“Everybody needs data literacy, because data is everywhere. It’s the new currency, it's the language of the business. We need to be able to speak that.” –Piyanka Jain*
blogs.nvidia.com: [Transcription] [Excerpts] At its Ignite conference in Seattle today, Microsoft announced its new NC H100 v5 VM series for Azure, the industry’s first cloud instances featuring NVIDIA H100 NVL GPUs.
JUPITER Supercomputer: JUPITER marks the debut of a quad NVIDIA GH200 Grace Hopper Superchip node configuration, based on Eviden’s BullSequana XH3000 liquid-cooled architecture, with a booster module comprising close to 24,000 NVIDIA GH200 Superchips interconnected with the NVIDIA Quantum-2 InfiniBand networking platform. Source: NVIDIA Corporation
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☑️ #48 Nov 13, 2023
NVIDIA Supercharges Hopper, the World’s Leading AI Computing Platform
nvidianews.nvidia.com: [Transcription] [Excerpts] HGX H200 Systems and Cloud Instances Coming Soon From World’s Top Server Manufacturers and Cloud Service Providers.
The NVIDIA H200 is the first GPU to offer HBM3e — faster, larger memory to fuel the acceleration of generative AI and large language models, while advancing scientific computing for HPC workloads. With HBM3e, the NVIDIA H200 delivers 141GB of memory at 4.8 terabytes per second, nearly double the capacity and 2.4x more bandwidth compared with its predecessor, the NVIDIA A100
NVDIA H200
NVIDIA H200: the first GPU to offer HBM3e. Source: NVIDIA Corporation
(Update) @drjimfan: NVIDIA basically compressed 30 years of its corporate memory into 13B parameters. Our greatest creations add up to 24B tokens, including chip designs, internal codebases, and engineering logs like bug reports. Let that sink in.
The model "ChipNeMo" is deployed internally, like a shared genie:
EDA scripts generation. EDA stands for "Electronic Design Automation", a core software suite for designing the next-gen GPUs. These scripts are the keys to a $1T market cap;
Engineering assistant chatbot for GPU ASIC and Architecture engineers that understands internal hardware design specs and is capable of explaining complex design topics;
Bug summarization and analysis as part of an internal bug and issue tracking system;
Domain-finetuned retriever that achieves much better accuracy over internal knowledge.
Congrats to Haoxing "Mark" Ren's team for the outstanding work!
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☑️ #45 Oct 30, 2023 🔴 rumor
Naver replaces Nvidia GPU with Intel CPU for its AI map app server
kedglobal.com: [Transcription] [Excerpts] The Naver-Intel tie-up will likely diminish Nvidia’s clout in the AI processor market, analysts say
South Korea’s top web portal giant Naver Corp. has replaced the main chip supplier of its artificial intelligence server for its map service, Naver Place, from Nvidia Corp. to Intel Corp.
Naver has so far used Nvidia’s graphic processing unit (GPU)-based server to run its AI-powered location information provision service but recently replaced it with Intel’s central processing unit (CPU)-based server*, people familiar with the matter said on Monday.
Eureka! NVIDIA Research Breakthrough Puts New Spin on Robot Learning
blogs.nvidia.com: [Transcription] [Excerpts] A new AI agent developed by NVIDIA Research that can teach robots complex skills has trained a robotic hand to perform rapid pen-spinning tricks — for the first time as well as a human can.
The stunning prestidigitation, showcased in the video above, is one of nearly 30 tasks that robots have learned to expertly accomplish thanks to Eureka, which autonomously writes reward algorithms to train bots.
The Eureka research, published today, includes a paper and the project’s AI algorithms, which developers can experiment with using NVIDIA Isaac Gym, a physics simulation reference application for reinforcement learning research. Isaac Gym is built on NVIDIA Omniverse, a development platform for building 3D tools and applications based on the OpenUSD framework. Eureka itself is powered by the GPT-4 large language model.
AI Trains Robots
Eureka-generated reward programs — which enable trial-and-error learning for robots — outperform expert human-written ones on more than 80% of tasks, according to the paper.
