NVIDIA Introduces TensorRT-LLM to Enhance Performance of Large Language Models
NVIDIA has recently launched TensorRT-LLM, an open-source library aimed at improving the performance of Large Language Models (LLMs) on NVIDIA hardware. This move comes in response to the growing popularity of generative AI and the increasing use of conversational AI systems such as ChatGPT to meet customer needs. As a leading player in the GPU market, NVIDIA provides the necessary hardware for training extensive language models like ChatGPT, GPT-4, BERT, and Google’s PaLM.
TensorRT-LLM: Redefining AI Inferencing
TensorRT-LLM is an open-source library designed to run on NVIDIA Tensor Core GPUs. Its primary purpose is to provide developers with an environment to experiment with and build new large language models that form the backbone of generative AI platforms like ChatGPT. The library focuses on inference, which enhances the training process of AI by enabling the system to understand concept linkage and make accurate predictions. NVIDIA highlights that TensorRT-LLM dramatically accelerates the speed of inference on their GPUs.
The software is well-equipped to handle contemporary LLMs such as Meta Llama 2, OpenAI GPT-4, Falcon, Mosaic MPT, BLOOM, and more. It incorporates the TensorRT deep learning compiler, optimized kernels, and pre- and post-processing tools. Additionally, it supports multi-GPU and multi-node communication. A notable feature is that developers can utilize TensorRT-LLM without requiring an in-depth understanding of C++ or NVIDIA CUDA.
Naveen Rao, the Vice President of Engineering at Databricks, praised the efficiency of TensorRT-LLM, describing it as easy to use, feature-packed, and efficient. He also noted that the software delivers outstanding performance for LLM serving using NVIDIA GPUs, resulting in cost savings for their customers.
Performance Boost with TensorRT-LLM
When it comes to tasks like article summarization, LLMs running on TensorRT-LLM with an NVIDIA H100 GPU outperform those using the older NVIDIA A100 chip alone. Specifically, GPT-J 6B LLM inference on the H100 GPU saw a fourfold improvement compared to the A100 chip. This enhancement jumps to eight times when coupled with the TensorRT-LLM software.
An essential feature of TensorRT-LLM is its utilization of tensor parallelism. This technique divides weight matrices across devices, enabling simultaneous inference across multiple GPUs and servers.
NVIDIA’s Vision for Affordable AI
Deploying LLMs can be a costly endeavor. NVIDIA aims to reshape how companies account for data centers and AI training in their financial statements through the use of LLMs. With TensorRT-LLM, NVIDIA intends to empower businesses to develop sophisticated generative AI models without a significant increase in total cost of ownership.
Currently, NVIDIA’s TensorRT-LLM is available for early access to registered members of the NVIDIA Developer Program. The wider release is expected in the coming weeks.
Editor Notes
In recent years, the field of generative AI has experienced exponential growth, transforming the way businesses interact with their customers. NVIDIA’s introduction of TensorRT-LLM is a significant development in enhancing the performance of Large Language Models, enabling more efficient and accurate AI inferencing. This open-source library provides developers with the tools they need to experiment and build extensive language models, facilitating the creation of cutting-edge conversational AI systems like ChatGPT.
With TensorRT-LLM, NVIDIA has demonstrated its commitment to driving the advancement of AI technology while addressing the challenges related to cost efficiency. By leveraging the power of NVIDIA GPUs and incorporating tensor parallelism, TensorRT-LLM significantly improves the speed and performance of LLM inference, empowering businesses to leverage generative AI without incurring substantial costs.
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