#NVIDIANIMmicroservices

[govindhtech]

NVIDIA Earth-2 NIM Microservices Exposed For Faster Forecast

Faster Predictions: To Introduces NVIDIA Earth-2 NIM Microservices to Deliver Higher-Resolution Simulations 500x Faster. Weather technology firms can now create and implement AI models for snow, ice, and hail predictions with to new NVIDIA NIM microservices.

Two new NVIDIA NIM microservices that can 500x the speed of climate change modeling simulation results in NVIDIA Earth-2 were unveiled by NVIDIA today at SC24

NVIDIA Earth-2 NIM microservices

High-resolution, AI-enhanced, accelerated climate and weather models with interactive visualization.

Climate Digital Twin Cloud Platform

NVIDIA Earth-2 simulates and visualizes weather and climate predictions at a global scale with previously unheard-of speed and accuracy by combining the capabilities of artificial intelligence (AI), GPU acceleration, physical models, and computer graphics. The platform is made up of reference implementations and microservices for simulation, visualization, and artificial intelligence.

Users may employ AI-accelerated models to optimize and simulate real-world climate and weather outcomes with NVIDIA NIM microservices for Earth-2.

The Development Platform for Climate Science

GPU-Optimized and Accelerated Climate Simulation

To increase simulated days per day (SDPD), the Earth-2 development platform is tuned for GPU-accelerated numerical climate simulations at the km-scale.

Data Federation and Interactive Weather Visualization

Extremely large-scale, high-fidelity, interactive projections of global weather conditions are made possible by NVIDIA Omniverse. A data federation engine included into Omniverse Nucleus provides transparent data access across external databases and real-time feeds.

A digital twin platform called Earth-2 is used to model and visualize climate and weather phenomena. To help with forecasting extreme weather occurrences, the new NIM microservices give climate technology application developers cutting-edge generative AI-driven capabilities.

While maintaining data security, NVIDIA NIM microservices aid in the quick deployment of foundation models.

The frequency of extreme weather events is rising, which raises questions about readiness and safety for disasters as well as potential financial effects.

Nearly $62 billion in natural disaster insurance losses occurred in the first half of this year. Bloomberg estimates that is 70% greater than the 10-year average.

The CorrDiff NIM and FourCastNet NIM microservices are being made available by NVIDIA to assist weather technology firms in producing more accurate and high-resolution forecasts more rapidly. When compared to conventional systems, the NIM microservices also provide the highest energy efficiency.

New CorrDiff NIM Microservices for Higher-Resolution Modeling

Image Credit To NVIDIA

NVIDIA a generative AI model for super resolution at the kilometer scale is called CorrDiff. At GTC 2024, it demonstrated its potential to super-resolve typhoons over Taiwan. In order to produce weather patterns at a 12x better resolution, CorrDiff was trained using numerical simulations from the Weather Research and Forecasting (WRF) model.

Meteorologists and companies depend on high-resolution forecasts that can be shown within a few kilometers. In order to evaluate risk profiles, the insurance and reinsurance sectors depend on comprehensive meteorological data. However, it is frequently too expensive and time-consuming to be feasible to achieve this level of precision using conventional numerical weather forecast models like WRF or High-Resolution Rapid Refresh.

Compared to conventional high-resolution numerical weather prediction utilizing CPUs, the CorrDiff NIM microservice is 10,000 times more energy-efficient and 500 times quicker. Additionally, CorrDiff is currently functioning at a 300x greater scale. In addition to forecasting precipitation events, such as snow, ice, and hail, with visibility in kilometers, it is super-resolving, or enhancing the quality of lower-resolution photos or videos, for the whole United States.

Enabling Large Sets of Forecasts With New FourCastNet NIM Microservice

Image Credit To NVIDIA

High-resolution predictions are not necessary for all use cases. Larger forecast sets with coarser resolution are more advantageous for some applications. Due to computational limitations, state-of-the-art numerical models like as IFS and GFS can only provide 50 and 20 sets of predictions, respectively.

Global, medium-range coarse predictions are provided by the FourCastNet NIM microservice, which is now accessible. Providers may provide predictions over the following two weeks 5,000 times faster than with conventional numerical weather models by using the initial assimilated state from operational weather centers like the National Oceanic and Atmospheric Administration or the European Centre for Medium-Range Weather predictions.