In this demo, we visualize the unmodified best Eureka reward for each environment and the policy trained using this reward. Our environment suite spans 10 robots and 29 distinct tasks across two open-sourced benchmarks, Isaac Gym (Isaac) and Bidexterous Manipulation (Dexterity).
Isaac Gym: NVIDIA’s physics simulation environment for reinforcement learning research.
Dexterity: in the video above.
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☑️ #43 Oct 18, 2023 🤖 robotics
Accelerate AI-Enabled Robotics with Advanced Simulation and Perception Tools on NVIDIA Isaac Platform
developer.nvidia.com: [Transcription] [Excerpts] NVIDIA announced major updates to the NVIDIA Isaac Robotics platform today at ROSCon 2023. The platform delivers performant perception and high-fidelity simulation to robotics developers worldwide. These updates include the release of NVIDIA Isaac ROS 2.0and NVIDIA Isaac Sim 2023.1 and perception and simulation upgrades that simplify building and testing performant AI-based robotic applications for ROS developers.
Simulation of Highly Dynamic Omnidirectional Robots in Isaac Sim
Talks During the Week of ROSCon 2023: Marvin Wiedemann from Fraunhofer Institute will demonstrate how the ROS ecosystem supports the development of AMRs, from modeling the robot’s dynamic-to-Sim2Real measurements. He’ll also discuss how to create a digital twin in the ROS ecosystem using NVIDIA Omniverse Isaac Sim™.
Bits and Bytes: ”What is this machine that you are trying to create? What is its output, what is its input, what are the conditions that it is in? What is the industry like? Is it a fast-moving industry? Is it bureaucratic? Is it highly regulated? What kind of industry is it? And what are you trying to build?”
Jensen Huang, on the importance of first principles thinking when creating and running a company.
@bloombergtechnology: Bloomberg's Ed Ludlow takes a look at what Nvidia's famous h100 GPU looks like in the real world and lift's the lid on Nvidia's AI supercomputer.
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☑️ #40 Oct 15, 2023
No one ever got fired for buying ... Nvidia
The Chip Letter: We need to talk about risk, FUD and other factors that influence decision making
Microsoft to Debut AI Chip Next Month That Could Cut Nvidia GPU Costs
theinformation.com: [Transcription] [Excerpts] Microsoft next month plans to unveil the company’s first chip designed for artificial intelligence at its annual developers’ conference, according to a person with direct knowledge. The move, a culmination of years of work, could help Microsoft lessen its reliance on Nvidia-designed AI chips, which have been in short supply as demand for them has boomed.
The Microsoft chip, similar to Nvidia GPUs, is designed for data center servers that train and run large language models, the software behind conversational AI features such as OpenAI’s ChatGPT. Microsoft’s data center servers currently use Nvidia GPUs to power cutting-edge LLMs for cloud customers, including OpenAI and Intuit, as well as for AI features in Microsoft’s productivity apps.
How Researchers Use Nvidia’s GPUs to Simulate Qubits
spectrum.ieee.org: [Transcription] [Excerpts] Classical computers can be a useful tool, up to a point.
Between integrating its Grace Hopper chip directly with a quantum processor and showing off the ability to simulate quantum systems on classical supercomputers, Nvidia is making waves in the quantum computing world this month.
Nvidia is certainly well positioned to take advantage of the latter. It makes GPUs that supercomputers use, the same GPUs that AI developers crave. These same GPUs are also valuable as tools for simulating dozens of qubits on classical computers. New software developments mean that researchers can now use more and more supercomputing resources in lieu of real quantum computers.
But simulating quantum systems is a uniquely demanding challenge, and those demands loom in the background.
PennyLane is an open-source software framework for quantum machine learning, quantum chemistry, and quantum computing, with the ability to run on all hardware. Built with ❤️ by Xanadu.
Enabling the World’s First GPU-Accelerated 5G Open RAN for NTT DOCOMO with NVIDIA Aerial
developer.nvidia.com: [Transcription] [Excerpts] NVIDIA, working with Fujitsu and Wind River, has enabled NTT DOCOMO to launch the first GPU-accelerated commercial Open RAN 5G service in its network in Japan.
