TL;DR:
- NVIDIA introduced TensorRT-LLM, an open-source software for optimizing large language models.
- Collaboration with industry leaders aims to accelerate and enhance LLMs’ implementation.
- TensorRT-LLM doubles H100 accelerator performance in key tests, reaching impressive results with A100 as well.
- The software supports popular LLMs and leverages tensor parallelism for efficient computation.
- Real-time batch processing enhances workload management, delivering twofold higher inference performance on H100.
- The library’s transformer engine simplifies data conversion, making H100 outperform A100.
Main AI News:
In a groundbreaking move, NVIDIA has unveiled TensorRT-LLM, an open-source software tailored to turbocharge the performance of large language models (LLMs). Set to debut in the coming weeks, this platform promises to revolutionize the landscape of language model implementation.
Collaborating closely with industry giants like Meta, Anyscale, Cohere, Deci, Grammarly, Mistral AI, MosaicML (a Databricks subsidiary), OctoML, Tabnine, and Together AI, NVIDIA is on a mission to enhance and accelerate LLMs. Yet, grappling with the immense size and unique characteristics of these models has proven to be a formidable challenge. Enter TensorRT-LLM, a purpose-built library meticulously crafted to address this predicament.
At its core, this software encompasses TensorRT, a deep learning compiler, a user-friendly kernel, pre- and post-processing tools, and performance-boosting components tailored for NVIDIA accelerators. What sets it apart is its ability to empower developers with the means to harness new LLMs, all without necessitating an in-depth understanding of C++ or CUDA. Leveraging an open, modular Python API, TensorRT-LLM allows for the definition, optimization, and execution of cutting-edge architectures while staying nimble in adapting to the ever-evolving realm of LLMs.
According to NVIDIA’s estimations, employing TensorRT-LLM results in a remarkable twofold increase in performance for the H100 accelerator during the GPT-J 6B test, a pivotal part of MLPerf Inference v3.1. Remarkably, the utilization of the Llama2-model pushes the A100’s performance to an impressive 4.6. TensorRT-LLM doesn’t stop there; it boasts fully optimized versions of several renowned LLMs, including Meta Llama 2, OpenAI GPT-2 and GPT-3, Falcon, Mosaic MPT, BLOOM, and more.
This software leverages tensor parallelism, also known as “Taxi parallelism,” a form of model parallelism where weight matrices are intelligently distributed across devices. During parallel processing, TensorRT-LLM deftly distributes the computational load among multiple accelerators connected by NVLink or interconnected nodes via NVIDIA Quantum 2 InfiniBand. Scalability becomes a reality with this approach.
To efficiently manage workloads, TensorRT-LLM adopts a real-time batch processing technique, allowing for the seamless asynchronous handling of small, varied requests on a single accelerator. This groundbreaking feature is available for all NVIDIA accelerators and is the driving force behind achieving a twofold boost in inference performance on the H100.
Finally, in the case of the H100, the library harnesses the transformer engine to generate an engine manual. This simplifies the dynamic conversion of data to the FP8 format, a highly reliable and efficient format for processing, significantly reducing memory consumption and ultimately making the H100 outpace the A100.
Conclusion:
NVIDIA’s TensorRT-LLM is a game-changer for the language model market. Its promise of doubling performance, support for key LLMs, and efficient workload management will likely reshape the industry, opening up new possibilities for language model applications and capabilities. This software signifies a significant step toward unleashing the full potential of large language models in various sectors.
