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govindhtech · 20 days

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Utilize Power of Nvidia BioNeMo to Promote Drug Discovery

Nvidia BioNeMo Models

With the integration of NVIDIA NIM, a set of cloud-native microservices, with Amazon Web Services, utilising optimised AI models for healthcare is now simpler than ever.

Through industry-standard application programming interfaces, or APIs, NIM, a component of the NVIDIA AI Enterprise software platform offered on the AWS Marketplace, gives developers access to an expanding library of AI models. With enterprise-grade security and support, the library offers foundation models for drug discovery, medical imaging, and genomics.

NIM may now be accessed through AWS ParallelCluster, an open-source platform for managing and deploying high performance computing clusters on AWS, and Amazon SageMaker, a fully managed service for preparing data and building, training, and deploying machine learning models. Another tool for orchestrating NIMs is AWS HealthOmics, a service designed specifically for biological data processing.

The hundreds of healthcare and life sciences businesses that currently use AWS will be able to implement generative AI more quickly thanks to easy access to NIM, eliminating the hassles associated with model building and production packaging. Additionally, it will assist developers in creating workflows that integrate AI models with data from many modalities, including MRI scans, amino acid sequences, and plain-text patient health records.

This initiative, which was presented today at the AWS Life Sciences Leader Symposium in Boston, expands the range of NVIDIA Clara accelerated healthcare software and services that are available on AWS. These services include NVIDIA BioNeMo‘s quick and simple-to-deploy NIMs for drug discovery, NVIDIA MONAI for medical imaging workflows, and NVIDIA Parabricks for accelerated genomics.

Pharmaceutical and Biotech Businesses Use NVIDIA AI on Amazon

Nvidia BioNeMo is a generative AI platform that supports the training and optimisation of biology and chemistry models on private data. It consists of foundation models, training frameworks, domain-specific data loaders, and optimised training recipes. Over a hundred organisations utilise it worldwide.

One of the top biotechnology firms in the world, Amgen, has trained generative models for protein design using the Nvidia BioNeMo framework and is investigating the possibility of integrating Nvidia BioNeMo with AWS.

The Nvidia BioNeMo models for molecular docking, generative chemistry, and protein structure prediction are pretrained and optimised to run on any NVIDIA GPU or cluster of GPUs. They are available as NIM microservices. Combining these models can enable a comprehensive approach for AI-accelerated drug discovery.

A-Alpha Bio is a biotechnology business that uses artificial intelligence (AI) and synthetic biology to quantify, forecast, and design protein-to-protein interactions. Researchers witnessed a speedup of more than 10x as soon as they switched from a generic version of the ESM-2 protein language model to one that was optimised by NVIDIA and ran on NVIDIA H100 Tensor Core GPUs on AWS. As a result, the team is able to sample a far wider range of protein possibilities than they otherwise could have.

Using retrieval-augmented generation, or RAG, also referred to as a lab-in-the-loop architecture, NIM enables developers to improve a model for organisations who wish to supplement these models with their own experimental data.

Accelerated Genomics Pipelines Made Possible by Parabricks

NVIDIA Parabricks genomics models are included in NVIDIA NIM and can be accessed on AWS HealthOmics as Ready2Run workflows, which let users set up pre-made pipelines.

The life sciences company Agilent greatly increased the processing rates for variant calling workflows on its cloud-native Alissa Reporter software by utilising Parabricks genomics analysis tools running on NVIDIA GPU-powered Amazon Elastic Compute Cloud (EC2) instances. Researchers can get quick data analysis in a secure cloud environment by integrating Parabricks with Alissa secondary analysis workflows.

Artificial Conversational Intelligence Promotes Digital Health

NIM microservices provide optimised big language models for conversational AI and visual generative AI models for avatars and digital humans, in addition to models that can read proteins and genetic sequences.

By providing logistical support to clinicians and responding to patient inquiries, AI-powered digital assistants can improve healthcare. After receiving training on RAG-specific data from healthcare organisations, they were able to link to pertinent internal data sources to aggregate research, reveal patterns, and boost efficiency.

startup using generative AI AI-powered healthcare agents that concentrate on a variety of tasks like wellness coaching, preoperative outreach, and post-discharge follow-up are now being tested by Hippocratic AI.