This makes it the first-ever telco in the world to deploy a GPU-accelerated commercial 5G network.
The announcement is a major milestone as the telecom industry strives to address the multi-billion-dollar problem of driving improvements in performance, total cost of ownership (TCO), and energy efficiency. The solution unlocks the flexibility, scalability, and supply chain diversity promise of Open RAN.
Infosys and NVIDIA Collaborate to Help World’s Enterprises Boost Productivity with Generative AI
infosys.com: [Transcription] [Excerpts] Expanded collaboration to provide expertise and technology needed to drive productivity gains with generative AI applications and solutions across industries. New Centre of Excellence will train 50,000 Infosys employees on NVIDIA AI technology.
“Infosys is transforming into an AI-first company to better provide AI-based services to our clients worldwide. Our clients are also looking at complex AI use cases that can drive significant business value across their entire value chain,”
“Infosys Topaz offerings and solutions are complementary to NVIDIA’s core stack. By combining our strengths and training 50,000 of our workforce on NVIDIA AI technology, we are creating end-to-end industry leading AI solutions that will help enterprises on their journey to become AI-first.”
Nandan Nilekani, Co-founder and Chairman, Infosys.
In total, Huang has now sold $70 million worth of $NVDAshares in the last week
@JesseCohenInv: Yesterday, Nvidia $NVDA CEO Jensen Huang sold 29,688 shares of Nvidia, worth $27 million dollars. Last week, Huang dumped more than 89,000 shares of Nvidia, worth $42.8 million dollars. In total, Huang has now sold $70 million worth of $NVDA shares in the last week.
It's noteworthy to mention that $NVDA experienced a decline of over 50% in the six months following his previous sale in early 2022. Does Jensen Huang know something we don't?
@MisterSpread: Yes Jesse, he knows how to take some profits of the table like any smart, experienced, seasoned investor would do. He has +86 million shares and since when is a problem when the founders/ceos are selling small chunks? P.S: Before any of the $NVDIA doomers start with their rambling, fyi I think that NVDIA is very overvalued, but posts like this are very poor in reason.
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☑️ #33 Sep 8, 2023
Nvidia, Tata to create extensive AI infrastructure
nvidianews.com: [Transcription] [Excerpts] NVIDIA today announced an extensive collaboration with Tata Group to deliver AI computing infrastructure and platforms for developing AI solutions. The collaboration will bring state-of-the-art AI capabilities within reach to thousands of organizations, businesses and AI researchers, and hundreds of startups in India.
The companies will work together to build an AI supercomputer powered by the next-generation NVIDIA® GH200 Grace Hopper Superchip to achieve performance that is best in class.
Jio Platforms (Reliance Industries) teams with NVDIA to bring state-of-the-art AI cloud infrastructure to India
ril.com: [Transcription] [Excerpts] New collaboration accelerates India’s AI development efforts, bringing leading AI capabilities to support nation’s competitiveness, address social challenges
Mumbai, 8th September 2023: The new AI cloud infrastructure will enable researchers, developers, startups, scientists, AI practitioners and others across India to access accelerated computing and high-speed, secure cloud networking to run workloads safely and with extreme energy efficiency.
The new infrastructure will greatly speed up a wide range of India’s key initiatives and AI projects, including AI chatbots, drug discovery, climate research and more.
As part of the collaboration, NVIDIA will provide Jio with end-to-end AI supercomputer technologies including CPU, GPU, networking, and AI operating systems and frameworks for building the most advanced AI models. Jio will manage and maintain the AI cloud infrastructure and oversee customer engagement and access.
spectrum.ieee.org: [Transcription] [Excerpts] Chief scientist Bill Dally explains the 4 ingredients that brought Nvidia so far.
Nvidia is riding high at the moment. The company has managed to increase the performance of its chips on AI tasks a thousandfold over the past 10 years, it’s raking in money, and it’s reportedly very hard to get your hands on its newest AI-accelerating GPU, the H100.
