Nvidia has announced the release of TensorRT-LLM, a new AI inference software that optimizes performance and improves AI inference for large language models (LLM). TensorRT is a platform for high-performance deep learning inference that supports multiple frameworks and applications.
Key features of TensorRT-LLM
TensorRT-LLM works by taking a trained AI model and transforming it into an optimized runtime engine that can run on various devices, such as GPUs, CPUs, and edge devices, with minimal latency and maximum efficiency.
TensorRT-LLM performs several optimizations on the AI model, such as layer fusion, kernel auto-tuning, tensor layout optimization, and memory management. These optimizations help reduce the computational complexity and memory usage of the model, resulting in faster and more efficient inference.
TensorRT-LLM supports features such as dynamic shapes and mixed precision, which enable adaptive and flexible inference for different input sizes and data types. For example, dynamic shapes allow TensorRT to adjust the model’s dimensions according to the input size, while mixed precision allows TensorRT to use lower-precision arithmetic operations to speed up inference and reduce memory consumption, without sacrificing accuracy.
TensorRT-LLM is a plugin for TensorRT that leverages Nvidia’s Ampere architecture and A100 GPUs to enable faster and more efficient deployment of LLMs, such as GPT-3, BERT, and Megatron-LM. LLMs are AI models that can generate natural language from text or speech inputs, and are widely used in various domains and applications, such as natural language processing, conversational AI, text generation, and more.
What makes TensorRT-LLM different from the others?
TensorRT-LLM reduces the memory footprint of LLMs by up to 90%, allowing them to run on smaller and cheaper hardware, while maintaining accuracy and throughput. This is a significant improvement over other solutions that require large amounts of memory and compute resources to run LLMs.
Tensor RT-LLM is compatible with popular LLM frameworks, such as Hugging Face Transformers, PyTorch, and TensorFlow, and can be easily integrated with existing workflows and pipelines. This makes it easy for developers to use their preferred tools and frameworks to build and deploy LLMs with TensorRT-LLM.
Tensor RT-LLM is available as a free download for Nvidia developers and customers, and can be accessed through the Nvidia NGC catalog. It is a powerful and versatile software product that can help accelerate the adoption and innovation of LLMs in various domains and applications.
Conclusion
TensorRT-LLM is a significant advancement in the field of deep learning inference, and it has the potential to make LLMs more accessible and affordable for a wider range of applications.