By estimating hazards associated with extreme weather at a different scale, climate tech providers may now anticipate the chance of low-probability occurrences that are missed by present computational processes.

Read more on govindhtech.com

[govindhtech]

NVIDIA H200 NVL: A Versatile GPU For AI And HPC Workloads

Reaching New Horizons, Hopper Speeds Up AI and HPC Applications for Commonplace Enterprise Servers.

NVIDIA H200 NVL

The NVIDIA H200 NVL is ideal for lower-power, air-cooled corporate rack systems because it enables 1.7x faster large language model inference and 1.3x faster high-performance computation.

Since its inception, the NVIDIA Hopper architecture has transformed AI and HPC, helping academics, developers, and companies solve the world’s biggest issues.

The NVIDIA H200 NVL PCIe GPU, the latest Hopper, was unveiled at Supercomputing 2024. The H200 NVL is the best option for data center enterprises seeking air-cooled, lower-power enterprise rack designs with adaptable configurations that can accelerate any size AI or HPC application.

A recent survey found that almost 70% of corporate racks utilize air cooling and are 20kW or less. Data centers can now fit more processing power into less places with to PCIe GPUs, which offer granularity of node placement whether utilizing one, two, four, or eight GPUs. After that, businesses may choose the amount of GPUs that best fits their requirements by using their current racks.

Businesses may utilize H200 NVL to speed up AI and HPC applications while simultaneously increasing energy efficiency by using less electricity. With a 1.5x memory and 1.2x bandwidth boost over the NVIDIA H100 NVL, businesses may fine-tune LLMs in a matter of hours and achieve up to 1.7x quicker inference performance with the H200 NVL. Performance gains of up to 1.3x over H100 NVL and 2.5x over the NVIDIA Ampere architecture generation are achieved for HPC applications.

NVIDIA NVLink technology enhances the H200 NVL’s raw power. To address the demands of HPC, massive language model inference, and fine-tuning, the most recent iteration of NVLink offers GPU-to-GPU connection seven times quicker than fifth-generation PCIe.

With the help of strong software tools, businesses may accelerate applications from AI to HPC with the NVIDIA H200 NVL. NVIDIA AI Enterprise, a cloud-native software platform for the creation and implementation of production AI, is included with a five-year subscription. For the safe and dependable implementation of high-performance AI model inference, NVIDIA AI Enterprise comes with NVIDIA NIM microservices.

Companies Tapping Into Power of H200 NVL

NVIDIA offers businesses a full-stack platform for developing and implementing their AI and HPC applications with H200 NVL.

Numerous AI and HPC use cases across industries are having a big impact on customers. For example, federal science organizations are using seismic imaging, medical imaging to improve anomaly detection in healthcare, pattern recognition for manufacturing, trading algorithms for finance, and visual AI agents and chatbots for customer service.

NVIDIA accelerated computing is being used by Dropbox for its infrastructure and services.

According to Ali Zafar, VP of Infrastructure at Dropbox, “Dropbox handles large amounts of content, requiring advanced AI and machine learning capabilities.” “In order to continuously enhance it offerings and provide to clients with greater value, its are investigating H200 NVL.”

NVIDIA accelerated computing has been used by the University of New Mexico for a number of scholarly and research purposes.

“As a public research university, the dedication to AI enables the university to be on the forefront of scientific and technological advancements.” “A number of applications, such as data science projects, bioinformatics and genomics research, physics and astronomy simulations, climate modeling, and more, will be able to accelerate as the transition to H200 NVL.”

H200 NVL Available Across Ecosystem

It is anticipated that Dell Technologies, Hewlett Packard Enterprise, Lenovo, and Supermicro will provide a variety of configurations that enable H200 NVL.

Furthermore, platforms from Aivres, ASRock Rack, ASUS, GIGABYTE, Ingrasys, Inventec, MSI, Pegatron, QCT, Wistron, and Wiwynn will provide the H200 NVL.

The NVIDIA MGX modular architecture, upon which certain systems are built, allows computer manufacturers to rapidly and economically construct a wide range of data center infrastructure designs.

Starting in December, NVIDIA’s international systems partners will provide platforms with H200 NVL. In addition, NVIDIA is creating an Enterprise Reference Architecture for H200 NVL systems to supplement the offerings of top international partners.

