#nvidianim

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Dell Native Edge: Key to Unlock AI Innovation at the Edge

Dell Native Edge

The strategic value of edge is more important than ever in a time when artificial intelligence is having a profound impact on society. Dell Technologies is pleased to announce partnerships with Microsoft, NVIDIA, and Service Now that will improve enterprises use of edge AI. A new version of Dell Native Edge, dell’s edge operations software platform, is also being released. It provides a complete, virtualized end-to-end solution at the edge, making it easier to design, launch, and scale AI applications there.

Enhanced Dell Native Edge Partnerships to Streamline AI Application Implementation at the Periphery

Dell is growing dell’s strategic alliance with NVIDIA to enable and accelerate AI applications everywhere that customers need them, as part of the most recent announcements about Dell AI Factory with NVIDIA. Since NVIDIA AI Enterprise is an end-to-end software platform that includes NVIDIA NIM and other microservices for the creation and deployment of production-grade applications, Dell Native Edge is the first edge orchestration platform that automates its delivery.

This partnership integrates NVIDIA AI tools and SDKs with NativeEdge, Dell’s edge operations software platform, for NVIDIA AI Enterprise customers. They’ve got you covered with new deployment blueprints that automate the distribution of NVIDIA AI frameworks to edge devices and beyond, whether it’s video analytics with NVIDIA Metropolis, voice and translation with NVIDIA Riva, or optimised inferencing at the edge with NVIDIA NIM.

Powered by NVIDIA-accelerated computation, this NativeEdge technology makes it simple for developers and IT operators to create and implement AI solutions automatically at the edge on NativeEdge Endpoints. They’re only getting started, but this innovation covers a broad spectrum of use cases, including visual analytics, industrial automation, and personalised retail experiences.

The first closed-loop integration for the Edge in the industry is created by Dell Native Edge and ServiceNow

By integrating with ServiceNow‘s Now Platform, Dell Native Edge expands on dell’s partner ecosystem and streamlines the development and deployment of AI applications at the edge. With the help of this integration, companies can effectively expand their IT operations from the main data centre to the edge. It also provides an automated edge management solution that can be used for operations starting on Day 1 and continuing until Day 2 plus. Dell’s alliance will streamline the orchestration, management, and workflow of edge computing resources through closed-loop automation, resulting in more secure, agile, and efficient operations and service models for edge workloads, including artificial intelligence, in many industries.

Using Dell Native Edge with Azure Arc to Accelerate Edge Innovation

With the release of Dell Native Edge’s Azure Arc enabling automation, the edge’s pace is further accelerated. The goal of this new solution is to strengthen edge security and optimise the Azure user experience. The integration’s main goals are to improve edge AI capabilities, streamline edge operations, and offer all-encompassing security using Zero Trust security principles. Customers may easily incorporate Azure services into their environments thanks to NativeEdge’s automation of Azure Arc enablement, which pushes Azure’s cloud advantages such as automation and quick deployment to the limit. Lastly, NativeEdge leverages Azure Arc to streamline customer experience through the creation of carefully crafted deployment blueprints and the deployment of Azure IoT Operations on Kubernetes, for example, simplifying the client experience.

Sustaining Growth in the Sector ISV Ecosystem

Dell have launched six new NativeEdge solutions for ISVs servicing the manufacturing, retail, and digital city industries in an effort to improve business outcomes and edge use cases. Specifically, by offering a 360-degree operational view for city planning, a new Unified Operations Centre with Aveva solution improves data management and citizen services. Using Astrikos AI, Dell Native Edge, and Aveva’s experience, it integrates multiple city systems and provides a safe, data-driven method for optimising infrastructure and urban management.

Enhancements to Dell Native Edge Boost Security, Scalability, and Performance of Edge Applications

Dell are pleased to announce the availability of a new version of Dell Native Edge that offers improved speed, scalability, and security through application deployment on bare-metal containers. Dell is providing new tools (such a Visual Studio Code plugin) to assist clients in developing application integrations and are introducing REST APIs for Dell Native Edge to integrate into DevOps workflows. Dell is now selling both NativeEdge software and NativeEdge Endpoints as a single monthly subscription with Dell APEX, offering an OpEx model for consumption, in response to client demand.

