NVIDIA Hopper™ and NVIDIA Grace Hopper™ processors — including theNVIDIA L4 Tensor Core GPUand theNVIDIA H100 NVL GPU, both launched today. Each platform is optimized for in-demand workloads, including AI video, image generation, large language model deployment and recommender...
Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With advancements in technologies such as chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed to LLMs are becoming increasingly lengthy,...
DeepSpeed-MoE for NLG: Reducing the training cost of language models by five times While recent works like GShard (opens in new tab) and Switch Transformers (opens in new tab) have shown that the MoE model structure can reduce large model pretraining cost for encoder-deco...
Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, especially for devices with limited DRAM capacity. This paper tackles the challenge of effi...
Large language models (LLMs) offer incredible new capabilities, expanding the frontier of what is possible with AI. However, their large size and unique execution characteristics can make them difficult to use in cost-effective ways. NVIDIA has been working closely with leading companies,...
大模型(LLM)最新论文摘要 | LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models Authors: Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With...
(a recurring cost). The most popularlarge language models (LLMs)today can reach tens to hundreds of billions of parameters in size and, depending on the use case, may require ingesting long inputs (or contexts), which can also add expense. For example,retrieval-augmented generation(RAG) ...
With an efficient API tool powered by NVIDIA GPUs and optimized for fast inference with NVIDIA® TensorRT™-LLM, Perplexity makes it easy for developers to integrate cutting-edge, open-source large language models (LLMs) into their projects.
Deploy large language models on AWS Inferentia2 using large model inference containers Clean up Inside the root directory of your repository, run the following code to clean up your resources: makedestroy Conclusion In this post, we introduced how you can u...
Explore the Features and Tools of NVIDIA Triton Inference Server Large Language Model Inference Triton offers low latency and high throughput for large language model (LLM) inferencing. It supportsTensorRT-LLM, an open-source library for defining, optimizing, and executing LLMs for inference in produ...