How did Nvidia get here? The company’s chief scientist, Bill Dally, managed to sum it all up in a single slide during his keynote address to the IEEE’s Hot Chips 2023 symposium in Silicon Valley on high-performance microprocessors last week. Moore’s Law was a surprisingly small part of Nvidia’s magic and new number formats a very large part. Put it all together and you get what Dally called Huang’s Law (for Nvidia CEO Jensen Huang).
@VALUEATINMENT: "They're Printing Money" - NVIDIA's Revenue SHOCKED Wall Street
PBD and the Home Team discuss the massive success of NVIDIA. How the stock is at an all-time high with rates going up, and how the company has consistently pivoted into the perfect industry.
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☑️ #29 Aug 24, 2023
Nvidia On the Mountaintop
stratechery.com: [Transcription] [Excerpt] That big jump in May was Nvidia’s last earnings, when the company shocked investors with an incredibly ambitious forecast; this last week Nvidia vastly exceeded those expectations and forecasted even bigger growth going forward.
The Path to $4.5B ✅ > Funding Round (Hugging Face)
@ClementDelangue: Super excited to welcome our new investors @SalesforceVC, @Google, @amazon, @nvidia, @AMD, @intel, @QualcommVenture, @IBM & @sound_ventures_ who all participated in @huggingface’s $235M series D at a $4.5B valuation to celebrate the crossing of 1,000,000 models, datasets and apps on the platform. These partners alone already shared over 1,000 open models and datasets and have over 10,000 users on Hugging Face. It takes a village to democratize good machine learning thanks to open-source and we’re just getting started!
NVIDIA Omniverse Opens Portals to Vast Worlds of OpenUSD
nvidianews.com: [Transcription] [Excerpts] New Omniverse Cloud APIs Help Developers Adopt OpenUSD; Generative AI Model ChatUSD LLM Converses in USD; RunUSD Translates USD to Interactive Graphics, DeepSearch LLM Enables Semantic 3D Search
SIGGRAPH—NVIDIA today announced a broad range of frameworks, resources and services for developers and companies to accelerate the adoption of Universal Scene Description, known as OpenUSD.
NVIDIA is advancing the development of OpenUSD — a 3D framework enabling interoperability between software tools and data types for the building of virtual worlds — through NVIDIA Omniverse™ and a new portfolio of technologies and cloud application programming interfaces (APIs) — including ChatUSD and RunUSD — along with a new NVIDIA OpenUSD Developer Program.
NVIDIA Unveils Next-Generation GH200 Grace Hopper Superchip Platform for Era of Accelerated Computing and Generative AI
First HBM3 processor
nvidianews.com: [Transcription] [Excerpts] World’s First HBM3e Processor Offers Groundbreaking Memory, Bandwidth; Ability to Connect Multiple GPUs for Exceptional Performance; Easily Scalable Server Design.
SIGGRAPH—NVIDIA today announced the next-generation NVIDIA GH200 Grace Hopper™ platform — based on a new Grace Hopper Superchip with the world’s first HBM3e processor — built for the era of accelerated computing and generative AI.
Created to handle the world’s most complex generative AI workloads, spanning large language models, recommender systems and vector databases, the new platform will be available in a wide range of configurations.
matt-rickard.com: [Transcription] [Excerpts] CUDA (Compute Unified Device Architecture) is a closed-source low-level API that interfaces software with NVIDIA GPUs.
CUDA is a major moat for NVIDIA. It’s part of why NVIDIA GPUs command such a premium over other hardware (and are perpetually in short supply).
A few reasons why the monopoly exists:
Hardware/software synergy. NVIDIA has consistently shipped the fastest hardware) and software. It’s been difficult for other companies to build this flywheel (software companies don’t have the hardware capabilities, and vice versa). Open-source libraries are magnitudes slower.
First mover. NVIDIA introduced CUDA in 2006. Both consumers and enterprises were locked in by designing their applications for CUDA.
Nvidia reportedly near deal with cloud provider Lambda Labs
theinformation.com: Nvidia reportedly near deal with cloud provider Lambda Labs
Everyone loves Lambda Stack — used by the F500, research labs, and the DOD. Source: Lambda, Inc.