The company is implementing Nvidia BioNeMo Models and NVIDIA ACE microservices to power a generative AI agent for digital health. The company employs NVIDIA GPUs through AWS. The team powered the discussion of an avatar healthcare assistant with NVIDIA Audio2Face facial animation technology, NVIDIA Riva automated voice recognition, text-to-speech capabilities, and more.

NVIDIA created a collection of tools called Nvidia BioNeMo models especially for use in life sciences research, including drug development. They are constructed around the Nemo Megatron framework from NVIDIA, which is a toolkit for creating and honing massive language models.

Features of Nvidia BioNeMo Models

Pre-conditioned AI models

Large volumes of biological data have already been used to train these models. Then, these models can be applied to a range of activities, including determining possible drug targets, assessing the impact of mutations, and forecasting protein function. Pre-trained Nvidia BioNeMo models include, for instance-

DNABERT:

This model is useful for analysing and forecasting the function of DNA sequences.

ScBERT:

This model can be used to identify distinct cell types and forecast the consequences of gene knockouts because it was developed using single-cell RNA sequencing data.

EquiDock:

The 3D structure of protein interactions may be predicted using this approach, which is useful for finding possible therapeutic options.

BioNeMo Service

Researchers can simply access and utilise Nvidia BioNeMo‘s pre-trained models through a web interface by using the BioNeMo Service, a cloud-based solution . For researchers without access to the computational power needed to train their own models, this service can be especially helpful.

All things considered, Nvidia BioNeMo models are an effective instrument that can be utilised to quicken medication discovery research. These models help researchers find novel drug targets and create new treatments more swiftly and effectively.

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#bionemo #nvidianim #microservices #amazonwebservices #nvidia #aimodels #drugdiscovery #nvidiamonai #NvidiaGPU #awshealthomics #nimmicroservices #news #technews #technology #technologynews #technologytrends #govindhtech

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moremedtech · 6 months

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NVIDIA BioNeMo Enables Generative AI for Drug Discovery on AWS

NVIDIA BioNeMo Enables Generative AI for Drug Discovery on AWS. Pharma and techbio companies can access the NVIDIA Clara healthcare suite, including BioNeMo, now via Amazon SageMaker and AWS ParallelCluster, and the NVIDIA DGX Cloud on AWS. New to AWS: NVIDIA BioNeMo Advances Generative AI for Drug Discovery Also Available on AWS: NVIDIA Clara for Medical Imaging and Genomics November 28, 2023 - Leading pharmaceutical and biotech companies' researchers and developers can now easily deploy NVIDIA Clara software and services for accelerated healthcare via Amazon Web Services. The initiative, announced today at AWS re:Invent, allows healthcare and life sciences developers who use AWS cloud resources to integrate NVIDIA-accelerated offerings such as NVIDIA BioNeMo—a generative AI platform for drug discovery—which is coming to NVIDIA DGX Cloud on AWS and is currently available via the AWS ParallelCluster cluster management tool for high-performance computing and the Amazon SageMaker machine learning service. AWS is used by thousands of healthcare and life sciences companies worldwide. They can now use BioNeMo to build or customize digital biology foundation models with proprietary data, scaling up model training and deployment on AWS using NVIDIA GPU-accelerated cloud servers. Alchemab Therapeutics, Basecamp Research, Character Biosciences, Evozyne, Etcembly, and LabGenius are among the AWS users who have already started using BioNeMo for generative AI-accelerated drug discovery and development. This collaboration provides them with additional options for rapidly scaling up cloud computing resources for developing generative AI models trained on biomolecular data. This announcement extends NVIDIA’s existing healthcare-focused offerings available on AWS — NVIDIA MONAI for medical imaging workflows and NVIDIA Parabricks for accelerated genomics.