In order to enable partners and customers to build and implement high-performance AI infrastructure based on H200 NVL at scale, the reference architecture will integrate NVIDIA’s design principles and experience. This offers comprehensive advice on the best server, cluster, and network setups along with full-stack hardware and software recommendations. The NVIDIA Spectrum-X Ethernet platform optimizes networking for optimal performance.

During SC24, which is being held at the Georgia World Congress Center until November 22, NVIDIA technology will be on display on the exhibition floor.

Read more on govindhtech.com

[govindhtech]

Rekor Uses NVIDIA AI Technology For Traffic Management

Rekor Uses NVIDIA Technology for Traffic Relief and Roadway Safety as Texas Takes in More Residents.

For Texas and Philadelphia highways, the company is using AI-driven analytics utilizing NVIDIA AI, Metropolis, and Jetson, which might lower fatalities and enhance quality of life.

Jobs, comedy clubs, music venues, barbecues, and more are all attracting visitors to Austin. Traffic congestion, however, are a major city blues that have come with this growth.

Due to the surge of new inhabitants moving to Austin, Rekor, which provides traffic management and public safety analytics, gets a direct view of the growing traffic. To assist alleviate the highway issues, Rekor collaborates with the Texas Department of Transportation, which is working on a $7 billion initiative to remedy this.

Based in Columbia, Maryland, Rekor has been using NVIDIA Jetson Xavier NX modules for edge AI and NVIDIA Metropolis for real-time video understanding in Texas, Florida, Philadelphia, Georgia, Nevada, Oklahoma, and many other U.S. locations, as well as Israel and other countries.

Metropolis is a vision AI application framework for creating smart infrastructure. Its development tools include the NVIDIA DeepStream SDK, TAO Toolkit, TensorRT, and NGC catalog pretrained models. The tiny, powerful, and energy-efficient NVIDIA Jetson accelerated computing platform is ideal for embedded and robotics applications.

Rekor’s initiatives in Texas and Philadelphia to use AI to improve road management are the most recent chapter in a long saga of traffic management and safety.

Reducing Rubbernecking, Pileups, Fatalities and Jams

Rekor Command and Rekor Discover are the two primary products that Rekor sells. Traffic control centers can quickly identify traffic incidents and areas of concern using Command, an AI-driven software. It provides real-time situational awareness and notifications to transportation authorities, enabling them to maintain safer and less congested municipal roads.

Utilizing Rekor’s edge technology, discover completely automates the collection of thorough vehicle and traffic data and offers strong traffic analytics that transform road data into quantifiable, trustworthy traffic information. Departments of transportation may better plan and carry out their next city-building projects by using Rekor Discover, which gives them a comprehensive picture of how cars travel on roads and the effect they have.

Command has been spread around Austin by the corporation to assist in problem detection, incident analysis, and real-time response to traffic activities.

Rekor Command receives a variety of data sources, including weather, linked vehicle information, traffic camera video, construction updates, and data from third parties. After that, it makes links and reveals abnormalities, such as a roadside incident, using AI. Traffic management centers receive the data in processes for evaluation, verification, and reaction.

As part of the NVIDIA AI Enterprise software platform, Rekor is embracing NVIDIA’s full-stack accelerated computing for roadway intelligence and investing heavily in NVIDIA AI and NVIDIA AI Blueprints, reference workflows for generative AI use cases constructed with NVIDIA NIM microservices. NVIDIA NIM is a collection of user-friendly inference microservices designed to speed up foundation model installations on any cloud or data center while maintaining data security.

Rekor is developing AI agents for municipal services, namely in areas like traffic control, public safety, and infrastructure optimization, leveraging the NVIDIA AI Blueprint for video search and summarization. In order to enable a variety of interactive visual AI agents that can extract complicated behaviors from vast amounts of live or recorded video, NVIDIA has revealed a new AI blueprint for video search and summarization.

Philadelphia Monitors Roads, EV Charger Needs, Pollution

The Philadelphia Industrial Development Corporation (PIDC), which oversees the Philadelphia Navy Yard, a famous tourist destination, has difficulties managing the roads and compiling information on new constructions. According to a $6 billion rehabilitation proposal, the Navy Yard property will bring thousands of inhabitants and 12,000 jobs with over 150 firms and 15,000 workers on 1,200 acres.

PIDC sought to raise awareness of how road closures and construction projects influence mobility and how to improve mobility during major events and projects. PIDC also sought to improve the Navy Yard’s capacity to measure the effects of speed-mitigating devices placed across dangerous sections of road and comprehend the number and flow of car carriers or other heavy vehicles.

