides various optimizations to enhance the efficiency of LLMs on NVIDIA GPUs. These optimizations include kernel fusion, quantization, in-flight batch, and paged attention. Here are the steps to optimize LLMs with TensorRT-LLM:

1. Download Model Checkpoints: Download the model checkpoints from Hugging Face using an access token.
2. Create a Kubernetes Secret: Create a Kubernetes secret with the access token to be used in the deployment process.
3. Build TensorRT Engines: Use TensorRT-LLM to build engines that contain the model optimizations.
4. Configure Parallelism: Configure tensor parallelism (TP) and pipeline parallelism (PP) based on the model size and GPU memory size.

Deploying LLMs with Triton Inference Server

NVIDIA Triton Inference Server is an open-source inference serving software that supports multiple frameworks and hardware platforms. Here are the steps to deploy LLMs with Triton:

1. Create a Kubernetes Deployment: Create a Kubernetes deployment for Triton servers.
2. Create a Kubernetes Service: Create a Kubernetes service to expose the Triton servers as a network service.
3. Deploy the Optimized Models: Deploy the optimized models with Triton Inference Server.

Autoscaling LLM Deployment with Kubernetes

Kubernetes can be used to autoscale the deployment of LLMs based on the volume of inference requests. Here are the steps to autoscale the deployment:

1. Monitor Performance: Monitor the application’s performance by examining the containers, Pods, and Services.
2. Use Prometheus: Use Prometheus to collect metrics for autoscaling.
3. Configure Horizontal Pod Autoscaler (HPA): Configure HPA to make decisions on scaling up or down the number of deployment and GPUs based on the amount of inference requests.

Example Use Case

To illustrate the process, let’s consider an example of deploying the Llama 3.1 405B model sharded across Amazon EC2 Accelerated GPU instances using NVIDIA Triton and NVIDIA TensorRT-LLM.

Step-by-Step Guide

Here is a step-by-step guide on how to optimize LLMs with TensorRT-LLM, deploy them with Triton Inference Server, and autoscale the deployment using Kubernetes:

1. Optimize LLMs with TensorRT-LLM:

   • Download the model checkpoints from Hugging Face.
   • Create a Kubernetes secret with the access token.
   • Build TensorRT engines with TensorRT-LLM.
   • Configure parallelism based on the model size and GPU memory size.

2. Deploy LLMs with Triton Inference Server:

   • Create a Kubernetes deployment for Triton servers.
   • Create a Kubernetes service to expose the Triton servers as a network service.
   • Deploy the optimized models with Triton Inference Server.

3. Autoscale LLM Deployment with Kubernetes:

   • Monitor the application’s performance.
   • Use Prometheus to collect metrics for autoscaling.
   • Configure HPA to make decisions on scaling up or down the number of deployment and GPUs.

Additional Resources

• Triton Inference Server Tutorials: Visit the Triton Inference Server tutorials on GitHub for more information on optimizing and deploying LLMs.
• Scaling LLMs on AWS: Learn how to autoscale LLMs on multinodes with Triton and TensorRT-LLM on AWS.

Table: Supported GPUs for TensorRT-LLM

Table: Comparison of LLM Deployment Methods

Table: Steps for Autoscaling LLM Deployment

Conclusion

Scaling large language models with NVIDIA Triton and NVIDIA TensorRT-LLM using Kubernetes provides a flexible and efficient way to handle real-time inference requests. By optimizing LLMs with TensorRT-LLM, deploying them with Triton Inference Server, and autoscaling the deployment using Kubernetes, enterprises can manage resources effectively and minimize costs.