New to AWS: NVIDIA BioNeMo Advances Generative AI for Drug Discovery

BioNeMo is a domain-specific framework for digital biology generative AI, including pretrained large language models (LLMs), data loaders, and optimized training recipes that can help advance computer-aided drug discovery by speeding target identification, protein structure prediction, and drug candidate screening. Drug discovery teams can use their proprietary data to build or optimize models with BioNeMo and run them on cloud-based high-performance computing clusters. One of these models, ESM-2, a powerful LLM that supports protein structure prediction, achieves almost linear scaling on 256 NVIDIA H100 Tensor Core GPUs. Researchers can scale to 512 H100 GPUs to complete training in a few days instead of a month, the training time published in the original paper. Developers can train ESM-2 at scale using checkpoints of 650 million or 3 billion parameters. Additional AI models supported in the BioNeMo training framework include small-molecule generative model MegaMolBART and protein sequence generation model ProtT5. BioNeMo’s pretrained models and optimized training recipes — which are available using self-managed services like AWS ParallelCluster and Amazon ECS as well as integrated, managed services through NVIDIA DGX Cloud and Amazon SageMaker — can help R&D teams build foundation models that can explore more drug candidates, optimize wet lab experimentation and find promising clinical candidates faster

Also Available on AWS: NVIDIA Clara for Medical Imaging and Genomics

Project MONAI, cofounded and enterprise-supported by NVIDIA to support medical imaging workflows, has been downloaded more than 1.8 million times and is available for deployment on AWS. Developers can harness their proprietary healthcare datasets already stored on AWS cloud resources to rapidly annotate and build AI models for medical imaging. These models, trained on NVIDIA GPU-powered Amazon EC2 instances, can be used for interactive annotation and fine-tuning for segmentation, classification, registration, and detection tasks in medical imaging. Developers can also harness the MRI image synthesis models available in MONAI to augment training datasets. To accelerate genomics pipelines, Parabricks enables variant calling on a whole human genome in around 15 minutes, compared to a day on a CPU-only system. On AWS, developers can quickly scale up to process large amounts of genomic data across multiple GPU nodes. More than a dozen Parabricks workflows are available on AWS HealthOmics as Ready2Run workflows, which enable customers to easily run pre-built pipelines. Read the full article

#bionemo #generativeai #healthtech #imaging #medtech #medtechnews #nvidia #technology

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exeton · 4 months

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Amgen Embarks on Innovative AI-Driven Drug Research with Generative Models

Biotech Giant Leverages NVIDIA DGX SuperPOD for Groundbreaking Insights from Extensive Human Data

Amgen, a global biotechnology leader, is revolutionizing drug discovery with generative AI, utilizing the technology to enhance its research capabilities significantly.

The company is set to develop AI models for in-depth analysis of one of the world’s most comprehensive human datasets, utilizing the NVIDIA DGX SuperPOD. This advanced data center platform will be stationed at Amgen’s deCODE genetics’ headquarters in Reykjavik, Iceland, and has been christened ‘Freyja’ after the Norse goddess known for foresight.

Freyja’s primary role will be to create a detailed human diversity atlas, crucial for identifying drug targets and disease-specific biomarkers. This atlas will be pivotal in developing diagnostic tools for tracking disease progress and regression. Additionally, Freyja will aid in crafting AI-powered precision medicine models, potentially offering personalized treatment options for patients with serious illnesses.

Amgen’s integration of the DGX SuperPOD, equipped with 31 NVIDIA DGX H100 nodes and 248 H100 Tensor Core GPUs, aims to expedite the training of sophisticated AI models. This acceleration allows researchers to analyze and derive insights from data more effectively, transforming the landscape of health research and therapeutic discoveries.

David M. Reese, Amgen’s Executive Vice President and Chief Technology Officer, emphasizes the company’s preparedness for this pivotal industry shift, combining technological and biotechnological expertise. Amgen’s collaboration with NVIDIA’s technologies aims to harness the full potential of their extensive human data capabilities.

Kári Stefánsson, the founder and CEO of deCODE, envisions a future where genetics play a crucial role in diagnosing uncommon diseases. He anticipates that the SuperPOD will not only hasten research but also encourage innovative scientific inquiries.

Incorporating Advanced Tech in Biotech Since 1996, deCODE has compiled over 200 petabytes of anonymized human data from nearly 3 million individuals, beginning with the Icelandic population known for its rich genealogical history. This data offers unique perspectives on human diversity and disease correlation.

Further, deCODE has contributed to sequencing over half a million human genomes through the UK Biobank project, necessitating robust AI systems to manage and interpret this vast data.

