Spatial Datacenters Profile
Version 10
3 June, Monday
12 noon
Newly development and deployment of Spatial Datacenters, exclusively for participants (Subscribers).
The worlds first vehicle Data-center with the most advanced Artificial Intelligence (AI) data capabilities. Focusing on video image data, including Spatial audio visual (AV), two dimensional (2D) and three dimensional (3D) supporting and able to database millions an billions of high-resolution images.
What is the difference between AI chip and regular chip? AI chips primarily focus on the computational aspects, managing the heavy data processing demands of AI tasks—challenges that surpass what general-purpose chips such as CPUs can handle. To meet these demands, they often utilize numerous smaller, quicker, and more efficient transistors.
Spatial Studio’s ER, Spatial Safety and all other Spatial forprofit and nonprofit offerings, planned to benefit Participants daily worldwide.
Spatial data, also known as geospatial data, is a term used to describe any data related to or containing information about a specific location on the Earth’s surface.
Spatial thinking allows understandably of the location and dimension of objects, and how different objects are related. It also allows visualization and manipulations of objects and shapes.
Extended reality (XR) is an umbrella term to refer to augmented reality (AR), virtual reality (VR), and mixed reality (MR). The technology is intended to combine or mirror the physical world with a “digital twin world” able to interact with it, giving users an immersive experience by being in a virtual or augmented environment.
The fields of virtual reality and augmented reality are rapidly growing and being applied in a wide range of areas such as entertainment, cinema, marketing, real estate, training, education, maintenance and remote work. Extended reality has the ability to be used for joint effort in the workplace, training, educational purposes, therapeutic treatments, and data exploration and analysis.
Extended reality works by using visual data acquisition that is either accessed locally or shared and transfers over a network and to the human senses. By enabling real-time responses in a virtual stimulus these devices create customized experiences.
Advancing in 5G and edge computing – a type of computing that is done “at or near the source of data” – could aid in data rates, increase user capacity, and reduce latency. These applications will likely expand extended reality into the future.
Autonomous advanced driving system support. Artificial intelligence AI imagery Data center capability working with third-party applications
Critical emergency situation support including autonomous operation, without humans when the environmental hazardous condition for humans, remote locations and 24/7 operations, lights-off capabilities.
Spatial thinking allows you to understand the location and dimension of objects, and how different objects are related. It also allows you to visualize and manipulate objects and shapes. Spatial data, also known as geospatial data, is a term used to describe any data related to or containing information about a specific location on the Earth’s surface.
Research suggests that spatial skills and geometric reasoning play a critical role in the development of problem-solving skills. Spatial intelligence is the concept of being able to successfully perceive and derive insight from visual data. This cognitive process is known as an aptitude for understanding visual information in the real and abstract word as well as an innate ability to envision information.
Spatial awareness, refers to the ability to understand and navigate physical space.
Three-dimensional data refers to the ability to visualize and mentally manipulate objects and spaces in three dimensions (3D).
Data-center participatants are expected to be: individuals, small businesses and supporting nonprofit organizationial activities to help make a better world.
Philanthropists, support is expected to help make a better world and benefit humanity. Including in-kind contribution of equipment, vehicles and other missions critical assets.
Mission critical support planned with vehicle Data-centers. (Configuration of components designed to be moved quickly and easily for indoor & outdoor temporary deployment).
Single-Rack planning with mobility, flexibility and in-motion capabilities.
Planning to use ski resort Gondolas, for Datacenters. Planning on placing the gondolas in the Tesla Cybertruck, truck-bed with removable capabilities from top and bottom (electric forklifts). Example: CWA – Model Omega S, 8 passengers. 80″long x 60″wide x 80″high. Sits on 4 pegs. Aluminum floor. Fiberglass seats, Plexiglas windows. Doors works. 2 ski racks.
NVIDIA Tesla A100
The Tesla A100 is meant to be scaled to up to thousands of units and can be partitioned into seven GPU instances for any size workload. Each Tesla A100 provides up to 624 teraflops performance, 40GB memory, 1,555 GB memory bandwidth, and 600GB/s interconnects.
