In 2024, Nvidia, posted the largest single daily gain of market cap by a stock in history, the largest single daily loss of market cap by a stock in history. We truly are witnessing history? /@KobeissiLetter/
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Nvidia's after-hours Form 8-K filing is impeccably timed. Nvidia understands that submitting such a form can significantly impact their stock price. So, what’s the strategy? The event in question (the AI Diffusion Framework) was officially announced on January 13, making the deadline for submission January 17
grok.com: [Not verified; Do your own research] The strategy behind Nvidia's timing of their Form 8-K filing after the announcement of the AI Diffusion Framework involves several considerations aimed at minimizing the immediate impact on their stock price:
Timing Relative to Market Closure: By filing the 8-K after market hours, Nvidia ensures that the immediate reaction to the news does not affect the stock price in real-time trading. This gives them control over when the market reacts to the news, potentially allowing them to manage investor reactions more strategically.
Long Weekend Effect: Filing just before a long weekend, particularly when Monday is a non-trading day due to a holiday, effectively extends the time before the market can fully react to the news. Over a long weekend, the attention of investors might be diluted by other news or events, reducing the intensity of market reactions when trading resumes. This can help in dispersing the impact over a longer period rather than a sharp drop or rise in one trading session.
Compliance with SEC Regulations: As you noted, under SEC regulations (17 CFR § 240.13a-11 and 17 CFR § 249.308), Nvidia had until January 17 to file the 8-K after the event was triggered on January 13. By filing at the end of this window, they utilize the full legal timeframe to assess and react to market conditions or other unfolding events before disclosure, thus possibly aligning the release with other favorable news or market conditions.
Information Absorption: Over a non-trading period, investors and analysts have additional time to digest the information, potentially leading to a more measured response rather than a knee-jerk reaction. This might be beneficial if the initial reaction to the news could be negative, as it allows for a more nuanced understanding of the implications.
Market Sentiment Management: The timing can also be seen as an attempt to manage or shape market sentiment. If the news is perceived as potentially negative, releasing it when trading is closed might help in reducing the immediate panic selling. Conversely, if the news had positive aspects, ensuring it's released when it can have a sustained impact over several days might be beneficial.
Controlled Release of Information: Nvidia might have aimed to coordinate this disclosure with other company announcements or industry events to counterbalance any negative news or to leverage positive news more effectively.
Therefore, the strategy appears to involve using regulatory timing windows, market psychology, and the natural ebb and flow of news cycles to minimize adverse impacts or to strategically position the company's stock price reaction. This approach reflects an understanding that information timing can be as crucial as the content of the information itself in the stock market.
+ Related content:
@kakashiii111: Nvidia's after-hours Form 8-K filing is impeccably timed. Nvidia understands that submitting such a form can significantly impact their stock price. So, what’s the strategy? According to 17 CFR § 240.13a-11 and 17 CFR § 249.308, public companies have four business days to file an 8-K after the occurrence of an event triggering its disclosure requirements. The event in question (the AI Diffusion Framework) was officially announced on January 13, making the deadline for submission January 17. What’s the best way to minimize stock impact, especially with Monday being a non-trading day, creating a long weekend where news may lose momentum?
File at the last possible moment, right before the end of the trading day on January 17, and let the SEC publish it within 24 hours, likely by Friday after hours.
Brilliant! Kudos to Nvidia's lawyers! (I heard it's Cooley—feel free to confirm or correct).
Now, let’s examine the content of the 8-K. Nvidia addresses two main points: the “AI Diffusion” Rule and the “Additional Due Diligence Measures” Rule. Regarding the latter, Nvidia claims it will have no material impact on their day-to-day business (a statement that’s partially accurate and open to interpretation). The former, however, is more impactful, so how do they craft it to avoid investor attention? By writing it in technical language, ensuring it doesn't explicitly indicate any direct effect on the business.
