TGI vs. TensorRT LLM: The Best Inference Library for Large Language Models

Introduction

This blog explores two inference libraries: Text Generation Inference (TGI) and TensorRT LLM. Both are designed to optimize the deployment and execution of LLMs, focusing on speed and efficiency.

TGI, created by Hugging Face, is a production-ready library for high-performance text generation, offering a simple API and compatibility with various models from the Hugging Face hub.

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

TGI 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 TGI 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 TGI and TensorRT LLM offer powerful solutions for serving large language models (LLMs), each with unique strengths tailored to different deployment needs. TGI is optimized for text generation and streaming, making it a strong choice for those within the Hugging Face ecosystem. 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 TGI 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

1. https://huggingface.co/docs/text-generation-inference/index
2. https://github.com/NVIDIA/TensorRT-LLM/
3. https://huggingface.co/blog/martinigoyanes/llm-inference-at-scale-with-tgi
4. https://docs.nvidia.com/tensorrt-llm/index.html

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