Furthermore, Dell has increased the support for edge infrastructure with NativeEdge Endpoints, which include additional PowerEdge servers, such as the recently released PowerEdge T160 Server, and Dell Precision workstations. Not to be overlooked is the new PowerEdge T160. It is perfect for small places like retail businesses because it is only 17L, 42% smaller than its predecessor2.

Introducing New Edge Services: Encouraging Edge Transformation

In conclusion, Dell is introducing two new Edge Services: Infrastructure and Application Design Services for Edge and ProConsult Advisory Services for Edge. Experts at Dell Services will evaluate your existing situation, develop an edge plan to get you there, and design an edge environment that will optimise productivity, ROI, and efficiency.

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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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NVIDIA Launches Generative AI Microservices for developers

In order to enable companies to develop and implement unique applications on their own platforms while maintaining complete ownership and control over their intellectual property, NVIDIA released hundreds of enterprise-grade generative AI microservices.

The portfolio of cloud-native microservices, which is built on top of the NVIDIA CUDA platform, includes NVIDIA NIM microservices for efficient inference on over two dozen well-known AI models from NVIDIA and its partner ecosystem. Additionally, NVIDIA CUDA-X microservices for guardrails, data processing, HPC, retrieval-augmented generation (RAG), and other applications are now accessible as NVIDIA accelerated software development kits, libraries, and tools. Additionally, approximately two dozen healthcare NIM and CUDA-X microservices were independently revealed by NVIDIA.

NVIDIA’s full-stack computing platform gains a new dimension with the carefully chosen microservices option. With a standardized method to execute bespoke AI models designed for NVIDIA’s CUDA installed base of hundreds of millions of GPUs spanning clouds, data centers, workstations, and PCs, this layer unites the AI ecosystem of model creators, platform providers, and organizations.

Prominent suppliers of application, data, and cybersecurity platforms, such as Adobe, Cadence, CrowdStrike, Getty Images, SAP, ServiceNow, and Shutterstock, were among the first to use the new NVIDIA generative AI microservices offered in NVIDIA AI Enterprise 5.0.

Jensen Huang, NVIDIA founder and CEO, said corporate systems have a treasure of data that can be turned into generative AI copilots. These containerized AI microservices, created with their partner ecosystem, enable firms in any sector to become AI companies.

Microservices for NIM Inference Accelerate Deployments From Weeks to Minutes

NIM microservices allow developers to cut down on deployment timeframes from weeks to minutes by offering pre-built containers that are driven by NVIDIA inference tools, such as TensorRT-LLM and Triton Inference Server.

For fields like language, voice, and medication discovery, they provide industry-standard APIs that let developers easily create AI apps utilizing their private data, which is safely stored in their own infrastructure. With the flexibility and speed to run generative AI in production on NVIDIA-accelerated computing systems, these applications can expand on demand.

For deploying models from NVIDIA, A121, Adept, Cohere, Getty Images, and Shutterstock as well as open models from Google, Hugging Face, Meta, Microsoft, Mistral AI, and Stability AI, NIM microservices provide the quickest and most efficient production AI container.

Today, ServiceNow revealed that it is using NIM to create and implement new generative AI applications, such as domain-specific copilots, more quickly and affordably.

Consumers will be able to link NIM microservices with well-known AI frameworks like Deepset, LangChain, and LlamaIndex, and access them via Amazon SageMaker, Google Kubernetes Engine, and Microsoft Azure AI.

Guardrails, HPC, Data Processing, RAG, and CUDA-X Microservices

To accelerate production AI development across sectors, CUDA-X microservices provide end-to-end building pieces for data preparation, customisation, and training.

Businesses may utilize CUDA-X microservices, such as NVIDIA Earth-2 for high resolution weather and climate simulations, NVIDIA cuOpt for route optimization, and NVIDIA Riva for configurable speech and translation AI, to speed up the adoption of AI.

NeMo Retriever microservices enable developers to create highly accurate, contextually relevant replies by connecting their AI apps to their business data, which includes text, photos, and visualizations like pie charts, bar graphs, and line plots. Businesses may improve accuracy and insight by providing copilots, chatbots, and generative AI productivity tools with more data thanks to these RAG capabilities.