As the saying goes: Tell me there’s a material change that will impact the future of the business without telling me there’s a material change that will impact the business
@DarioCpx: This NVIDIA 8-K dropped after market close on Friday is such a sloppy attempt to play on investors ignorance of its business.
The new rules announced by the US the 13 Jan are aimed to shut the loophole so far exploited by NVIDIA to use 3rd party countries in order to circumvent selling restrictions into specific countries (and to fabricate a good deal of fake revenues in the process too).
So declaring that the new rules won’t have any impact towards its business “in China” it’s an egregious insult to people’s intelligence besides being a blatant lie and - surprise surprise - if you take the magnifier and read the chunky footnote at the bottom of the second page you can see how the disclaimer sneaked in correctly categorises that sentence as a “forward looking statement” that in layman terms means “empty bs”
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☑️ #199 Jan 7, 2024
DIGITS and more
@BloombergTechnology: Nvidia CEO Jensen Huang talks about the future of artificial intelligence, their new lineup of chips, which disappointed some investors, autonomous cars, robotics, Elon Musk, and how he'd like to meet President-elect Donald Trump and offer his help to the incoming administration. He speaks to Bloomberg's Ed Ludlow at CES in Las Vegas on "Bloomberg Technology."
+ Related content:
@EdLudlow: Think Jensen clarified that Project DIGITS is compatible with any computer platform (Linux, MacOS, Windows) during that conversation but any follow ups?
Introducing Project R2X | A Preview of a RTX-Powered Digital Human Interface
@NVIDIA: Introducing Project R2X, a preview of an RTX-powered digital human interface for developers & enthusiasts. R2X can help assist in various apps, analyze complex documents, create custom workflows, optimize PC settings, perceive the world around you, and more. Learn more:
run.ai: [Excerpt] We will continue to help our customers to get the most out of their AI Infrastructure and offer the ecosystem maximum flexibility, efficiency and utilization for GPU systems, wherever they are: On-Prem, in the cloud through native solutions, or on NVIDIA DGX Cloud, co-engineered with leading CSPs.
blogs.nvidia.com: [Excerpt] From Generative to Agentic AI, Wrapping the Year’s AI Advancements.
Unlocking Productivity and Creativity With AI-Powered Chatbots.
Introducing RTX-Accelerated Partner Applications.
Agentic AI — Enabling Complex Problem-Solving.
AI Decoded Wrapped.
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☑️ #194 Dec 17, 2024 🟠 opinion
GB200 Rack Supply Chain Requires Further Optimization, Peak Shipments Expected Between 2Q25 and 3Q25, Says TrendForce
trendforce.com: [Excerpt] As the market closely follows the progress of NVIDIA’s GB200 rack-mounted solution, TrendForce’s latest research indicates that the supply chain requires additional time for optimization and adjustment. This is largely due to the higher design specifications of the GB200 rack, including its requirements for high-speed interconnect interfaces and thermal design power (TDP), which significantly exceed market norms. Consequently, TrendForce projects that mass production and peak shipments are unlikely to occur until between Q2 and Q3 of 2025.
The NVIDIA GB rack series, which includes the GB200 and GB300 models, features more complex technology and higher production costs, making it a preferred solution for large CSPs. Other potential users include Tier-2 data centers, national sovereign cloud providers, and academic research institutions engaged in HPC and AI applications. The GB200 NVL72 is expected to become the most widely adopted model in 2025, potentially accounting for up to 80% of total deployments as NVIDIA ramps up its market push.
nvidia.com (3/18/24): [Excerpt] NVIDIA Blackwell Platform Arrives to Power a New Era of Computing.