NVIDIA’s technologies newest conclusion, based on the initial specifications and preliminary performance benchmarks, the NVIDIA HG, H200 seems a significant step forward from A100 and H100 GPUs in terms of overall performance, energy savings, and TCO (total cost of ownership).
Choosing the right NVIDIA data center GPU as the ideal solution problems in deep learning and AI, HPC, graphics, or virtualization in the data centers or at the edge.
Technology section determined on many factors: performance requirements, availability and other parameters (Not the cost/price).
The rack or racks are easily changeable, the components on the racks(s) are easily changed. The best available technologies available will be provided.
Preferred Providers relationships with technology leadership are important considerations. Enabling the best leadership to determine the best overall technology is the best way to achieve having the best available technologies.
Elon Musk wants to turn Tesla’s fleet into AWS (Amazon web services) for AI. During an earnings call with investors, Elon Musk threw out, an idea: what if AWS, but for Tesla?
How powerful is a Tesla GPU? The monster computer found in every Tesla built since October 2016 is the Nvidia computer used by the Autopilot and Self Driving systems. Its two GPUs are capable of 25 Trillion operations per second and require liquid cooling from the car’s cooling system. Nov 12, 2021
Musk, who loves to riff on earnings calls, compared the unused compute power of millions of idle Tesla vehicles to Amazon’s cloud service business.
If they’re just sitting there, he mused, why not put them to good use to run AI models?
“There’s a potential… when the car is not moving to actually run distributed inference,” Musk said. “If you imagine the future perhaps where there’s a fleet of 100 million Teslas and on average, they’ve got like maybe a kilowatt of inference compute. That’s 100 gigawatts of inference compute, distributed all around the world.”
So, to summarize, you buy a Tesla. It’s your property. But Musk wants to freely use the unused compute power in your vehicle for… something? Possibly AI-related? (Tesla is an AI company now, by the way. Musk said so himself during the call.)
Planning on having, the e NVIDIA H200 Tensor Core GPU supercharges generative AI and HPC workloads with game-changing performance and memory capabilities.
Each racking will be specific to the requirements of the participants requirements.
How fast is the Nvidia H200? 4.8 terabytes per second. With HBM3e, the NVIDIA H200 delivers 141GB of memory at 4.8 terabytes per second
What is the Nvidia H200 used for? The world’s most powerful GPU for supercharging AI and HPC workloads.
Benefitcial for rapid deployment, remote locations and modular integration. Hub and spoke concept with hub featuring maximum capabilities and spokes being Tesla vehicles with Compute capabilities.
Planning on Compute opt-in program argumenting the private vehicle Data-center vehicle fleet.
NVIDIA’s Tesla data GPU.
Planning integration of Preferred Providers hardware, software including Applications, and other third-party products/services. Off-the-shelf proven products with mass production support scalability worldwide.
Tesla planning on using the Tesla Cybertruck as the chassis for Datacenters platform. The trucks
The Tesla Cybertruck is a battery electric medium duty full-size pickup truck built by Tesla, Inc.Cybertruck bed has a full 6 feet length and 4 feet wide. The truck bed used for datacenters equipment. Planning using Tesla roof racks for mounting satellite antennas. (SpaceX Starlink communication system, with in-motion capabilities) and energy storage system (Tesla energy, Powerwalls).
The Tesla Cybertruck has a payload capacity of 2,500 pounds in its 6 ft long by 4 ft wide composite cargo bed. The Cybertruck also has 67 cubic feet of lockable storage space, and the second row seats can be folded down for an additional 54 cubic feet of storage.
The Tesla Cybertruck has wade mode, which allows it to confidently navigate shallow rivers and streams, thanks to its high ground clearance, air suspension, and pressurized battery pack technology.
About 71 percent of the Earth’s surface is water-covered. It’s beneficial to navigate waterways, supporting Datacenters placement.
Cybertruck also has four-wheel steering. When the driver turns the steering wheel, all four wheels respond. This gives Cybertruck a tighter turning radius.
Geolocation Third-party Application (What 3 Words) and Apple’s geolocation application planned.