Amgen’s adoption of NVIDIA BioNeMo and access to the NVIDIA DGX Cloud illustrates its commitment to employing cutting-edge AI in drug discovery and development. These models not only enhance drug efficacy but also mitigate undesired effects and facilitate large-scale biologic production.

The integration of DGX SuperPOD positions Amgen at the forefront of data-driven drug discovery, potentially revolutionizing the process and scope of medical advancements.

David M. Reese concludes, “The synergy of advanced AI, pioneering biology, molecular engineering, and extensive human data is transforming the way we discover and develop new medicines, essentially redefining the entire field of medicine.”

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

#Nvidia #Nvidia DGX H100 #exeton #technology #Data Science #artifical intelligence #DGX SuperPOD

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jhavelikes · 4 months

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At Recursion, we call this phenomics - the systematic study of a cell’s phenotype in response to many different chemical or genetic perturbations. It’s one of several layers of data that form the foundation of our maps of biology and chemistry and allow us to discover novel relationships that lead to new drug discovery programs. And now, after investing nearly a billion dollars to build the Recursion OS, we are pleased to release one important component of our work, a phenomics foundation model we call Phenom-Beta. It flexibly processes cellular microscopy images into general-purpose embeddings at any scale, from small projects to billions of images. In other words, Phenom-Beta can turn a series of image inputs into meaningful representations that are foundational to analyzing and understanding the underlying biology. We are putting some of the power of Recursion’s approach into a form accessible to the scientific community, subject to commercial limitations (please see the license details).

Nothing Short of Phenomenal: New Deep Learning Model Available on NVIDIA’s BioNeMo Platform

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jcmarchi · 6 months

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AWS and NVIDIA expand partnership to advance generative AI

New Post has been published on https://thedigitalinsider.com/aws-and-nvidia-expand-partnership-to-advance-generative-ai/

AWS and NVIDIA expand partnership to advance generative AI

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Amazon Web Services (AWS) and NVIDIA have announced a significant expansion of their strategic collaboration at AWS re:Invent. The collaboration aims to provide customers with state-of-the-art infrastructure, software, and services to fuel generative AI innovations.

The collaboration brings together the strengths of both companies, integrating NVIDIA’s latest multi-node systems with next-generation GPUs, CPUs, and AI software, along with AWS technologies such as Nitro System advanced virtualisation, Elastic Fabric Adapter (EFA) interconnect, and UltraCluster scalability.

Key highlights of the expanded collaboration include:

Introduction of NVIDIA GH200 Grace Hopper Superchips on AWS:

AWS becomes the first cloud provider to offer NVIDIA GH200 Grace Hopper Superchips with new multi-node NVLink technology.

The NVIDIA GH200 NVL32 multi-node platform enables joint customers to scale to thousands of GH200 Superchips, providing supercomputer-class performance.

Hosting NVIDIA DGX Cloud on AWS:

Collaboration to host NVIDIA DGX Cloud, an AI-training-as-a-service, on AWS, featuring GH200 NVL32 for accelerated training of generative AI and large language models.

Project Ceiba supercomputer:

Collaboration on Project Ceiba, aiming to design the world’s fastest GPU-powered AI supercomputer with 16,384 NVIDIA GH200 Superchips and processing capability of 65 exaflops.

Introduction of new Amazon EC2 instances:

AWS introduces three new Amazon EC2 instances, including P5e instances powered by NVIDIA H200 Tensor Core GPUs for large-scale generative AI and HPC workloads.

Software innovations:

NVIDIA introduces software on AWS, such as NeMo Retriever microservice for chatbots and summarisation tools, and BioNeMo to speed up drug discovery for pharmaceutical companies.

This collaboration signifies a joint commitment to advancing the field of generative AI, offering customers access to cutting-edge technologies and resources.

Internally, Amazon robotics and fulfilment teams already employ NVIDIA’s Omniverse platform to optimise warehouses in virtual environments first before real-world deployment.

The integration of NVIDIA and AWS technologies will accelerate the development, training, and inference of large language models and generative AI applications across various industries.