The GB200 is a key component of the NVIDIA GB200 NVL72, a multi-node, liquid-cooled, rack-scale system for the most compute-intensive workloads. It combines 36 Grace Blackwell Superchips, which include 72 Blackwell GPUs and 36 Grace CPUs interconnected by fifth-generation NVLink. Additionally, GB200 NVL72 includes NVIDIA BlueField®-3 data processing units to enable cloud network acceleration, composable storage, zero-trust security and GPU compute elasticity in hyperscale AI clouds. The GB200 NVL72 provides up to a 30x performance increase compared to the same number of NVIDIA H100 Tensor Core GPUs for LLM inference workloads, and reduces cost and energy consumption by up to 25x.
The platform acts as a single GPU with 1.4 exaflops of
developer.nvidia.com (3/18/24): NVIDIA GB200 NVL72 Delivers Trillion-Parameter LLM Training and Real-Time Inference.
Fifth-generation NVLink and NVLink Switch System.
The NVIDIA GB200 NVL72 introduces fifth-generation NVLink, which connects up to 576 GPUs in a single NVLink domain with over 1 PB/s total bandwidth and 240 TB of fast memory. Each NVLink switch tray delivers 144 NVLink ports at 100 GB so the nine switches fully connect each of the 18 NVLink ports on every one of the 72 Blackwell GPUs.
The revolutionary 1.8 TB/s of bidirectional throughput per GPU is over 14x the bandwidth of PCIe Gen5, providing seamless high-speed communication for today’s most complex large models.
Source: NVIDIA Corporation
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☑️ #193 Dec 17, 2024
Verizon collaborates with NVIDIA to power AI workloads on 5G private networks with Mobile Edge Compute
verizon.com: [Excerpt] Verizon's reliable, secure private 5G network and Private Mobile Edge Compute (MEC) with NVIDIA AI combine to deliver powerful, real-time AI services at the edge, empowering customers through innovation.
The new AI powered private 5G platform stack is a Verizon and NVIDIA developed infrastructure designed to be plug & play, helping third-party developers to innovate with speed, while also accommodating future evolutions in AI computing and a variety of AI and connectivity applications. It can support multi-tenancy for multiple use cases or customers, is modular to be able to scale as needed for a bespoke solution for various applications, and can provide these services remotely via portable private network solutions or on a customer’s premise with a permanent private network on site. The stack is being built to handle compute intensive apps including Generative AI Large Language Models and Vision Language Models, Video streaming, broadcast management, Computer Vision (CV), Augmented/Virtual/Extended Reality (AR/VR/XR), Autonomous Mobile Robot/ Automated Guided Vehicle (AMR/AGV), and IoT.
NVIDIA Unveils Its Most Affordable Generative AI Supercomputer
blogs.nvidia.com: [Excerpt] The Jetson Orin Nano Super delivers up to a 1.7x gain in generative AI performance, supporting popular models for hobbyists, developers and students.
NVIDIA is taking the wraps off a new compact generative AI supercomputer, offering increased performance at a lower price with a software upgrade.
The new NVIDIA Jetson Orin Nano Super Developer Kit, which fits in the palm of a hand, provides everyone from commercial AI developers to hobbyists and students, gains in generative AI capabilities and performance. And the price is now $249, down from $499.
Available today, it delivers as much as a 1.7x leap in generative AIinference performance, a 70% increase in performance to 67 INT8 TOPS, and a 50% increase in memory bandwidth to 102GB/s compared with its predecessor.
Processor and memory communications in a stacked memory system
@seti_park: @NVIDIA appears to have developed a groundbreaking new GPU architecture that achieves a 50x improvement in memory performance while reducing energy consumption by a factor of 10!
This dramatic performance leap is made possible by moving away from traditional planar architectures to an innovative design that vertically stacks and directly connects processors with memory. These improvements in memory performance and energy efficiency are particularly significant for AI and HPC applications.
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☑️ #190 Dec 10, 2024
China launched an antitrust investigation. But why?