A platform that is ready for plug and play with any third-party app including Google Maps, what3words has already sliced the world into 57 trillion three-meter squares.
Potentially, placement of vehicle Data-centers in 57 Trillion different locations in the world.
That is, 57 trillion three-meter squares, at a time. On earth, all landmass is covered, including the north pole and the south pole. And oceans too.
Any location a user pins, gets earmarked into a square that is 3 meters x 3 meters. That is how what3words envisions all of the earth’s surface. Each of these squares is given a unique address, made of three words.
Participation exclusively for participants (Subscribers) not available to general public. Limited number of subscribers/participants to insure high-quality experiences.
Data-center important with advanced vision capacity is critical for humanity. Instant data conversion to actionable intelligence supports improved decision-making.
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Network activities using, .net
Landing craft are the ultimate utility vessel. Their large, spacious decks and bow doors/loading ramp make them perfect for transporting everything from small vehicles to materials and personnel. The ability to beach themselves and operate efficiently in shallow waters offers much more flexibility than normal watercraft. Gone are the days of struggling to heave heavy supplies over the gunnel or squeezing them through a small side door — a landing craft’s large bow door vastly simplifies the process of moving supplies.
What is a Landing craft?
Landing craft are specialized vessels that are designed to efficiently transport cargo, equipment, personnel, and materials across the water. Taking cargo from larger ships, offshore bases, or just across a small body of water, they are extremely versatile in their uses. Coming in all sizes and configurations, the bow door and its use to connect sea and land is what makes it a landing craft.
Originally being used for military operations in World War II, modern landing craft have become much more diverse in their applications. Distinctly different, they are adaptable and useful for almost any work done on the water.
Boats for datacenters planning on having a fleet of landing craft boats. are built using commercial-grade aluminum alloy. A thick hull allows boats to handle most bumps and scrapes along the way without worry.
Additionally, custom designs maximize bow door length and space on the deck. We understand that every mission is unique and no single boat is going to be perfect for everything, and that is why we offer fully customizable options and configurations for our boats.
Planning arrangements of landing craft’s for exactly what is needed.
Landing crafts are used all over the world. Their relevance for most tasks showcases their amazing adaptability and importance in all industries. Having a jobs on the water, chances are that a landing craft will be the perfect vessel.
Planning electric motor watercrafts, with capabilities of Cybertruck’s drive-on and drive-off capabilities.
With a hull that packs power and comfort, the Pardo yachts has the perfect combination for performance and cruise-ability. Planning on having Datacenters on Prado yachts. The hulls ha limited drafts, shallow-water capabilities. Outboard electric motors are planned, inboard electric motors planned in future.
Water capabilities are imperative for vehicle Data-centers deployments. The Tesla Cybertruck has wade mode, which allows it to confidently navigate shallow rivers and streams, thanks to its high ground clearance, air suspension, and pressurized battery pack technology.
Data-center fleet. It’s planned to have many pre-positioned Datacenters vehicle fleets.
Three (3) Cybertrucks within each separate fleet. Each truck with different purposes
Data-center truck One (lead vehicle in motorcade). Primary purpose to transport stand behind compact track loader (CTL) electric powered, to ensure accessibility including narrow doorways, attachment planned include forks and buckets for moving debris, equipment and setting,-up Dome structures.
Data-center truck two (2) primary for datacenters rack, including transportation of gondola’s.
Data-center truck three (3) support vehicle for additional energy storage (Tesla Powerwalls) potentially settling to and from charging infrastructure support and continuing operations. Also, others supportive aspects including food and drinks, dome structures and much more.
The three vehicle fleet providers unparalleled off-grid, completely self-sufficient and self-contained Data-center capabilities, that were never possible before.
All vehicles plan to have roof-mounted Lighting systems including emergency lights for high-speed authorized movements. Inver ness with microphone technologies.
Motion robotic arms for camera clusters are planned third party product from cinnabot.
Camera clusters plan to include multiple cameras capable of spatial video recordings and spatial sound recordings.
Human and humanoids are planned for shopping the data centers. Tesla bots (robots) are planned for operating data centers as appropriate.