(Photo by ANIRUDH on Unsplash)

See also: Inflection-2 beats Google’s PaLM 2 across common benchmarks

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, amazon web services, artificial intelligence, aws, dgx cloud, generative ai, gh200, large language model, Nvidia, omniverse, project ceiba, re:invent

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goldiers1 · 1 year

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NVIDIA Fourth Quarter Results 2022 and Fiscal 2023

- Quarterly revenue of $6.05 billion, down 21% from a year ago - Fiscal-year revenue of $27.0 billion, flat from a year ago - Quarterly and annual return to shareholders of $1.15 billion and $10.44 billion, respectively Tech company NVIDIA, today reported revenue for the fourth quarter ended January 29, 2023, of $6.05 billion, down 21% from a year ago and up 2% from the previous quarter. GAAP earnings per diluted share for the quarter were $0.57, down 52% from a year ago and up 111% from the previous quarter. Non-GAAP earnings per diluted share were $0.88, down 33% from a year ago and up 52% from the previous quarter. For fiscal 2023, revenue was $26.97 billion, flat from a year ago. GAAP earnings per diluted share were $1.74, down 55% from a year ago. Non-GAAP earnings per diluted share were $3.34, down 25% from a year ago. Jensen Huang, founder and CEO of NVIDIA said, "AI is at an inflection point, setting up for broad adoption reaching into every industry." "From startups to major enterprises, we are seeing accelerated interest in the versatility and capabilities of generative AI." “We are set to help customers take advantage of breakthroughs in generative AI and large language models. Our new AI supercomputer, with H100 and its Transformer Engine and Quantum-2 networking fabric, is in full production. “Gaming is recovering from the post-pandemic downturn, with gamers enthusiastically embracing the new Ada architecture GPUs with AI neural rendering.” NVIDIA AI Cloud Service Offerings NVIDIA is partnering with leading cloud service providers to offer AI-as-a-service that provides enterprises access to NVIDIA’s world-leading AI platform. Customers will be able to engage each layer of NVIDIA AI – the AI supercomputer, acceleration libraries software or pretrained generative AI models – as a cloud service. Using their browser, they will be able to engage an NVIDIA DGX™ AI supercomputer through the NVIDIA DGX Cloud, which is already offered on Oracle Cloud Infrastructure, with Microsoft Azure, Google Cloud Platform and others expected soon. At the AI platform software layer, they will be able to access NVIDIA AI Enterprise for training and deploying large language models or other AI workloads. And at the AI-model-as-a-service layer, NVIDIA will offer its NeMo™ and BioNeMo™ customizable AI models to enterprise customers who want to build proprietary generative AI models and services for their businesses.

Return to Shareholders

During the fourth quarter of fiscal 2023, NVIDIA returned to shareholders $1.15 billion in share repurchases and cash dividends, bringing the return in the fiscal year to $10.44 billion. NVIDIA will pay its next quarterly cash dividend of $0.04 per share on March 29, 2023, to all shareholders of record on March 8, 2023. Q4 Fiscal 2023 Summary GAAP ($ in millions, except earnings per share) Q4 FY23 Q3 FY23 Q4 FY22 Q/Q Y/Y Revenue $6,051 $5,931 $7,643 Up 2% Down 21% Gross margin 63.3% 53.6% 65.4% Up 9.7 pts Down 2.1 pts Operating expenses $2,576 $2,576 $2,029 -- Up 27% Operating income $1,257 $601 $2,970 Up 109% Down 58% Net income $1,414 $680 $3,003 Up 108% Down 53% Diluted earnings per share $0.57 $0.27 $1.18 Up 111% Down 52% Non-GAAP ($ in millions, except earnings per share) Q4 FY23 Q3 FY23 Q4 FY22 Q/Q Y/Y Revenue $6,051 $5,931 $7,643 Up 2% Down 21% Gross margin 66.1% 56.1% 67.0% Up 10.0 pts Down 0.9 pts Operating expenses $1,775 $1,793 $1,447 Down 1% Up 23% Operating income $2,224 $1,536 $3,677 Up 45% Down 40% Net income $2,174 $1,456 $3,350 Up 49% Down 35% Diluted earnings per share $0.88 $0.58 $1.32 Up 52% Down 33% Fiscal 2023 Summary GAAP ($ in millions, except earnings per share) FY23 FY22 Y/Y Revenue $26,974 $26,914 -- Gross margin 56.9% 64.9% Down 8.0 pts Operating expenses $11,132 $7,434 Up 50% Operating income $4,224 $10,041 Down 58% Net income $4,368 $9,752 Down 55% Diluted earnings per share $1.74 $3.85 Down 55% Non-GAAP ($ in millions, except earnings per share) FY23 FY22 Y/Y Revenue $26,974 $26,914 -- Gross margin 59.2% 66.8% Down 7.6 pts Operating expenses $6,925 $5,279 Up 31% Operating income $9,040 $12,690 Down 29% Net income $8,366 $11,259 Down 26% Diluted earnings per share $3.34 $4.44 Down 25%