In order to handle any fluctuations in traffic, Discover offered PIDC information about further infrastructure initiatives that must be implemented.

By knowing how many electric cars are coming into and going out of the Navy Yard, PIDC can make informed decisions about future locations for the installation of EV charging stations. Navy Yard can better plan possible locations for EV charge station deployment in the future by using Rekor Discover, which gathers data from Rekor’s edge systems which are constructed with NVIDIA Jetson Xavier NX modules for powerful edge processing and AI to understand the number of EVs and where they’re entering and departing.

By examining data supplied by the AI platform, Rekor Discover allowed PIDC planners to produce a hotspot map of EV traffic. The solution uses Jetson and NVIDIA’s DeepStream data pipeline for real-time traffic analysis. To further improve LLM capabilities, it makes advantage of NVIDIA Triton Inference Server.

The PIDC sought to reduce property damage and address public safety concerns about crashes and speeding. When average speeds are higher than what is recommended on certain road segments, traffic calming measures are being implemented using speed insights.

NVIDIA Jetson Xavier NX to Monitor Pollution in Real Time

Rekor’s vehicle identification models, which were powered by NVIDIA Jetson Xavier NX modules, were able to follow pollution to its origins, moving it one step closer to mitigation than the conventional method of using satellite data to attempt to comprehend its placements.

In the future, Rekor is investigating the potential applications of NVIDIA Omniverse for the creation of digital twins to model traffic reduction using various techniques. Omniverse is a platform for creating OpenUSD applications for generative physical AI and industrial digitization.

Creating digital twins for towns using Omniverse has significant ramifications for lowering traffic, pollution, and traffic fatalities all of which Rekor views as being very advantageous for its clients.

Read more on Govindhtech.com

[govindhtech]

NayaOne Digital Sandbox Supports Financial Services Growth

Leaders in Fintech Use Generative AI to Provide Faster, Safer, and More Accurate Financial Services.

Ntropy, Contextual AI, NayaOne, and Securiti improve financial planning, fraud detection, and other AI applications with NVIDIA NIM microservices and quicker processing. A staggering 91% of businesses in the financial services sector (FSI) are either evaluating artificial intelligence or currently using it as a tool to improve client experiences, increase operational efficiency, and spur innovation.

Generative AI powered by NVIDIA NIM microservices and quicker processing may improve risk management, fraud detection, portfolio optimization, and customer service.

Companies like Ntropy, Contextual AI, and NayaOne all part of the NVIDIA Inception program for innovative startups are using these technologies to improve financial services applications.

Additionally, NVIDIA NIM is being used by Silicon Valley-based firm Securiti to develop an AI-powered copilot for financial services. Securiti is a centralized, intelligent platform for data and generative AI safety.

The businesses will show how their technology can transform heterogeneous, sometimes complicated FSI data into actionable insights and enhanced innovation possibilities for banks, fintechs, payment providers, and other organizations at Money20/20, a premier fintech conference taking place this week in Las Vegas.

- Advertisement -

Ntropy Brings Order to Unstructured Financial Data

New York-based Ntropy Organizes Unstructured Financial Data Ntropy assists in clearing financial services processes of different entropy disorder, unpredictability, or uncertainty states.

By standardizing financial data from various sources and geographical locations, the company’s transaction enrichment application programming interface (API) serves as a common language that enables financial services applications to comprehend any transaction with human-like accuracy in milliseconds, at a 10,000x lower cost than conventional techniques.

The NVIDIA Triton Inference Server and Llama 3 NVIDIA NIM microservice use NVIDIA H100 Tensor Core GPUs. The Llama 3 NIM microservice increased Ntropy’s large language models (LLMs) usage and throughput by 20x compared to native models.

Using LLMs and the Ntropy data enricher, Airbase, a top supplier of procure-to-pay software platforms, improves transaction authorization procedures.

Ntropy will talk at Money20/20 about how their API may be used to clean up merchant data belonging to consumers, which increases fraud detection by enhancing risk-detection algorithms’ accuracy. Consequently, this lowers revenue loss and erroneous transaction declines.

In order to expedite loan distribution and user decision-making, an additional demonstration will demonstrate how an automated loan agent uses the Ntropy API to examine data on a bank’s website submit an appropriate investment report.

What Is A Contextual AI?