24 how was your day, 7 days a week and 365 days a year data centers are expected to achieve tier 4 Status: meaning up-time of 99.99%, less than 6 hours downtime per year.
Military and law enforcement
Planning on coaching military branches and law enforcement ages with limited involvements.
Disclaimer
All information is up to change without notice that is completely conceptual.
Forward-looking statement
The information contains a significant forward looking statement that may or may not it happen in the future.
Open-source not proprietary
All information is open-source is not proprietary can be used by anyone at any time for useful purposes.
Backgrounds
Nvidia: How a U.S. company richer than the Canadian economy hired a key leader from Toronto. The U.S. tech giant recently became one of the few companies on Earth with a market cap value of more than US$2 trillion
Author of the article: Chris Knight
Published on May 30, 2024
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Extended reality (XR) is an umbrella term to refer to augmented reality (AR), virtual reality (VR), and mixed reality (MR). The technology is intended to combine or mirror the physical world with a “digital twin world” able to interact with it, giving users an immersive experience by being in a virtual or augmented environment.
The fields of virtual reality and augmented reality are rapidly growing and being applied in a wide range of areas such as entertainment, cinema, marketing, real estate, training, education, maintenance and remote work. Extended reality has the ability to be used for joint effort in the workplace, training, educational purposes, therapeutic treatments, and data exploration and analysis.
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Extended reality works by using visual data acquisition that is either accessed locally or shared and transfers over a network and to the human senses. By enabling real-time responses in a virtual stimulus these devices create customized experiences. Advancing in 5G and edge computing – a type of computing that is done “at or near the source of data” – could aid in data rates, increase user capacity, and reduce latency. These applications will likely expand extended reality into the future.
Spatial awareness, refers to the ability to understand and navigate physical space. This includes agility. Three-dimensional thinking refers to the ability to visualize and mentally manipulate objects and spaces in three dimensions.
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Spatial Thinking:
Research suggests that spatial thinking skills and geometric reasoning play a critical role in the development of problem-solving skills
Spatial intelligence is the concept of being able to successfully perceive and derive insight from visual data. This cognitive process is known as an aptitude for understanding visual information in the real and abstract word as well as an innate ability to envision information.
Spatial data, also known as geospatial data, is a term used to describe any data related to or containing information about a specific location on the Earth’s surface.
Spatial thinking allows you to understand the location and dimension of objects, and how different objects are related. It also allows you to visualize and manipulate objects and shapes.
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Planning to use ski resort Gondolas, for Datacenters. Planning on placing the gondolas in the Tesla Cybertruck, truck -bed with removable capabilities from top and bottom (electric forklifts)
Example: CWA – Model Omega S, 8 passengers. 80″long x 60″wide x 80″high. Sits on 4 pegs. Aluminum floor. Fiberglass seats, Plexiglas windows. Doors works. 2 ski racks.
1997 Swiss made ski resort gondola. CWA – Model Omega S, 8 passengers. 80″long x 60″wide x 80″high. Sits on 4 pegs. Aluminum floor. Fiberglass seats, Plexiglas windows. Doors works. 2 ski racks. Kerosene heater system (in the floor) does not work anymore.
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Pardo Yachts:
With a hull that packs power and comfort, the Pardo yachts has the perfect combination for performance and cruise-ability.
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About 71 percent of the Earth’s surface is water-covered.
What is a Landing craft?
Landing craft are specialized vessels that are designed to efficiently transport cargo, equipment, personnel, and materials across the water. Taking cargo from larger ships, offshore bases, or just across a small body of water, they are extremely versatile in their uses.
Coming in all sizes and configurations, the bow door and its use to connect sea and land is what makes it a landing craft.
Originally being used for military operations in World War II, modern landing craft have become much more diverse in their applications.
Distinctly different, they are adaptable and useful for almost any work done on the water.
Landing craft are the ultimate utility vessel. Their large, spacious decks and bow doors/loading ramp make them perfect for transporting everything from small vehicles to materials and personnel.