Outlook

NVIDIA’s outlook for the first quarter of fiscal 2024 is as follows: - Revenue is expected to be $6.50 billion, plus or minus 2%. - GAAP and non-GAAP gross margins are expected to be 64.1% and 66.5%, respectively, plus or minus 50 basis points. - GAAP and non-GAAP operating expenses are expected to be approximately $2.53 billion and $1.78 billion, respectively. - GAAP and non-GAAP other income and expense are expected to be an income of approximately $50 million, excluding gains and losses from non-affiliated investments. - GAAP and non-GAAP tax rates are expected to be 13.0%, plus or minus 1%, excluding any discrete items.

Highlights

NVIDIA achieved progress since its previous earnings announcement in these areas: Data Center - Fourth-quarter revenue was $3.62 billion, up 11% from a year ago and down 6% from the previous quarter. Fiscal-year revenue rose 41% to a record $15.01 billion. - Announced a partnership with Deutsche Bank to extend the use of AI in the financial-services sector. - Launched, together with Dell Technologies, 15 next-generation Dell PowerEdge systems available with NVIDIA® acceleration, enabling enterprises to use AI to efficiently transform their business. - Announced that NVIDIA A100 Tensor Core GPUs showed unrivaled throughput and top latency in the latest STAC-ML benchmarks for financial services. Gaming - Fourth-quarter revenue was $1.83 billion, down 46% from a year ago and up 16% from the previous quarter. Fiscal-year revenue was down 27% to $9.07 billion. - Unveiled the GeForce RTX™ 40 Series for laptops, providing the company’s largest-ever generational leap in performance and power efficiency. - Launched the GeForce RTX 4070 Ti, which is faster than the GeForce RTX 3090 Ti, featuring NVIDIA Ada Lovelace architecture and NVIDIA DLSS 3 technology. - Announced that DLSS 3 is available on, or coming soon to, more than 50 games and apps — including Cyberpunk 2077, Portal with RTX and Marvel’s Spider-Man: Miles Morales. - Launched the GeForce NOW™ Ultimate membership tier, delivering GeForce RTX 4080-class performance with NVIDIA Reflex, full ray tracing and DLSS 3. - Signed a 10-year agreement with Microsoft to bring the Xbox PC game lineup, including Minecraft, Halo and Flight Simulator, to GeForce NOW. Following the close of Microsoft’s Activision acquisition, GeForce NOW will add titles like Call of Duty and Overwatch. Professional Visualization - Fourth-quarter revenue was $226 million, down 65% from a year ago and up 13% from the previous quarter. Fiscal-year revenue was down 27% to $1.54 billion. - Enhanced NVIDIA Omniverse™ Enterprise’s capabilities to help teams build connected 3D pipelines and develop large-scale 3D works through increased performance, generational leaps in real-time RTX ray and path tracing, and streamlined workflows. - Announced a collaboration with Lockheed Martin to build a digital twin of global weather conditions, enabling the U.S. National Oceanic and Atmospheric Administration to better monitor global environmental conditions, including extreme weather events. - Shared news that Mercedes-Benz is taking the next step to digitalize its production process, using NVIDIA Omniverse to design and plan manufacturing and assembly facilities. Automotive and Embedded - Fourth-quarter revenue was a record $294 million, up 135% from a year ago and up 17% from the previous quarter. Fiscal-year revenue rose 60% to a record $903 million. - Announced a strategic partnership with Foxconn to develop automated and autonomous vehicle platforms based on NVIDIA DRIVE Orin™ and DRIVE Hyperion™. - Released major updates to the NVIDIA Isaac Sim™ robotics simulation tool, including AI capabilities and cloud access, enabling the building and testing of virtual robots in realistic environments. CFO Commentary Commentary on the quarter by Colette Kress, NVIDIA’s executive vice president and chief financial officer, is available at https://investor.nvidia.com/. Conference Call and Webcast Information NVIDIA will conduct a conference call with analysts and investors to discuss its fourth quarter and fiscal 2023 financial results and current financial prospects today at 2 p.m. Pacific time (5 p.m. Eastern time). A live webcast (listen-only mode) of the conference call will be accessible at NVIDIA’s investor relations website, https://investor.nvidia.com. The webcast will be recorded and available for replay until