Nvidia nemo

There will soon be more NVIDIA NeMo microservices available for the creation of bespoke models. These include NVIDIA NeMo Evaluator, which analyzes AI model performance, NVIDIA NeMo Guardrails for LLMs, NVIDIA NeMo Customizer, which builds clean datasets for training and retrieval, and NVIDIA NeMo Evaluator.

Ecosystem Uses Generative AI Microservices To Boost Enterprise Platforms

Leading application suppliers are collaborating with NVIDIA microservices to provide generative AI to businesses, as are data, compute, and infrastructure platform vendors from around the NVIDIA ecosystem.

NVIDIA microservices is collaborating with leading data platform providers including Box, Cloudera, Cohesity, Datastax, Dropbox, and NetApp to assist users in streamlining their RAG pipelines and incorporating their unique data into generative AI applications. NeMo Retriever is a tool that Snowflake uses to collect corporate data in order to create AI applications.

Businesses may use the NVIDIA microservices that come with NVIDIA AI Enterprise 5.0 on any kind of infrastructure, including popular clouds like Google Cloud, Amazon Web Services (AWS), Azure, and Oracle Cloud Infrastructure.

More than 400 NVIDIA-Certified Systems, including as workstations and servers from Cisco, Dell Technologies, HP, Lenovo, and Supermicro, are also capable of supporting NVIDIA microservices. HPE also announced today that their enterprise computing solution for generative AI is now available. NIM and NVIDIA AI Foundation models will be integrated into HPE’s AI software.

VMware Private AI Foundation and other infrastructure software platforms will soon support NVIDIA AI Enterprise microservices. In order to make it easier for businesses to incorporate generative AI capabilities into their applications while maintaining optimal security, compliance, and control capabilities, Red Hat OpenShift supports NVIDIA NIM microservices. With NVIDIA AI Enterprise, Canonical is extending Charmed Kubernetes support for NVIDIA microservices.

Through NVIDIA AI Enterprise, the hundreds of AI and MLOps partners that make up NVIDIA’s ecosystem such as Abridge, Anyscale, Dataiku, DataRobot, Glean, H2O.ai, Securiti AI, Scale.ai, OctoAI, and Weights & Biases are extending support for NVIDIA microservices.

Vector search providers like as Apache Lucene, Datastax, Faiss, Kinetica, Milvus, Redis, and Weaviate are collaborating with NVIDIA NeMo Retriever microservices to provide responsive RAG capabilities for businesses.

Accessible

NVIDIA microservices are available for free experimentation by developers at ai.nvidia.com. Businesses may use NVIDIA AI Enterprise 5.0 on NVIDIA-Certified Systems and top cloud platforms to deploy production-grade NIM microservices.

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NVIDIA NIM: Scalable AI Inference Improved Microservices

Nvidia Nim Deployment

The usage of generative AI has increased dramatically. The 2022 debut of OpenAI’s ChatGPT led to over 100M users in months and a boom in development across practically every sector.

POCs using open-source community models and APIs from Meta, Mistral, Stability, and other sources were started by developers by 2023.

As 2024 approaches, companies are turning their attention to full-scale production deployments, which include, among other things, logging, monitoring, and security, as well as integrating AI models with the corporate infrastructure already in place. This manufacturing route is difficult and time-consuming; it calls for specific knowledge, tools, and procedures, particularly when operating on a large scale.

What is Nvidia Nim?

Industry-standard APIs, domain-specific code, efficient inference engines, and enterprise runtime are all included in NVIDIA NIM, a containerized inference microservice.

A simplified approach to creating AI-powered workplace apps and implementing AI models in real-world settings is offered by NVIDIA NIM, a component of NVIDIA AI workplace.

NIM is a collection of cloud-native microservices that have been developed with the goal of reducing time-to-market and streamlining the deployment of generative AI models on GPU-accelerated workstations, cloud environments, and data centers. By removing the complexity of creating AI models and packaging them for production using industry-standard APIs, it increases the number of developers.

