Nvidia Unveils Breakthrough Tensorrt-Llm Boost: Accelerates Generative Ai Capabilities By Leaps And Bounds

NVIDIA TensorRT-LLM Accelerates Encoder-Decoder Models with In-Flight Batching for Enhanced Generative AI Capabilities

The TensorRT-LLM library now supports encoder-decoder models, expanding its capabilities in optimizing and efficiently running large language models (LLMs) across various architectures. This development is expected to significantly boost performance and functionality of generative AI applications on NVIDIA GPUs.

TensorRT-LLM optimizes inference for diverse model architectures, including decoder-only models like Llama 3.1, mixture-of-experts (MoE) models such as Mixtral, selective state-space models (SSM) like Mamba, and multimodal models for vision-language and video-language applications. The addition of encoder-decoder model support enables highly optimized inference for an even broader range of generative AI applications.

The library leverages the NVIDIA TensorRT deep learning compiler, incorporating the latest optimized kernels for cutting-edge implementations of different attention mechanisms for LLM model execution. It also includes pre- and post-processing steps and multi-GPU/multi-node communication primitives in a simple, open-source API to achieve groundbreaking LLM inference performance on GPUs.

To address nuanced differences in encoder-decoder model families such as T5, mT5, Flan-T5, BART, mBART, FairSeq NMT, UL2, and Flan-UL2, TensorRT-LLM abstracts the common and derivative components, providing generic support for encoder-decoder models. This abstraction enables seamless integration with popular LLM frameworks.

A key feature of TensorRT-LLM is its in-flight batching capability for encoder-decoder architectures. Unlike decoder-only models, which have a more straightforward runtime pattern, encoder-decoder models require more complex handling logic for key-value (KV) cache management and batch management. To address this complexity, TensorRT-LLM introduces several extensions, including:

Runtime support for encoder models, enabling the setup of input/output buffers and model execution. Dual-paged KV cache management for the decoder’s self-attention cache as well as the decoder’s cross-attention cache computed from the encoder’s output. Decoupled batching strategy for the encoder and decoder, allowing for independent and asynchronous batching.

The TensorRT-LLM encoder-decoder models are also supported in the NVIDIA Triton TensorRT-LLM backend for production-ready deployments. This backend provides a streamlined platform for serving these models, offering features such as low-rank adaptation support.

Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning technique that enables customization of LLMs while maintaining impressive performance and minimal resource usage. The TensorRT-LLM BART LoRA support uses optimization capabilities to efficiently handle the low-rank matrices, enabling benefits such as:

Efficient serving of multiple LoRA adapters within a single batch Reduced memory footprint through dynamic loading of LoRA adapters Seamless integration with existing BART model deployments

With this update, enterprises seeking the fastest time to value can leverage NVIDIA NIM, part of the NVIDIA AI Enterprise software platform, which offers optimized inference on popular models from NVIDIA and its partner ecosystem. This provides a comprehensive solution for running LLMs efficiently and effectively.

The expansion of TensorRT-LLM capabilities is expected to significantly enhance generative AI applications, enabling faster performance and improved functionality. As the technology continues to evolve, it will be exciting to see how NVIDIA addresses upcoming enhancements such as FP8 quantization, which promises further improvements in latency and throughput.