Lambda Labs: The world’s best deep learning cloud. is nearing a deal to take an equity stake in Lambda Labs, a startup that competes with Amazon Web Services and other established cloud providers in renting servers with Nvidia chips to other companies, according to people with knowledge of the situation.
theinformation.com: Nvidia reportedly near deal with cloud provider Lambda Labs.
Nvidia is nearing a deal to take an equity stake in Lambda Labs, a startup that competes with Amazon Web Services and other established cloud providers in renting servers with Nvidia chips to other companies, according to people with knowledge of the situation.
Recursion Announces Collaboration and $50 Million Investment from NVIDIA to Accelerate Groundbreaking Foundation Models in AI-Enabled Drug Discovery
ir.recursion.com: [Transcription] [Excerpts] Companies to collaborate on software for biotech and pharmaceutical companies to create improved patient treatments faster
SALT LAKE CITY, TORONTO and MONTRÉAL, July 12, 2023 (GLOBE NEWSWIRE) -- Recursion (NASDAQ: RXRX), a leading clinical stage TechBio company decoding biology to industrialize drug discovery, today announced a $50 million investment by NVIDIA, which was executed as a private investment in public equity (PIPE). Recursion also announced plans to accelerate development of its AI foundation models for biology and chemistry, which, in collaboration with NVIDIA, it intends to optimize and distribute to biotechnology companies using NVIDIA cloud services.
Source: Recursion Pharmaceuticals, Inc.
🔹Continue reading & Blog (Recursion Partners with NVIDIA in Groundbreaking Collaboration)
reuters.com: Nvidia in talks to become anchor investor in Arm IPO, sources say
Softbank Group Corp.’s telecom arm is exploring a US listing for its loss-making PayPay mobile payments business, Reuters reported Wednesday, citing unnamed sources familiar with the matter.
SoftBank had been planning to sell Arm to U.S. chip designer Nvidia in a deal worth up to $80 billion, but that fell through last year due to objections from U.S. and European antitrust regulators. It has been targeting an IPO for the unit since then.
NVIDIA and SoftBank Group Announce Termination of NVIDIA’s Acquisition of Arm Limited (2/7/22)
NVIDIA to Acquire Arm for $40 Billion, Creating World’s Premier Computing Company for the Age of AI (9/13/20)
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☑️ #21 Jul 3, 2023
Top semiconductors by Market Cap
@genuineimpact: NVIDIA, the world's most valuable semiconductor company, has reached a staggering market cap of $1.044 trillion!
🤖️ Amidst the AI boom, this US chipmaker has gained over $500 billion in value since the beginning of 2023. 💥 Currently, Nvidia dominates the discrete GPU market share, with 80% of the market. 📈This has also widened the gap between NVIDIA and other semiconductor companies. NVIDIA's market cap is equivalent to that of two second-ranked TSMCs! Its market cap is also worth seven times that of the long-standing CPU giant, Intel. 😮 In terms of market cap, NVIDIA would rank 5⃣️ on the US stock market, following Apple, Microsoft, Google, and Amazon.
📊It's from our premium content. More semiconductor-related company analysis and charts are in our newsletter.
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☑️ archives | Jun 29, 2023
SoftBank Group Corp. to develop its own generative AI for companies»
asia.nikkei.com: Telecommunications arm to invest $138m in supercomputer-like equipment
TOKYO - - SoftBank Group's domestic telecommunications arm, SoftBank, will develop its own generative artificial intelligence (AI), Nikkei has learned, as the business of providing AI to companies for specific applications starts to spread in Japan's private sector.
SoftBank will invest 20 billion yen ($138 million) in computing infrastructure equivalent to a supercomputer equipped with a graphics processing unit from U.S. chip designer Nvidia. The computing infrastructure is expected to be one of the most powerful among Japanese companies. Such equipment is indispensable for processing the vast amounts of data needed to develop cutting-edge AI.
> SoftBank Corp. is already developing a "large language model" for its AI.
> In-house AI Chat Service: All of approximately 20,000 SoftBank employees use generative AI for work in a secure environment.