Contextual AI perceives and reacts to its surroundings. This implies that when it answers, it takes into account the user’s location, prior actions, and other crucial information. These systems are designed to provide customized and relevant responses.

Contextual AI Advances Retrieval-Augmented Generation for FSI

A California-based company with headquarters in Mountain View, provides a production-grade AI platform that is perfect for developing corporate AI applications in knowledge-intensive FSI use cases. Retriever-augmented generation powers this platform.

In order to provide significantly higher accuracy in context-dependent tasks, the Contextual AI platform combines the entire RAG pipeline extraction, retrieval, reranking, and generation into a single, optimized system that can be set up in a matter of minutes and further customized and tuned in response to user requirements.

HSBC intends to employ contextual AI to retrieve and synthesize pertinent market outlooks, financial news, and operational papers in order to enhance research findings and process recommendations. Contextual AI’s pre-built applications, which include financial analysis, policy-compliance report production, financial advising inquiry resolution, and more, are also being used by other financial institutions.

A user may inquire, “What’s our forecast for central bank rates by Q4 2025?” for instance. With references to certain parts of the source, the Contextual AI platform would provide a succinct explanation and a precise response based on real documents.

Contextual AI works with the open-source NVIDIA TensorRT-LLM library and NVIDIA Triton Inference Server to improve LLM inference performance.

NayaOne Provides Digital Sandbox for Financial Services Innovation

London-based NayaOne Offers a Digital Sandbox for Financial Services Innovation. Customers may safely test and certify AI applications using NayaOne‘s AI sandbox before they are commercially deployed. Financial institutions may develop synthetic data and access hundreds of fintechs on its platform.

Customers may utilize the digital sandbox to better assure top performance and effective integration by benchmarking apps for accuracy, fairness, transparency, and other compliance standards.

The need for AI-powered financial services solutions is growing, and our partnership with NVIDIA enables organizations to use generative AI’s potential in a safe, regulated setting. “Its’re building an ecosystem that will enable financial institutions to prototype more quickly and efficiently, resulting in genuine business transformation and expansion projects.”

Customers may explore and experiment with optimal AI models using NayaOne‘s AI Sandbox, which makes use of NVIDIA NIM microservices, and more quickly deploy them. When using NVIDIA accelerated computing, NayaOne can analyze massive datasets for its fraud detection models up to 10 times quicker and with up to 40% less infrastructure cost than when using extensive CPU-based algorithms.

Using the open-source NVIDIA RAPIDS data science and AI libraries, the digital sandbox speeds up money movement fraud detection and prevention. At the NVIDIA AI Pavilion at Money20/20, the company will display its digital sandbox.

Securiti’s AI Copilot Enhances Financial Planning

Securiti’s very adaptable Data+AI platform enables customers to create secure, end-to-end corporate AI systems, supporting a wide variety of generative AI applications such as safe enterprise AI copilots and LLM training and tuning.

The business is currently developing a financial planning assistant that is driven by NVIDIA NIM. In order to deliver context-aware answers to customers’ financial inquiries, the copilot chatbot accesses a variety of financial data while abiding by privacy and entitlement regulations.

The chatbot pulls information from a number of sources, including investing research materials, customer profiles and account balances, and earnings transcripts. Securiti’s technology preserves controls like access entitlements while securely ingesting and preparing information for usage with high-performance, NVIDIA-powered LLMs. Lastly, it offers consumers personalized replies via an easy-to-use user interface.

Securiti ensured the secure usage of data while optimizing the LLM’s performance using the Llama 3 70B-Instruct NIM microservice. The company will demonstrate generative AI at Money20/20. The NVIDIA AI Enterprise software platform offers Triton Inference Server and NIM microservices.

Read more on Govindhtech.com

[govindhtech]

Using Frontline AI Teammate To Reduce Pre-Surgery Anxiety

NVIDIA and Deloitte Collaborate to Implement Digital AI Agents for Healthcare. Ottawa Hospital will use NVIDIA AI-powered Deloitte Frontline AI Teammate to implement AI assistants that improve patient experiences and lessen administrative workload. Patients are often anxious and have many concerns about what to anticipate before they go to the hospital for surgery.

What is Frontline AI?

One potent conversational AI product is Frontline AI. It offers a new, channel-agnostic method of human interaction that cleverly organizes, evaluates, and responds to input.