The ability to beach themselves and operate efficiently in shallow waters offers much more flexibility than normal watercraft.
Gone are the days of struggling to heave heavy supplies over the gunnel or squeezing them through a small side door — a landing craft’s large bow door vastly simplifies the process of moving supplies.
ARMOR Marine’s all-welded aluminum landing craft are built using commercial-grade aluminum alloy. A thick hull allows our boats to handle most bumps and scrapes along the way without worry.
Additionally, custom designs maximize bow door length and space on the deck. We understand that every mission is unique and no single boat is going to be perfect for everything, and that is why we offer fully customizable options and configurations for our boats. Contact us to discuss how we can arrange one of our landing craft’s for exactly what you need.
Aluminum landing crafts are used all over the world. Their relevance for most tasks showcases their amazing adaptability and importance in all industries.
If you have a job on the water, chances are that a landing craft will be the perfect vessel for you. View our newest landing craft models below and contact us to get started on your landing craft today.
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What3words Application:
A platform that is ready for plug and play with any third-party app including Google Maps, what3words has already sliced the world into 57 trillion three-meter squares
That is, 57 trillion three-meter squares, at a time. On earth, all landmass is covered, including the north pole and the south pole. And oceans too.
Any location a user pins, gets earmarked into a square that is 3 meters x 3 meters. That is how what3words envisions all of the earth’s surface. Each of these squares is given a unique address, made of three words.
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The Tesla Cybertruck is a battery electric medium duty full-size pickup truck built by Tesla, Inc.
Cybertruck also has four-wheel steering. When the driver turns the steering wheel, all four wheels respond. This gives Cybertruck a tighter turning radius.
The Tesla Cybertruck has wade mode, which allows it to confidently navigate shallow rivers and streams, thanks to its high ground clearance, air suspension, and pressurized battery pack technology.
The Tesla Cybertruck has a payload capacity of 2,500 pounds in its 6 ft long by 4 ft wide composite cargo bed. The Cybertruck also has 67 cubic feet of lockable storage space, and the second row seats can be folded down for an additional 54 cubic feet of storage.
Cybertruck at a full 6 feet, the narrow 4 feet. Bed size used for Datacenters equipment.
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What is the difference between AI chip and regular chip?
AI chips primarily focus on the computational aspects, managing the heavy data processing demands of AI tasks—challenges that surpass what general-purpose chips such as CPUs can handle. To meet these demands, they often utilize numerous smaller, quicker, and more efficient transistors.
Nvidia:
What is the Nvidia H200 used for? The world’s most powerful GPU for supercharging AI and HPC workloads.
The NVIDIA H200 Tensor Core GPU supercharges generative AI and HPC workloads with game-changing performance and memory capabilities.
How fast is the Nvidia H200? 4.8 terabytes per second. With HBM3e, the NVIDIA H200 delivers 141GB of memory at 4.8 terabytes per second.
What is A100 chip?
The Most Powerful Compute Platform for. Every Workload. The NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration—at every scale—to power the world’s highest- performing elastic data centers for AI, data analytics, and high- performance computing (HPC) applications.
NVIDIA data center GPU
A Comparative Analysis of NVIDIA A100 Vs. H100 Vs. L40S Vs. H200 — December 1, 2023
NVIDIA recently announced the 2024 release of the NVIDIA HGX™ H200 GPU—a new, supercharged addition to its leading AI computing platform.
Gcore is excited about the announcement of the H200 GPU because we use the A100 and H100 GPUs to power up our AI GPU cloud infrastructure and look forward to adding the L40S GPUs to our AI GPU configurations in Q1-2024.
So we consider this the right time to share a comparative analysis of the NVIDIA GPUs: the current generation A100 and H100, the new-generation L40S, and the forthcoming H200.
The NVIDIA A100, H100, L40S, and H200 represent some of the most advanced and powerful GPUs in the company’s lineup. They’re designed specifically for professional, enterprise, and data center applications, and they feature architectures and technologies optimized for computational tasks, AI, and data processing. Let’s see how they stack up against each other on key technical specifications.