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☑️ archives Jun 28, 2023
Intel and Nvidia Square Off in GPT-3 Time Trials »
spectrum.ieee.org: [Transcription] [Excerpts] MLPerf provides LLM testbed for Nvidia’s H100 and top Intel chipsets
For the first time, a large language model—a key driver of recent AI hype and hope—has been added to MLPerf, a set of neural-network training benchmarks that have previously been called the olympics of machine learning. computers built around nvidia’s h100 gpu and intel’s habana gaudi2 chips were the first to be tested on how quickly they could perform a modified train of gpt-3, the large language model behind chatgpt.
> By one estimate, Nvidia and CoreWeave’s 11-minute (10.94) record-setting training time would scale up to about two days of full-scale training.
> A 3,584-GPU computer run as a collaboration between Nvidia and cloud provider CoreWeave performed this task in just under 11 minutes.
> The smallest entrant, a 256-Gaudi2 system, did it in a little over 7 hours. On a per-chip basis, H100 systems were 3.6-times as fast at the task as Gaudi2.
Habana Gaudi2. Source: Intel Corporation
Eight benchmark tests (included LLMs). Interactive chart here by Spectrum IEEE
China's ByteDance Has Gobbled Up $1 Billion of Nvidia GPUs for AI This Year
tomshardware.com: Chinese companies are frantically pre-ordering GPUs before government sanctions fully kick in.
[Transcription] [Excerpts] Chinese publication Jitwei revealed that ByteDance has already ordered around $1 billion worth of Nvidia GPUs in 2023 so far, which amounts to around 100,000 units split between Nvidia's A100 (ordered before the US government told Nvidia to stop selling its top-performing HPC cards to China, back in August 2022) and H800 cards - that last series number corresponding to a Hopper-based custom accelerator Nvidia built to comply with export restrictions — a nerfed cameo of the H100 accelerator.
The perspective is staggering, really, considering the remaining Chinese tech giants who have also heavily increased their investment into HPC hardware.
If ByteDance alone has already eclipsed Nvidia's sales in China for an entire year, what can be said for the Chinese market?
According to Chinese industry sources, the country's tech giants' volume and product demands are too much for China's own distributor chain to handle; which explains why at least ByteDance and Alibaba have been reported as directly negotiating product with Nvidia (as if the H800's existence allowed any doubt on that).
TSMC Is Sprinting to 2nm to Satisfy Demand From Nvidia, Apple
tomshardware.com: Getting ready for 2nm trial production and using Nvidia AI for optimized chip floor planning
[Transcription] According to the Economic Daily (UDN) in Taiwan, TSMC is putting its process node pedal to the metal to satisfy customers like Nvidia and Apple. The report suggests the contract chipmaker has started pre-production work to prepare for 2nm trial production, and that 2nm mass production is on track for 2025. TSMC’s plans, if successful, would also likely maintain its competitive edge against rivals like Intel and Samsung.
NVIDIA Collaborates With SoftBank Corp. to Power SoftBank's Next-Gen Data Centers Using Grace Hopper Superchip for Generative AI and 5G/6G
softbank.jp: Arm-Based Superchip and BlueField-3 DPU Power Revolutionary Architecture to Enable Generative AI-Driven Wireless Communications
“As we enter an era where society coexists with AI, the demand for data processing and electricity requirements will rapidly increase. SoftBank will provide next-generation social infrastructure to support the super-digitalized society in Japan,”
“Our collaboration with NVIDIA will help our infrastructure achieve a significantly higher performance with the utilization of AI, including optimization of the RAN. We expect it can also help us reduce energy consumption and create a network of interconnected data centers that can be used to share resources and host a range of generative AI applications.”
Junichi Miyakawa, president and CEO of SoftBank Corp.
⚡️
“Demand for accelerated computing and generative AI is driving a fundamental change in the architecture of data centers,”
“NVIDIA Grace Hopper is a revolutionary computing platform designed to process and scale-out generative AI services. Like with other visionary initiatives in their past, SoftBank is leading the world to create a telecom network built to host generative AI services.”