@yicaichina: Nvidia is willing to answer any questions regulators may have about its business, the US chipmaker told Yicai today after China’s market regulator placed it under investigation for alleged violations of anti-monopoly laws for the acquisition of Israeli tech firm Mellanox Technologies. @nvidia
+ Related content:
yicaiglobal.com (12/10/24): [Excerpt] Nvidia Says US Chip Giant Happy to Address Inquiries From China’s Antitrust Probe.
China’s State Administration for Market Regulation said yesterday that it had placed Santa Clara-based Nvidia under investigation for allegedly violating the country’s anti-monopoly law in relation to its acquisition of Mellanox Technologies, an Israeli-US maker of computer networking equipment, five years ago.
SiPh needs to ramp in volume. NV would only pursue this if they could guarantee 1m+ SiPh connections a month.
Stacking creates thermals. Materials research needs to happen before we think about logic on logic on logic. Cooling might be intra-chip at that point.
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☑️ #188 Dec 6, 2024
A really insightful and valuable interview with a Data network expert on Nvidia
@RihardJarc: A really insightful and valuable interview with a Data network expert on $NVDA (InfiniBand, CUDA), Pytorch & custom ASICs. A must-read!
Because of how AI data training works, there is a lot of east-west traffic flow. There is a need to have low latency and a very optimized network because any drop or impact on network connectivity between the GPUs means that the whole AI training workflow must restart or take longer to complete. That is why $NVDA's InfiniBand is very useful, as it has low latency and works well with $NVDA GPUs.
However, the Ultra Ethernet Consortium, which is made up of large companies like $GOOGL, $AMZN, and even $NVDA, made a solution to solve this problem on the Ethernet network. It is called RDMA RoCE v2, and according to him, it gives you similar performances as InfiniBand, but it is still based on Ethernet.
He mentions that you are seeing an increase in adoption of this new protocol with companies like $META and others as companies want to move away from InfiniBand because it is more expensive and a closed technology.
New chipsets (Tomahawk chipsets) that are coming and are produced by $AVGO support the RDMA RoCE v2 Ethernet protocol and will allow hyperscalers to be able to use it in the networking of data centers rather than InfiniBand.
$NVDA just recently joined the Ultra Ethernet Consortium, and in a way, it feels like they are ok with losing revenue on InfiniBand if that means they are able to sell more GPUs faster.
He doesn't see the whole high compute workload leaning towards $NVDA, as he believes you have a lot of use cases where customers will instead choose alternative chips. Especially if performance and speed are not the primary drivers, but costs are.
Hyperscalers also want different options, not just $NVDA, as they don't want a vendor locking.
He thinks some customers are using PyTorch to manage a hybrid solutions environment, for example, with both $NVDA and $AMD with CUDA and ROCm underneath. But with this, he does see the potential problem. If PyTorch becomes a thread, $NVDA could cut off the APIs or not have great support for that integration, which could again push customers back towards a lower layer having to know languages like CUDA and ROCm.
To me, this shows the first steps of how the industry is trying to hedge away from $NVDA's most significant sticky assets, InfiniBand and CUDA
+ Related content:
@RihardJarc: If you found this insightful, feel free to share & follow, as I often post content like this. Full interview on @AlphaSenseInc:
alpha-sense.com/uncoveralpha: [Excerpt] Endorsed by UncoverAlpha, a leading source of market analysis, AlphaSense is a key tool providing access to proprietary content, company data, and expert interviews with industry specialists. It offers a comprehensive market intelligence platform that delivers deep insights from company documents, integrating expert research, news, regulatory content, and AI-driven analysis to empower investment professionals to make well-informed decisions. That’s why 85% of the S&P 100 rely on AlphaSense for confident, data-backed decision-making.
NVIDIA to establish AI research and development center in Viet Nam
en.baochinhphu.vn: [Excerpt] VGP - Prime Minister Pham Minh Chinh on Thursday witnessed the signing of an agreement between the Vietnamese Government and NVIDIA on the establishment of an artificial intelligence (AI) research and development center and an AI data center in Viet Nam.