Expecting the H200 to outperform the previous and current generation of NVIDIA data center GPUs across use cases.
The current generation—the H100—is a close match to the H200, with near identical multi-precision computing performance. So, while H200s will offer improvements, H100s will remain a top option.
As for the A100, it’s the least- performant GPU when compared to its successors, while still offering solid performance for certain tasks.
Conclusion, based on the initial specifications and preliminary performance benchmarks, the NVIDIA HG, H200 seems a significant step forward from A100 and H100 GPUs in terms of overall performance, energy savings, and TCO (total cost of ownership).
Choosing the right NVIDIA data center GPU as the ideal solution problems in deep learning and AI, HPC, graphics, or virtualization in the data centers or at the edge.
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CPUs and GPUs, move over. Thanks to recent revelations surrounding new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads.
Using TPUs for the inference stage of deep neural networks. TPU help to bolster the effectiveness of various artificial intelligence workloads, including language translation and image recognition programs.
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Self-driving cars are one of the most prominent areas where deep learning and AI will have an impact. Beyond that, there are many other places where having an on-board AI chip to react to real-world conditions, including in mobile phones and virtual reality headsets. The technology is moving very quickly at the moment, and we’ll soon see other practical uses that will impact our lives.
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NVIDIA — Jensen Huang
Nvidia Corp’s co-founder Jensen Huang has surpassed each member of Walmart’s founding Walton family in terms of personal wealth in a testament to the burgeoning dominance of tech titans.
Following Nvidia’s performance in the latest quarter, Huang’s net worth surged to $91.3 billion, propelling him to the 17th spot on the Bloomberg Billionaires Index.
The major share of Huang’s wealth, amounting to nearly $91 billion, is tied up in Nvidia stock, which soared by 9.3% fueled by an optimistic sales forecast.
What you should know…. Huang, renowned for his visionary outlook and leadership, has often heralded the dawn of a new industrial revolution.
His foresight in recognizing Nvidia’s potential in the AI domain has been pivotal in steering the company to unprecedented heights.
Nvidia’s publicly traded stock moment climbing by 23% this year, Nvidia’s stock stands out as the third-best performer in the S&P 500 Index, soaring by an impressive 110%.
Jensen Huang’s journey to tech mogul status traces back to his co-founding of Nvidia in 1993 alongside Chris Malachowsky and Curtis Priem.
Born in Taiwan and educated in the United States, Huang received an undergraduate electrical engineering degree in 1984 from Oregon state university and a master’s degree in the same subject from Stanford University in 1992.
Nvidia’s rise can be largely attributed to its pivotal role in the artificial intelligence sphere, particularly with its AI accelerator chips.
This dominance has propelled Nvidia’s market value beyond $2.5 trillion, solidifying its position as a frontrunner in the tech industry.
Nvidia Corporation is an American technology company renowned for its graphics processing units (GPUs) and artificial intelligence (AI) computing solutions.
Nvidia initially focused on producing graphics cards for gaming, however, the company expanded its scope to include AI, data centers, autonomous vehicles, and other emerging technologies.
Nvidia’s GPUs are widely used in gaming, professional visualization, data centers, and automotive markets.
The company’s AI computing platforms, such as the Nvidia Tesla GPU accelerators, are pivotal in powering AI applications across various industries, including healthcare, finance, and transportation.
This ascent reinforces Nvidia’s stature as a primary beneficiary of the burgeoning artificial intelligence sector, where spending is on the rise.
For its part, NVidia will be looking to solidify its hold on the emerging machine learning market. While energy-hungry GPUs aren’t as efficient on the inference side of the equation, they’re tough to be beat for the compute-intensive training of neural networks, which is why Web giants like Google, Facebook, Microsoft and others are using so many of them for AI workloads.
However, NVidia isn’t giving up on the inference side of the market, and recently published a benchmark that showed how much better its latest Pascal GPU architectures, most notably the P40, is at inferring than its older Kepler GPU architecture (check out the HPCWire story here). The K80 also out-performed the Google TPU, although Google has probably advanced its TPU since 2015, which is when it calculated the benchmark figures it recently shared. NVidia’s recent hiring of Clément Farabet (formerly of Twitter) also could also portend a shift to more real-time workloads too.
