{"id":30414,"date":"2022-09-20T14:00:00","date_gmt":"2022-09-20T21:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=30414"},"modified":"2022-09-19T11:30:40","modified_gmt":"2022-09-19T18:30:40","slug":"nvidia-launches-large-language-model-cloud-services","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/09\/20\/nvidia-launches-large-language-model-cloud-services\/","title":{"rendered":"NVIDIA Launches Large Language Model Cloud Services"},"content":{"rendered":"<div class=\"wp-block-image is-style-default\">\n<figure class=\"alignright size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"300\" height=\"159\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/09\/NVIDIA-LLM-Services-GTC-Fall-22.png\" alt=\"\" class=\"wp-image-30417\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/09\/NVIDIA-LLM-Services-GTC-Fall-22.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/09\/NVIDIA-LLM-Services-GTC-Fall-22-150x80.png 150w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><\/div>\n\n\n<p>NVIDIA today announced two new large language model cloud AI services \u2014 the NVIDIA  <a href=\"https:\/\/www.nvidia.com\/en-us\/gpu-cloud\/nemo-llm-service\/\" target=\"_blank\" rel=\"noreferrer noopener\">NeMo Large Language Model Service<\/a> and the <a href=\"https:\/\/www.nvidia.com\/en-us\/gpu-cloud\/bionemo\/\" target=\"_blank\" rel=\"noreferrer noopener\">NVIDIA BioNeMo LLM Service<\/a> \u2014 that  enable developers to easily adapt LLMs and deploy customized AI applications for  content generation, text summarization, chatbots, code development, as well as protein  structure and biomolecular property predictions, and more.<\/p>\n\n\n\n<p>The NeMo LLM Service allows developers to rapidly tailor a number of pretrained  foundation models using a training method called prompt learning on NVIDIA-managed infrastructure. The NVIDIA BioNeMo Service is a cloud application programming interface (API) that expands LLM use cases beyond language and into scientific applications to  accelerate drug discovery for pharma and biotech companies.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\"><p>\u201cLarge language models hold the potential to transform every industry,\u201d said Jensen Huang, founder and  CEO of NVIDIA. \u201cThe ability to tune foundation models puts the power of LLMs within reach of millions of  developers who can now create language services and power scientific discoveries without needing to build a massive model from scratch.\u201d<\/p><\/blockquote>\n\n\n\n<p><strong>NeMo LLM Service Boosts Accuracy With Prompt Learning, Accelerates Deployments<\/strong><\/p>\n\n\n\n<p>With the NeMo LLM Service, developers can use their own training data to customize foundation models  ranging from 3 billion parameters up to Megatron 530B, one of the world\u2019s largest LLMs. The process  takes just minutes to hours compared with the weeks or months required to train a model from scratch.<\/p>\n\n\n\n<p>Models are customized with prompt learning, which uses a technique called p-tuning. This allows  developers to use just a few hundred examples to rapidly tailor foundation models that were originally  trained with billions of data points. The customization process generates task-specific prompt tokens,  which are then combined with the foundation models to deliver higher accuracy and more relevant responses for specific use cases.<\/p>\n\n\n\n<p>Developers can customize for multiple use cases using the same model and generate many different  prompt tokens. A playground feature provides a no-code option to easily experiment and interact with  models, further boosting the effectiveness and accessibility of LLMs for industry-specific use cases.<\/p>\n\n\n\n<p>Once ready to deploy, the tuned models can run on cloud instances, on-premises systems or through an API.<\/p>\n\n\n\n<p><strong>BioNeMo LLM Service Enables Researchers to Tap Power of Massive Models<\/strong><\/p>\n\n\n\n<p>The <a href=\"https:\/\/blogs.nvidia.com\/blog\/2022\/09\/20\/bionemo-large-language-models-drug-discovery\/\" target=\"_blank\" rel=\"noreferrer noopener\">BioNeMo LLM Service<\/a> includes two new BioNeMo language models for chemistry and biology  applications. It provides support for protein, DNA and biochemical data to help researchers discover  patterns and insights in biological sequences.<\/p>\n\n\n\n<p>BioNeMo enables researchers to expand the scope of their work by leveraging models that contain  billions of parameters. These larger models can store more information about the structure of proteins,  evolutionary relationships between genes, and even generate novel biomolecules for therapeutic  applications.