Frontline AI in healthcare

Patient care

AI supports healthcare professionals in a variety of ways, including as improving surgical accuracy, monitoring patients in real time, and evaluating test findings and patient data to develop individualized treatment regimens.

Administrative task automation

AI technologies free up staff members to concentrate on patients by automating customer service, invoicing, and scheduling.

Telemedicine

Healthcare is becoming more accessible with to AI-powered telemedicine services that provide remote tests and consultations. Virtual health assistants help patients follow their treatment programs and provide them medical information.

NVIDIA and Deloitte Frontline AI Teammate

NVIDIA and Deloitte are working together to create AI agents that will use NVIDIA AI to provide the next generation of digital, frontline colleagues to patients before they ever enter the hospital, reducing presurvey anxiety

Before preadmission visits at hospitals, these virtual colleagues may answer a variety of inquiries, engage in genuine, human-like dialogues with patients, and provide helpful advice.

In collaboration with NVIDIA, Deloitte created Frontline AI Teammate, a digital avatar that can converse almost in any language and provide immediate answers to urgent queries for end users, including patients, in environments like hospitals.

Large language models, generative AI, and avatars are all features of Frontline AI Teammate, which is powered by the NVIDIA AI Enterprise software platform.

Conversational AI agents based on avatars provide a fantastic chance to lessen the productivity paradox that digitalization presents for the healthcare system. It may be a complementing innovation that reduces administrative burdens, frees up healthcare staff resources, and improves patient experience.

Next-Gen Technologies Powering Digital Humans

Doctors’ and patients’ experiences may be improved by the realistic interactions that digital people can provide.

To create digital people for applications in the healthcare sector, developers may use NVIDIA NIM microservices, which simplify the process of creating AI-powered apps and putting AI models into production. In addition to NVIDIA NeMo Retriever, an industry-leading embedding, retrieval, and re-ranking model that enables quick responses based on current healthcare data, NIM also includes an easily customizable NIM Agent Blueprint that developers can use to create interactive, AI-driven avatars that are perfect for telehealth.

James, an interactive demo created by NVIDIA, is an example of a customizable artificial person that can do duties including appointment scheduling, intake form completion, and providing information about forthcoming health services. Patients may find healthcare services more accessible and efficient as a result.

James provides lifelike, low-latency replies using NVIDIA ACE and Eleven Labs digital human technologies in addition to NIM microservices.

NVIDIA ACE is a collection of simulation, graphics, and AI tools for creating lifelike digital people. From realistic facial and bodily movement animations to speech and translation capabilities that can comprehend a variety of dialects and languages, it can include every facet of a digital person into healthcare applications.

Built on top of Deloitte’s Conversational AI Framework and powered by the NVIDIA AI Enterprise platform, Deloitte’s Frontline AI Teammate is intended to provide human-to-machine interactions in healthcare environments. The realistic avatar created by Deloitte inside the NVIDIA Omniverse platform is capable of answering intricate, domain-specific queries that are essential to the provision of healthcare.

To guarantee that no patient is left behind because of language limitations, the avatar makes use of NVIDIA Riva for smooth, multilingual conversation. Additionally, it has the NeMo Megatron-Turing 530B big language model installed for precise patient data processing and comprehension. These cutting-edge features might help people who might be uncomfortable in medical settings feel less anxious during clinical appointments.

Personalized Experiences for Hospital Patients

The volume of pre-operative information might overwhelm patients. Many weeks before to the operation, patients usually only get one preadmission visit, which might leave them with unanswered questions and growing anxiety. They may not ask all the important questions during these quick encounters since they are under stress from a severe diagnosis.

This may cause patients to arrive for preadmission visits unprepared, unaware of the reason for the appointment, its length, the location, and the required paperwork. This might result in surgical delays or even rescheduling.

The Ottawa Hospital is using AI agents, which are fueled by NVIDIA and Deloitte technology, to improve patient preparation and lessen anxiety before procedures by offering more reliable, accurate, and continuous information access.

Benefits that patients may enjoy with the digital companion include:

Using a smartphone, tablet, or home computer, you may access your digital buddy around-the-clock.

Dependable, pre-approved responses to specific inquiries, such as details on anesthesia or the actual treatment.

Consultation after surgery to address any concerns about the healing process, which might enhance health outcomes and treatment compliance.

The Frontline AI Teammate provides a fresh and creative way to address the issue in health human resources. It might lessen the administrative load, freeing up time for medical professionals to provide the high-quality treatment that the people need and deserve from the Ottawa Hospital’s digital experience lead.