Qualcomm could also be involved in the inference side of the equation. The mobile chipmaker has been working with Yann LeCun, Facebook’s Director of AI Research, to develop new chips for real-time inference, according to this Wired story. LeCun developed one of the first AI-specific chips for inference more than 25 years ago while working at Bell Labs.
The San Diego company recently announced plans to spend $47 billion to buy NXP, a Dutch company that makes chips for cars. NXP was working on deep learning and computer vision problems before the acquisition was announced, and it appears that Qualcomm will be looking to NXP to give it an edge in developing systems for autonomous driving.
Self-driving cars are one of the most prominent areas where deep learning and AI will have an impact. Beyond that, there are many other places where having an on-board AI chip to react to real-world conditions, including in mobile phones and virtual reality headsets. The technology is moving very quickly at the moment, and we’ll soon see other practical uses that will impact our lives.
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Groq’s incorporation:
According to an SEC document filed for Groq’s incorporation, the company has raised about $10 million. Leading the way is Chamath Palihapitiya, a prominent Silicon Valley venture capitalist. Other ex-Googlers named in the SEC document include Jonathan Ross, who helped invent the TPU, and Douglas Wightman, who worked on the Google X “moonshot factory.”
But that’s not all. “We have eight of the 10 original people that built that chip building the next generation chip now,” Palihapitiy said in a March interview with CNBC. Groq is playing its cards close to the vest, and isn’t disclosing exactly what it’s working on—although by all indications, it would appear to have something to do with machine learning chips.
There are many other groups chasing this new market opportunity, including traditional chip bigwigs Intel and IBM.
While Big Blue pushes a combination of its RISC Power chips and NVidia GPUs in its Minsky AI server, its research arm is exploring other chip architectures. Most recently, the company’s Almaden Lab has discussed the capabilities of its “brain-inspired” TrueNorth chip, which features 1 million neurons and 256 million synapses. IBM says TrueNorth has delivered “deep networks that approach state-of-the-art classification accuracy” on several vision and speech datasets.
“The goal of brain-inspired computing is to deliver a scalable neural network substrate while approaching fundamental limits of time, space, and energy,” IBM Fellow Dharmendra Modha, chief scientist of Brain-inspired Computing at IBM Research, said in a blog post.
Intel isn’t standing still, and is developing its own chip architectures for next-generation AI workloads. Last year the company announced that its first AI-specific hardware, code-named “Lake Crest,” which is based on technology Intel acquired with $400-million acquisition of Nervana Systems, would debut in the first half of 2017. That is to be followed later this year with Knights Mill, the next iteration of its Xeon Phi co-processor architecture.
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Google’s new Tensor Processing Unit (TPU)
CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads.
Google has been using its TPUs for the inference stage of a deep neural network since 2015. It credits the TPU for helping to bolster the effectiveness of various artificial intelligence workloads, including language translation and image recognition programs. It also says TPU helped power its widely reported victory in the game of Go.
While TPUs aren’t new to Google data centers, the company started talking about them publicly only recently. Earlier this month, the Alphabet subsidiary opened up about the TPU, which it called “our first machine learning chip,” in a blog post. The company also released a technical paper, titled “In-Datacenter Performance Analysis of a Tensor Processing Unit,” that details the design and performance characteristics of the TPU.
According to the paper, Google’s TPU was 15 to 30 times faster at inference than NVidia’s K80 GPU and Intel Haswell CPU in a Google benchmark test. On a performance per watt scale, the TPUs are 30 to 80 times more efficient than the CPU and GPU (with the caveat that these are older designs). You can read more details on the TPU comparisons over at HPCwire.
While Google has been mum on possible commercial ventures around the TPU, some recent developments indicate that Google itself may not be aiming to compete directly with traditional chip manufacturers. Last week CNBC reported that a group of the original Google engineers who designed the TPU recently left the Web giant to found their own company, called Groq.
