DeepSpeed MII vs. TensorRT LLM: Choosing the Best Inference Library for Large Language Models

Table of contents

Introduction

This blog explores two inference libraries: DeepSpeed MII and TensorRT LLM. Both are designed to optimize the deployment and execution of LLMs, focusing on speed and efficiency.

DeepSpeed MII, an open-source Python library developed by Microsoft, aims to make powerful model inference accessible, emphasizing high throughput, low latency, and cost efficiency.

TensorRT LLM, an open-source framework from NVIDIA, is designed for optimizing and deploying large language models on NVIDIA GPUs. It leverages TensorRT for inference acceleration and supports popular LLM architectures.

Performance Metrics

DeepSpeed MII and TensorRT LLM are popular solutions for deploying large language models (LLMs), renowned for their efficiency and performance. We will compare them based on latency, throughput, and time to first token (TTFT):

Features

Both DeepSpeed MII and TensorRT LLM offer robust capabilities for serving large language models efficiently. Below is a detailed comparison of their features:

Ease of Use

Scalability

Integration

Conclusion

Both DeepSpeed MII and TensorRT LLM offer powerful solutions for serving large language models (LLMs), each with unique strengths tailored to different deployment needs. DeepSpeed MII excels in scenarios involving long prompts and short outputs, with a focus on low-cost weight-only quantization. In contrast, TensorRT LLM, with its deep integration into NVIDIA's GPU ecosystem, is highly optimized for throughput and latency, making it a strong choice for NVIDIA-centric deployments.

Ultimately, the choice between DeepSpeed MII and TensorRT LLM will depend on specific project requirements, including performance metrics, ease of use, and existing infrastructure. As the demand for efficient LLM serving continues to grow, both libraries are poised to play critical roles in advancing AI applications across various industries.

Resources