The establishment of NVIDIA's AI Research and Development Center and Viettel's AI Data Center that utilizes technologies provided by NVIDIA, will serve as a crucial foundation to promote the research and development of advanced AI technologies in Viet Nam, he noted.
viettel.com.vn (6/30/22): [Excerpt] Viettel enters AI collaboration with NVIDIA.
Viettel is the first Vietnam company and one of five in Asia to officially establish a strategic partnership with NVIDIA involving AI initiatives. NVIDIA is a global leader in AI hardware and software from edge to cloud computing. The company’s technologies are used in 70% of the world's top 500 fastest supercomputers.
Viettel will join NVIDIA’s Partner Network, a global ecosystem of leading companies across industries, and expect to benefit from NVIDIA's expertise in opportunities for machine learning (ML) and AI research, ML/AI industry collaborations, and other strategic engagements.
blogs.nvidia.com (update; 12/6/24): [Excerpt] Thailand and Vietnam Embrace Sovereign AI to Drive Economic Growth.
During his visit to the region, Huang also joined Bangkok-based cloud infrastructure company SIAM.AI Cloud onstage for a fireside chat on sovereign AI. In Vietnam, he announced NVIDIA’s collaboration with the country’s government on an AI research and development center— and NVIDIA’s acquisition of VinBrain, a health technology startup funded by Vingroup, one of Vietnam’s largest public companies.
Sovereign AI refers to a nation’s capabilities to produce artificial intelligence using its own infrastructure, data, workforce and business networks.
blogs.nvidia.com (2/12/24): [excerpt] NVIDIA CEO: Every Country Needs Sovereign AI.
Jensen Huang describes transformative potential of sovereign AI at World Governments Summit.
Source: NVIDIA Corporation
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☑️ #185 Dec 4, 2024
Schneider Electric Announces New Solutions to Address the Energy and Sustainability Challenges Spurred by AI
se.com: [Excerpts] Schneider Electric, the leader in the digital transformation of energy management and automation, has today accelerated its end-to-end AI-ready data center solutions with new announcements that address the urgent energy and sustainability challenges driven by high demand for AI systems.
The first part of the announcement is a new data center reference design, co-developed with NVIDIA, which will support liquid-cooled, high-density AI clusters of up to 132 kW per rack. Optimized for NVIDIA’s GB200 NVL72 and Blackwell chips, the design streamlines planning and deployment with proven, validated architectures, addressing the unique challenges of utilizing liquid cooling at-scale.
Latest announcements bolster company’s mission to decarbonize digital infrastructure, while enabling customers to deploy more sustainable, AI-ready data center solutions anywhere, at scale.
New GB200 NVL72 reference design, co-developed with NVIDIA, provides a proven, validated, and highly energy efficient architecture to support NVIDIA’s new Blackwell chip.
Introduces Galaxy VXL UPS, the industry’s most compact, high-density power protection system for AI, data center, and large-scale electrical workloads.
se.com (3/18/24): [Excerpt] Schneider Electric Collaborates with NVIDIA on Designs for AI Data Centers. New reference designs will offer a robust framework for implementing NVIDIA’s accelerated computing platform within data centers. Designs will optimize performance, scalability, and energy efficiency
Schneider Electric, the leader in the digital transformation of energy management and automation, today announced a collaboration with NVIDIA to optimize data center infrastructure and pave the way for groundbreaking advancements in edge artificial intelligence (AI) and digital twin technologies.
Schneider Electric will leverage its expertise in data center infrastructure and NVIDIA’s advanced AI technologies to introduce the first publicly available AI data center reference designs. These designs are set to redefine the benchmarks for AI deployment and operation within data center ecosystems, marking a significant milestone in the industry's evolution.