Given the development of the New Campus Development, a new hospital project in Ottawa, the chance to investigate these technologies is opportune. It is essential to accurately identify the issues its are attempting to resolve in order to guarantee that this is carried out in a responsible and open manner.

To implement digital agents, Deloitte is collaborating with other medical facilities. By the end of the year, a patient-facing pilot with Ottawa Hospital is anticipated to launch.

Read more on govindhtech.com

[govindhtech]

Apriel Nemotron 15B LLM Developed by ServiceNow & NVIDIA

Apriel Nemotron 15B LLM

15B-Parameter Super Genius NVIDIA and ServiceNow Developers Join Your Service Teams.NVIDIA NeMo, Llama Nemotron, and ServiceNow domain data trained on NVIDIA DGX Cloud built the open-source Apriel Nemotron 15B LLM.

ServiceNow and NVIDIA announced at ServiceNow's annual Knowledge 2025 customer and partner event that they are collaborating to support a new class of intelligent AI agents in the workplace. Apriel Nemotron 15B, a new high-performance ServiceNow reasoning model developed with NVIDIA, weighs objectives, applies rules, and evaluates relationships to reach conclusions or make choices.

Post-training the open-source LLM with NVIDIA and ServiceNow data speeds up agentic AI, reduces latency, and lowers inference costs. The companies also want to speed up ServiceNow Workflow Data Fabric data processing by integrating NVIDIA NeMo microservices. This will trigger a closed-loop data flywheel that enhances model accuracy and user customisation.

Small, enterprise-grade LLMs for real-time process execution have improved using the Apriel Nemotron 15B reasoning model. The model was trained with NVIDIA DGX Cloud on AWS using ServiceNow domain-specific data, NeMo, and the Llama Nemotron Post-Training Dataset. As an NVIDIA NIM microservice, it delivers advanced reasoning in a smaller package, making it quicker, more effective, and cheaper to operate on GPU hardware.

Benchmarks show promising results for the model's size category, supporting its promise to perform agentic AI activities at scale. Enterprise AI is a groundbreaking technology that helps firms manage complexity, macroeconomic volatility, and more intelligent, resilient operations. This model's release corresponds with its rise.

ServiceNow and NVIDIA also introduced a shared data flywheel architecture to connect ServiceNow Workflow Data Fabric with NVIDIA NeMo microservices for model innovation and AI agent performance. This integrated solution contextualises and curates organisational workflow data to enhance and optimise reasoning models with protections to provide consumers control over how their data is used and processed securely and compliantly. This allows a closed-loop learning process to improve model accuracy and flexibility, speeding up the construction and implementation of highly customised, context-aware AI agents to improve business efficiency.

The announcement follows April's NVIDIA Llama Nemotron Ultra release. This model leverages the NVIDIA open dataset ServiceNow used for Apriel Nemotron 15B. Ultra is a top open-source model for advanced maths, coding, scientific thinking, and agentic AI.

Smaller Models Have More Impact

The Apriel Nemotron 15B reasoned, concluded, weighed goals, and followed rules in real time. It provides enterprise-grade intelligence but is smaller than contemporary general-purpose LLMs with over a trillion parameters. This speeds up answers and lowers inference costs.

The model was post-trained using AWS's NVIDIA DGX Cloud, which employed high-performance infrastructure to hasten development. AI models that support thousands of concurrent corporate tasks must be fast, efficient, scalable, and accurate.

Ongoing Education Closed Loop

The model and a new data flywheel architecture that integrates NVIDIA NeMo microservices like Customiser and Evaluator with ServiceNow's Workflow Data Fabric are also being released.

This arrangement allows a closed-loop workflow data-driven reply procedure that improves accuracy over time. Guardrails provide clients control over safe and lawful data use.

Scaling the AI Agent Era

The cooperation alters business AI strategy. Dynamic, intelligent systems are replacing static models in businesses. ServiceNow and NVIDIA extend their agentic AI collaboration across sectors to a new level.

Companies benefit from faster resolution, more responsive digital experiences, and higher productivity. It can scale with IT leaders' needs and match current performance and price standards.

Availability

Post-Knowledge 2025 Apriel Nemotron 15B-powered ServiceNow AI Agents are expected. The model will underpin ServiceNow's agentic AI and Now LLM services.