MediaTek Partners With NVIDIA to Provide Full-Scale Product Roadmap to the Automotive Industry
corp.mediatek.com: Integrating new NVIDIA GPU chiplet into the MediaTek Dimensity Auto platform provides the most advanced AI, connectivity and computing capabilities for next-generation smart cabins
nvidianews.com: New Class of AI Supercomputer Connects 256 Grace Hopper Superchips Into Massive, 1-Exaflop, 144TB GPU for Giant Models Powering Generative AI, Recommender Systems, Data Processing
NVIDIA Collaborates With Microsoft to Accelerate Enterprise-Ready Generative AI
nvidianews.com: NVIDIA AI Enterprise Integration With Azure Machine Learning Provides End-to-End Cloud Platform for Developers to Build, Deploy and Manage AI Applications for Large Language Models
Source: NVIDIA Corporation
“With the coming wave of generative AI applications, enterprises are seeking secure accelerated tools and services that drive innovation,”
“The combination of NVIDIA AI Enterprise software and Azure Machine Learning will help enterprises speed up their AI initiatives with a straight, efficient path from development to production.”
Manuvir Das, vice president of enterprise computing at NVIDIA.
⚡️
“Microsoft Azure Machine Learning users come to the platform expecting the highest performing, most secure development platform available,”
“Our integration with NVIDIA AI Enterprise software allows us to meet that expectation, enabling enterprises and developers to easily access everything they need to train and deploy custom, secure large language models.
John Montgomery, corporate vice president of AI platform at Microsoft.
Dell Technologies and NVIDIA Introduce Project Helix for Secure, On-Premises Generative AI
nvidianews.com: Project Helix makes it easy for enterprises to build and deploy trustworthy generative AI
[Transcription] [Excertps]
“Project Helix gives enterprises purpose-built AI models to more quickly and securely gain value from the immense amounts of data underused today,”
“With highly scalable and efficient infrastructure, enterprises can create a new wave of generative AI solutions that can reinvent their industries.”
Jeff Clarke,ice chairman and co-chief operating officer, Dell Technologies.
⚡️
“We are at a historic moment, when incredible advances in generative AI are intersecting with enterprise demand to do more with less,”
“With Dell Technologies, we’ve designed extremely scalable, highly efficient infrastructure that enables enterprises to transform their business by securely using their own data to build and operate generative AI applications.”
HPE and Tokyo Tech Collaborate to Build the Next Generation TSUBAME4.0 Supercomputer for Artificial Intelligence
@NVIDIADC: Powered by NVIDIA H100 #GPUs and NVIDIA Quantum-2 InfiniBand, @tokyotech_en TSUBAME4.0 supercomputer will accelerate #AI-driven research and discovery in medicine, materials science, climate research, and more. Learn now. #ISC23
[Transcription] [Excerpts] History of Healthcare Insights
Academia, startups and the UK’s large pharma ecosystem used the Cambridge-1 supercomputing resource to accelerate research and design new approaches to drug discovery, genomics and medical imaging with generative AI in some of the following ways:
InstaDeep, in collaboration with NVIDIA and the Technical University of Munich Lab, developed a 2.5 billion-parameter LLM for genomics on Cambridge-1. This project aimed to create a more accurate model for predicting the properties of DNA sequences.
King’s College London used Cambridge-1 to create 100,000 synthetic brain images — and made them available for free to healthcare researchers. Using the open-source AI imaging platform MONAI, the researchers at King’s created realistic, high-resolution 3D images of human brains, training in weeks versus months.
Oxford Nanopore used Cambridge-1 to quickly develop highly accurate, efficient models for base calling in DNA sequencing. The company also used the supercomputer to support inference for the ORG.one project, which aims to enable DNA sequencing of critically endangered species
Peptone, in collaboration with a pharma partner, used Cambridge-1 to run physics-based simulations to evaluate the effect of mutations on protein dynamics with the goal of better understanding why specific antibodies work efficiently. This research could improve antibody development and biologics discovery.
Relation Therapeutics developed a large language model which reads DNA to better understand genes, which is a key step to creating new medicines. Their research takes us a step closer to understanding how genes are controlled in certain diseases.