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☑️ #184 Dec 2, 2024
Nebius announces oversubscribed strategic equity financing of USD 700 million to accelerate roll-out of full-stack AI infrastructure
group.nebius.com: [Excerpt] Nebius announces oversubscribed strategic equity financing of USD 700 million to accelerate roll-out of full-stack AI infrastructure.
The AI-native Nebius GPU cloud is designed to manage the full ML lifecycle – from data processing and training through to fine-tuning and inference – all in one place. The recently launched Nebius AI Studio inference service expands the Company’s offering to app builders, with access to a range of state-of-the-art open-source models in a flexible, user-friendly environment at among the lowest price-per-token on the market.
+ Continue reading
+ Related content:
pitchbook.com (update; 12/3/24): Global investor news for December 3, 2024.
Nebius, a Nasdaq-listed AI infrastructure company formerly known as Yandex NV, raised $700 million from investors including Nvidia, Accel and Orbis for expanding into the US.
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☑️ #183 Dec 2, 2024 🔴 rumor
Production Hurdles for GB200 Spark Rumors of Microsoft Cutting Orders
GB200 chip production delayed until March 2025 due to technical hurdles.
Poor testing yields for cartridge connectors causing bottlenecks.
$MSFT cuts NVIDIA orders by 40%, reallocating to GB300 chips
+ Related content:
ctee.com.tw: [Excerpt] [Translated] GB200 mass production card level, rumored that Microsoft cut orders.
The supply chain revealed that this problem comes from the American merchant factory. In order to connect 72 Blackwell GPUs through 5,000 NVLink copper cables for high-speed interconnection, the new cartridge connector module has thousands of wires in each cartridge. The GH200 specification reaches 112G per one, while the GB200 specification is expected to be upgraded to 224G, which is greatly improved in difficulty. Now it faces a bottleneck of poor yield and poor testing.
@dnystedt: Nvidia is expected to launch the next-generation GB300 platform in mid-2025 with better performance than GB200 AI servers and fully liquid cooled, media report, adding a number of thermal solutions firms, including Auras Tech and Asia Vital Components, are expected to benefit. $NVDA#semiconductors#semiconductormoney.udn.com/money/story/11074/8396804
money.udn.com (): [Excerpt] [Translated] GB300 will be released next year with Wang Shuanghong.
NVIDIA's latest GB200 AI server is currently being shipped one after another. The next generation GB300 is expected to be released in the middle of next year. The market estimates that the power consumption of the new GB300 platform is bound to be further expanded. At that time, the role of water cooling and heat dissipation will be more important, and even "full water cooling solution" must be used. Only then can the server operate normally, which is expected to promote the surge in demand for water cooling and heat dissipation, with Wang Shuanghong (3324), Qiqi and other Taiwan factories.
Judging from the cost of the AI server cabinet, the average price of the GB200 NVL36 server cabinet is about 1.8 million US dollars, while the cost of NVL72 is about 3 million US dollars. Once the water leaks, the whole expensive server cabinet is likely to be reimbursed. Water cooling and heat dissipation are essential components. Major Cloud service providers (CSP) would rather spend more money to improve quality than take any risks.
It is reported that at present, Shuanghong water-cooled panels are mainly supplied to Honghai and Guangda. After the penetration rate of GB200 expands next year, Shuanghong operation is expected to take off again.
Source: Lianhe News Department
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☑️ #182 Dec 2, 2024
The Rise of Nvidia
@EricFlaningam: Nvidia may be history's best case study in preparing for a new wave of technology.
Over the last 30 years, Nvidia:
Invented the GPU and rose to the top of the market (against nearly 100 competitors).
Enabled general-purpose computing on those GPUs with CUDA.
Built systems (networking, servers, and software) catered towards high-performance computing.
When AI finally came into the spotlight (after decades of research), Nvidia was the best-positioned company in the world.
A timeline with some of those key moments below (full article linked in comments):
asia.nikkei.com: [Excerpt] TAIPEI -- Server makers are scrambling to prepare for the possibility that Donald Trump will follow through on his vow to slap a 25% tariff on all goods imported from Mexico, a nation that has emerged as a key manufacturing hub for suppliers to major players like Nvidia, AMD and Intel.