NVIDIA’s conference call to discuss its financial results for its first quarter of fiscal 2024. Non-GAAP Measures supplement NVIDIA’s condensed consolidated financial statements presented in accordance with GAAP, the company uses non-GAAP measures of certain components of financial performance. These non-GAAP measures include non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses, non-GAAP income from operations, non-GAAP other income (expense), net, non-GAAP net income, non-GAAP net income, or earnings, per diluted share, and free cash flow. For NVIDIA’s investors to be better able to compare its current results with those of previous periods, the company has shown a reconciliation of GAAP to non-GAAP financial measures. These reconciliations adjust the related GAAP financial measures to exclude acquisition termination costs, stock-based compensation expense, acquisition-related and other costs, contributions, IP-related costs, legal settlement costs, restructuring costs and other, gains and losses from non-affiliated investments, interest expense related to amortization of debt discount, the associated tax impact of these items where applicable, foreign tax benefit and domestication tax adjustments. Free cash flow is calculated as GAAP net cash provided by operating activities less both purchases of property and equipment and intangible assets and principal payments on property and equipment and intangible assets. NVIDIA believes the presentation of its non-GAAP financial measures enhances the user’s overall understanding of the company’s historical financial performance. The presentation of the company’s non-GAAP financial measures is not meant to be considered in isolation or as a substitute for the company’s financial results prepared in accordance with GAAP, and the company’s non-GAAP measures may be different from non-GAAP measures used by other companies. NVIDIA CORPORATION CONDENSED CONSOLIDATED STATEMENTS OF INCOME (In millions, except per share data) (Unaudited) Three Months Ended Twelve Months Ended January 29, January 30, January 29, January 30, 2023 2022 2023 2022 Revenue $ 6,051 $ 7,643 $ 26,974 $ 26,914 Cost of revenue 2,218 2,644 11,618 9,439 Gross profit 3,833 4,999 15,356 17,475 Operating expenses Research and development 1,951 1,466 7,339 5,268 Sales, general and administrative 625 563 2,440 2,166 Acquisition termination cost - - 1,353 - Total operating expenses 2,576 2,029 11,132 7,434 Income from operations 1,257 2,970 4,224 10,041 Interest income 115 9 267 29 Interest expense (65 ) (61 ) (262 ) (236 ) Other, net (18 ) (53 ) (48 ) 107 Other income (expense), net 32 (105 ) (43 ) (100 ) Income before income tax 1,289 2,865 4,181 9,941 Income tax expense (benefit) (125 ) (138 ) (187 ) 189 Net income $ 1,414 $ 3,003 $ 4,368 $ 9,752 Net income per share: Basic $ 0.57 $ 1.20 $ 1.76 $ 3.91 Diluted $ 0.57 $ 1.18 $ 1.74 $ 3.85 Weighted average shares used in per share computation: Basic 2,464 2,504 2,487 2,496 Diluted 2,477 2,545 2,507 2,535 NVIDIA CORPORATION CONDENSED CONSOLIDATED BALANCE SHEETS (In millions) (Unaudited) Read the full article

#NVIDIA #NVIDIACorporation #NVIDIAFourthQuarterResults2022

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loudmouthrep · 2 years

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Nvidia launches new services for training large language models

As interest around large AI models — particularly large language models (LLMs) like OpenAI’s GPT-3 — grows, Nvidia is looking to cash in with new fully managed, cloud-powered services geared toward enterprise software developers. Today at the company’s fall 2022 GTC conference, Nvidia announced the NeMo LLM Service and BioNeMo LLM Service, which ostensibly make […] Nvidia launches new services for training large language models by Kyle Wiggers originally published on TechCrunch http://dlvr.it/SYgt0D

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isfeed · 2 years

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Nvidia launches new services for training large language models

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govindhtech · 6 months

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AI Opening: NVIDIA BioNeMo Improves AWS Drug Discovery

Using Amazon Web Services, researchers and engineers at top pharmaceutical and techbio firms may now quickly and simply use NVIDIA Clara software and services for faster healthcare.