Nvidia CEO highlights accelerated computing and AI’s role in chip manufacturing at ITF World 2023
digitimes.com: NVIDIA founder and CEO Jensen Huang outlines the role of accelerated computing and #AI in an address to semiconductor industry leaders at #ITFWorld2023. Learn more on our blog: https://nvda.ws/3IfDbnY
digitimes.com: Nvidia has placed additional orders for AI chips that require TSMC's CoWoS (chip on wafer on substrate) packaging, according to industry sources.
Nvidia is optimistic about demand for AI chips, but it needs one-stop support from TSMC for both chip manufacturing and advanced packaging, the sources said.
Source: Nvidia Corporation
Related content (update):
https://www.tomshardware.com/news/nvidia-boosts-orders-of-compute-gpus-for-ai-report
[Transcription] [Excerpts] TSMC reportedly committed to process an additional 10,000 CoWoS wafers for Nvidia throughout 2023 to support growing demand for its widely used AI chips. The report estimates that this means an extra 1,000 to 2,000 wafers each month for the remaining part of the year.
The story does not reveal which compute GPUs Nvidia plans to increase production of, but at present the company has A100, A30, H100, and China-specific A800 and H800 GPUs in its line-up.
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☑️ #7 May 3, 2023
Nvidia launches new AI-powered gaming chip
@Byron_Wan: Tencent estimates that systems using Nvidia’s H800 — a slowed-down chip tailored for the Chinese market — will cut the time it takes to train its largest AI system by more than half, from 11 days to 4 days.
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☑️ #6 May 1, 2023
Now Shipping: DGX H100 Systems Bring Advanced AI Capabilities to Industries Worldwide
blogs.nvidia.com: Customers from Tokyo to Stockholm will plug into NVIDIA’s latest AI supercomputers to advance workloads that include generative AI across manufacturing, healthcare, robotics and more.
nvidianews.nvdia.com: NVIDIA GeForce RTX 4070 Brings Power of Ada Lovelace Architecture and DLSS 3 to Millions More Gamers and Creators, Starting at $599
“Affordable” is relative (arsTECHNICA): Nvidia’s $599 GeForce RTX 4070 is a more reasonably priced (and sized) Ada GPU. But it's the cheapest way (so far) to add DLSS 3 support to your gaming PC.
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☑️ #4 Apr 6, 2023
How Intel, AMD and Nvidia are Approaching the AI Arms Race
datacenterfrontier: Can the traditional data center chip vendors stay on top of AI? Here's a closer look at the artificial intelligence hardware roadmaps for Intel. AMD and NVIDIA.
The Grace Hopper Superchip combines the Grace ARM CPU architecture wit the Nvidia Hopper 9th generation data center GPU. Introducing Grace. Source: Nvidia Corporation
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☑️ #2 Apr 3, 2023
S&P 500 Map - Year to Date performance
finviz.com: Standard and Poor's 500 index stocks categorized by sectors and industries. Size represents market cap.
Market data and information provided by Morningstar (screenshot): Research disclosure notice. Source: TD Ameritrade, Inc. (The Charles Schwab Corporation)
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☑️ archives Feb 20, 2023
Mitsui and NVIDIA Announce Japan’s First Generative AI Supercomputer for Pharmaceutical Industry »
blogs.nvidia.com: Leading pharma companies in Japan will use the Tokyo-1 NVIDIA DGX supercomputer to accelerate drug discovery.
Tokyo-1 Accelerates Japanese Companies in Pharma and Beyond
Major Japanese pharma companies including Astellas Pharma, Daiichi-Sankyo and Ono Pharmaceutical are already making plans to advance their drug discovery projects with Tokyo-1.
This First Principles video complements our previous episode: First Principles: building a high performance network in the public cloud. In this episode Pradeep Vincent and Jag Brar, Oracle Cloud Infrastructure architects, explain how OCI took its application of RDMA networking a step further, building superclusters with the help of NVIDIA's ConnectX RDMA NICs to support tens of thousands of GPUs.