<\/p>\n\n\n\n<p><strong>Cloud API Provides Access to Megatron 530B, Other Ready-Made Models<\/strong><\/p>\n\n\n\n<p>In addition to tuning foundation models, the LLM services include the option to use ready-made and  custom models through a cloud API.<\/p>\n\n\n\n<p>This gives developers access to a broad range of pretrained LLMs, including Megatron 530B. It also  provides access to T5 and GPT-3 models created with the <a href=\"https:\/\/developer.nvidia.com\/blog\/?search_posts_filter=NeMo+Megatron\" target=\"_blank\" rel=\"noreferrer noopener\">NVIDIA NeMo Megatron<\/a> framework \u2014 now  available in open beta \u2014 to support a broad range of applications and multilingual service requirements.<\/p>\n\n\n\n<p>Leaders in automotive, computing, education, healthcare, telecommunications and other industries are  using NeMo Megatron to pioneer services for customers in Chinese, English, Korean, Swedish and other languages.<\/p>\n\n\n\n<p><strong>Availability<\/strong><\/p>\n\n\n\n<p>The NeMo LLM and BioNeMo services and cloud APIs are expected to be available in early access starting next month. Developers can apply now for more details.<\/p>\n\n\n\n<p>The beta release of the NeMo Megatron framework is available from <a href=\"https:\/\/www.nvidia.com\/en-us\/gpu-cloud\/\" target=\"_blank\" rel=\"noreferrer noopener\">NVIDIA NGC<\/a>\u2122 and is optimized to  run on <a href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/dgx-foundry\/\" target=\"_blank\" rel=\"noreferrer noopener\">NVIDIA DGX\u2122 Foundry<\/a> and <a href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/dgx-superpod\/\" target=\"_blank\" rel=\"noreferrer noopener\">NVIDIA DGX SuperPOD<\/a>\u2122, as well as accelerated cloud instances from  Amazon Web Services, Microsoft Azure and Oracle Cloud Infrastructure. <\/p>\n\n\n\n<p>To experience the NeMo  Megatron framework, developers can try <a href=\"https:\/\/www.nvidia.com\/en-us\/launchpad\/ai\/train-a-large-scale-nlp-model-with-nemo-megatron\/\" target=\"_blank\" rel=\"noreferrer noopener\">NVIDIA LaunchPad<\/a> labs at no charge.<\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/inside-bigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;<a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on LinkedIn: <a href=\"https:\/\/www.linkedin.com\/company\/insidebigdata\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on Facebook: <a href=\"https:\/\/www.facebook.com\/insideBIGDATANOW\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>NVIDIA today announced two new large language model cloud AI services \u2014 the NVIDIA  NeMo Large Language Model Service and the NVIDIA BioNeMo LLM Service \u2014 that  enable developers to easily adapt LLMs and deploy customized AI applications for  content generation, text summarization, chatbots, code development, as well as protein  structure and biomolecular property predictions, and more.<\/p>\n","protected":false},"author":10513,"featured_media":17071,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,66,180,122,268,56,1],"tags":[117,948,263,96],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NVIDIA Launches Large Language Model Cloud Services - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/09\/20\/nvidia-launches-large-language-model-cloud-services\/\" \/>\n<meta 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The integration will create a secure, enterprise-ready platform that enables Azure customers worldwide to quickly build, deploy and manage customized applications using the more than 100\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":31899,"url":"https:\/\/insidebigdata.com\/2023\/03\/22\/nvidia-launches-inference-platforms-for-large-language-models-and-generative-ai-workloads\/","url_meta":{"origin":30414,"position":5},"title":"NVIDIA Launches Inference Platforms for Large Language Models and Generative AI Workloads","date":"March 22, 2023","format":false,"excerpt":"NVIDIA launched four inference platforms optimized for a diverse set of rapidly emerging generative AI applications \u2014 helping developers quickly build specialized, AI-powered applications that can deliver new services and insights. The platforms combine NVIDIA\u2019s full stack of inference software with the latest NVIDIA Ada, NVIDIA Hopper\u2122 and NVIDIA Grace\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/30414"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/10513"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=30414"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/30414\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/17071"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=30414"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=30414"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=30414"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}