NVIDIA NIM for AI inference optimization

With NVIDIA NIM, 10-100X more business application developers will be able to contribute to their organizations’ AI transformations by bridging the gap between the intricate realm of AI development and the operational requirements of corporate settings.Image Credit to NVIDIA

Figure: Industry-standard APIs, domain-specific code, efficient inference engines, and enterprise runtime are all included in NVIDIA NIM, a containerized inference microservice.

The following are a few of the main advantages of NIM.

Install somewhere

Model deployment across a range of infrastructures, including local workstations, cloud, and on-premises data centers, is made possible by NIM’s controllable and portable architecture. This covers workstations and PCs with NVIDIA RTX, NVIDIA Certified Systems, NVIDIA DGX, and NVIDIA DGX Cloud.

Various NVIDIA hardware platforms, cloud service providers, and Kubernetes distributions are subjected to rigorous validation and benchmarking processes for prebuilt containers and Helm charts packed with optimized models. This guarantees that enterprises can deploy their generative AI applications anywhere and retain complete control over their apps and the data they handle. It also provides support across all environments powered by NVIDIA.

Use industry-standard APIs while developing

It is easier to construct AI applications when developers can access AI models using APIs that follow industry standards for each domain. With as few as three lines of code, developers may update their AI apps quickly thanks to these APIs’ compatibility with the ecosystem’s normal deployment procedures. Rapid implementation and scalability of AI technologies inside corporate systems is made possible by their seamless integration and user-friendliness.

Use models specific to a domain

Through a number of important features, NVIDIA NIM also meets the demand for domain-specific solutions and optimum performance. It bundles specialized code and NVIDIA CUDA libraries relevant to a number of disciplines, including language, voice, video processing, healthcare, and more. With this method, apps are certain to be precise and pertinent to their particular use case.

Using inference engines that have been tuned

NIM provides the optimum latency and performance on accelerated infrastructure by using inference engines that are tuned for each model and hardware configuration. This enhances the end-user experience while lowering the cost of operating inference workloads as they grow. Developers may get even more precision and efficiency by aligning and optimizing models with private data sources that remain within their data center, in addition to providing improved community models.

Assistance with AI of an enterprise-level

NIM, a component of NVIDIA AI Enterprise, is constructed using an enterprise-grade base container that serves as a strong basis for corporate AI applications via feature branches, stringent validation, service-level agreements for enterprise support, and frequent CVE security upgrades. The extensive support network and optimization tools highlight NIM’s importance as a key component in implementing scalable, effective, and personalized AI systems in real-world settings.

Accelerated AI models that are prepared for use

NIM provides AI use cases across several domains with support for a large number of AI models, including community models, NVIDIA AI Foundation models, and bespoke models given by NVIDIA partners. Large language models (LLMs), vision language models (VLMs), voice, picture, video, 3D, drug discovery, medical imaging, and other models are included in this.

Using cloud APIs provided by NVIDIA and available via the NVIDIA API catalog, developers may test the most recent generative AI models. Alternatively, they may download NIM and use it to self-host the models. In this case, development time, complexity, and expense can be reduced by quickly deploying the models on-premises or on major cloud providers using Kubernetes.

By providing industry-standard APIs and bundling algorithmic, system, and runtime improvements, NIM microservices streamline the AI model deployment process. This makes it possible for developers to include NIM into their current infrastructure and apps without the need for complex customization or specialist knowledge.

Businesses may use NIM to optimize their AI infrastructure for optimal performance and cost-effectiveness without having to worry about containerization or the intricacies of developing AI models. NIM lowers hardware and operating costs while improving performance and scalability on top of accelerated AI infrastructure.

NVIDIA offers microservices for cross-domain model modification for companies wishing to customize models for corporate apps. NVIDIA NeMo allows for multimodal models, speech AI, and LLMs to be fine-tuned utilizing private data. With an expanding library of models for generative biology, chemistry, and molecular prediction, NVIDIA BioNeMo expedites the drug development process. With Edify models, NVIDIA Picasso speeds up creative operations. Customized generative AI models for the development of visual content may be implemented thanks to the training of these models using licensed libraries from producers of visual material.

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