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Nvidia CEO Jensen Huang has confirmed that an upcoming iteration of the company’s server family will be liquid cooled. Huang let slip the detail during a presentation at the 2024 SIEPR Economic Summit at Stanford, but is likely to officially announce the new GPU server system at the company’s GTC event from March 18. Mar 10, 2024
Liquid cooling is better for more demanding tasks. This kind of cooling involves a closed-loop system with a pump circulating coolant for efficient heat transfer. Liquid cooling is favored by both hardcore gamers and enthusiasts for its sleek aesthetics and high thermal load handling.
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Tesla Technology:
HW4 better than HW3?
While the images are impressive, the next set is how the camera technology will translate into improved Full Self-Driving capabilities. Elon Musk has hinted that HW4-equipped cars could be 3 to 5 times more adept at autonomous driving. We know that HW4 has more ports for additional cameras as well.
Sep 2, 2023
Why is Tesla building a supercomputer? The Dojo supercomputer is expected to be able to process massive volumes of sensor data to help train AI on real-world driving footage.
Tesla currently has 35,000 H100 chips — the most powerful and super-expensive graphics processing units designed for AI applications — Musk revealed during the company’s first quarterly earnings for 2024.
Apr 24, 2024
How powerful is a Tesla GPU? The monster computer found in every Tesla built since October 2016 is the Nvidia computer used by the Autopilot and Self Driving systems.
Its two GPUs are capable of 25 Trillion operations per second and require liquid cooling from the car’s cooling system.
Nov 12, 2021
Beyond Tesla’s Full Self-Driving (FSD) beta systems, Dojo is expected to have several potential applications, being one of the world’s most prolific computing clusters.
Jan 27, 2024
On Tesla’s Q4 conference call, CEO Elon Musk mentioned plans for additional Dojo computers, discussing Dojo 1.5, Dojo 2, Dojo 3, and more if the investment continues to pay off.
Who supplies Tesla AI chips? Nvidia makes the GPUs for Tesla’s Dojo Computer
These hardware pieces are perfect for crunching loads of data, which is necessary when creating an AI model. Nvidia (NVDA 0.81%) supplied the GPUs for the first Dojo computer, and with its best-in-class GPUs, it will benefit from the sales of thousands more.
Feb 7, 2024
NVIDIA Tesla A100
The Tesla A100 is meant to be scaled to up to thousands of units and can be partitioned into seven GPU instances for any size workload. Each Tesla A100 provides up to 624 teraflops performance, 40GB memory, 1,555 GB memory bandwidth, and 600GB/s interconnects.
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Graphics processing unit (GPU) computing is the process of offloading processing needs from a central processing unit (CPU) in order to accomplish smoother rendering or multitasking with code via parallel computing.
GPU compute better than CPU?
A graphics processing unit (GPU) is a similar hardware component but more specialized. It can more efficiently handle complex mathematical operations that run in parallel than a general CPU.
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There a shortage of data centers? The frenzy to build data centers to serve the exploding demand for artificial intelligence is causing a shortage of the parts, property and power that the sprawling warehouses of supercomputers require. The lead time to get custom cooling systems is five times longer than a few years ago, data center executives say.
Apr 24, 2024
What city has the most data centers?
Boasting more than 250 data centers, Northern Virginia (NOVA) is widely recognized as the data center capital of the world – for good reason.
How many employees does a typical data center have?
Data centers tend to be relatively low on employment. Typical headquarters, manufacturing, or shared service operations can have between 200 and 1,000 jobs on site. By comparison, the number of jobs at a typical data center can be anywhere between five and 30.
How much electricity do data centers consume? Data centers switch on to AI
While the hyperscalers typically need 10-14kW per rack in existing data centers, this is likely to rise to 40-60kW for AI-ready racks equipped with resource-hungry GPUs. This means that overall consumption of data centers across the US is likely to reach 35GW by 2030, up from 17GW in 2022.
Jan 15, 2024
What is the most beautiful data center in the world? MareNostrum 4, the main supercomputer of Barcelona Supercomputing Center (BSC), has won the prize to the Most Beautiful Data Center in the World, organized by Datacenter Dynamics (DCD).