The program, which was unveiled at AWS re:Invent today, allows developers working in the healthcare and life sciences industry to integrate NVIDIA-accelerated products like NVIDIA BioNeMo, a generative AI platform for drug discovery that will soon be available on NVIDIA DGX Cloud on AWS. It is also currently accessible through the AWS ParallelCluster cluster management tool for high performance computing and the Amazon SageMaker machine learning service.

AWS is used by thousands of healthcare and life sciences businesses worldwide. With proprietary data, they will now have access to BioNeMo, enabling them to construct or modify foundation models for digital biology. Model training and deployment will be sped by the use of NVIDIA GPU-accelerated cloud servers on AWS.

Techbio innovators employing BioNeMo for generative AI-accelerated drug discovery and development include LabGenius, Alchemab Therapeutics, Basecamp Research, Character Biosciences, Evozyne, Etcembly, and AWS customers. They now have additional options to quickly scale up cloud computing resources for creating generative AI models that have been trained on biomolecular data thanks to this collaboration.

With this release, NVIDIA expands its portfolio of healthcare-oriented products on AWS, which includes NVIDIA Parabricks for accelerated genomics and NVIDIA MONAI for medical imaging processes.

NVIDIA BioNeMo: Introducing AWS and Advancing Generative AI for Drug Discovery

BioNeMo is a domain-specific framework for generative AI in digital biology that includes data loaders, pretrained large language models (LLMs), and optimized training recipes that can accelerate target identification, protein structure prediction, and drug candidate screening in computer-aided drug discovery.

Teams working on drug development can utilize BioNeMo to build or optimize models using their proprietary data, which can then be performed on cloud-based high performance computing clusters.

Using 256 NVIDIA H100 Tensor Core GPUs, one of these models the potent LLM ESM-2 achieves nearly linear scalability for protein structure prediction. Instead of taking a month to complete training, as stated in the original report, researchers may scale to 512 H100 GPUs and finish in a few days.

ESM-2 may be trained at scale by developers with checkpoints of 3 billion or 650 million parameters. The BioNeMo training framework supports other AI models, such as the protein sequence generation model ProtT5 and the small-molecule generative model MegaMolBART.

Using self-managed services like AWS ParallelCluster and Amazon ECS as well as integrated, managed services like NVIDIA DGX Cloud and Amazon SageMaker, BioNeMo’s pretrained models and optimized training recipes can help R&D teams build foundation models that can explore more drug candidates, optimize wet lab experimentation, and find promising clinical candidates more quickly.

NVIDIA Clara for Medical Imaging and Genomics is also accessible on AWS

With over 1.8 million downloads, Project MONAI, which NVIDIA cofounded and is enterprise-supported to support medical imaging workflows, may be deployed on AWS. Using their own healthcare datasets that are currently saved on AWS cloud services, developers may quickly annotate and construct AI models for medical imaging.

These models can be used for interactive annotation and fine-tuning for medical imaging segmentation, classification, registration, and detection tasks. They were trained on NVIDIA GPU-powered Amazon EC2 instances. Additionally, MRI image synthesis models included in MONAI can be used by developers to enhance training datasets.

In order to speed up genomics workflows, variant calling on the entire human genome can be accomplished with Parabricks in about 15 minutes as opposed to a day on a CPU-only system. Developers can easily scale up to handle massive volumes of genomic data over numerous GPU nodes on AWS.

AWS HealthOmics offers over twelve Parabricks workflows as Ready2Run workflows, allowing users to quickly run pre-configured pipelines.

Start accelerating AI workflows for drug development, genomics, and medical imaging with NVIDIA Clara on AWS.