Some companies are ramping up production capacity on American soil while others say they are pausing planned construction in Mexico, after Trump said on Monday that he will impose the tariff on his first day back in office. He said the 25% levy will also apply to Canadian imports, while Chinese goods will face an additional 10% tariff.
A source from one of the two companies' suppliers said that "we have quickly started to calculate how many components and parts we might need to produce in the US in order to ... meet [Trump's] country of origin requirements."
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☑️ #179 Nov 23, 2024 🟠 opinion
NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever
@deedydas: NVIDIA's $7B Mellanox acquisition was actually one of tech's most strategic deals ever. The untold story of the most important company in AI that most people haven't heard of
1/12
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2/12: Most people think NVIDIA = GPUs. But modern AI training is actually a networking problem. A single A100 can only hold ~50B parameters. Training large models requires splitting them across hundreds of GPUs.
3/12: Enter Mellanox. They pioneered RDMA (Remote Direct Memory Access) which lets GPUs directly access memory on other machines with almost no CPU overhead. Before RDMA, moving data between GPUs was a massive bottleneck.
4/12: The secret sauce is in Mellanox's InfiniBand. While Ethernet does 200-400ns latency, InfiniBand does ~100ns. For distributed AI training where GPUs constantly sync gradients, this 2-3x latency difference is massive.
5/12: Mellanox didn't just do hardware. Their GPUDirect RDMA software stack lets GPUs talk directly to network cards, bypassing CPU & system memory. This cuts latency another ~30% vs traditional networking stacks.
6/12: NVIDIA's master stroke: Integrating Mellanox's ConnectX NICs directly into their DGX AI systems. The full stack - GPUs, NICs, switches, drivers - all optimized together. No one else can match this vertical integration.
7/12: The numbers are staggering: - HDR InfiniBand: 200Gb/s per port - Quantum-2 switch: 400Gb/s per port - End-to-end latency: ~100ns - GPU memory bandwidth matching: ~900GB/s.
8/12: Why it matters: Training SOTA scale models requires: - 1000s of GPUs - Petabytes of data movement - Sub-millisecond latency requirements Without Mellanox tech, it would take literally months longer.
9/12: The competition is playing catch-up: - Intel killed OmniPath - Broadcom/Ethernet still has higher latency - Cloud providers mostly stuck with RoCE NVIDIA owns the premium AI networking stack.
10/12: Looking ahead: CXL + Mellanox tech will enable even tighter GPU-NIC integration. We'll see dedicated AI networks with sub-50ns latency and Tb/s bandwidth. The networking advantage compounds.
11/12: In the AI arms race, networking is the silent kingmaker. NVIDIA saw this early. The Mellanox deal wasn't about current revenue - it was about controlling the foundational tech for training next-gen AI.
12/12: Next time you hear about a new large language model breakthrough, remember: The GPUs get the glory, but Mellanox's networking makes it possible. Sometimes the most important tech is invisible.
+ Related content:
nvidianews.nvidia.com (3/11/19): [Excerpt] NVIDIA to Acquire Mellanox for $6.9 Billion. Datacenters in the future will be architected as giant compute engines with tens of thousands of compute nodes, designed holistically with their interconnects for optimal performance.
An early innovator in high-performance interconnect technology, Mellanox pioneered the InfiniBand interconnect technology, which along with its high-speed Ethernet products is now used in over half of the world’s fastest supercomputers and in many leading hyperscale datacenters.
With Mellanox, NVIDIA will optimize datacenter-scale workloads across the entire computing, networking and storage stack to achieve higher performance, greater utilization and lower operating cost for customers.
nvidianews.nvidia.com (4/27/20): [Excerpt] NVIDIA Completes Acquisition of Mellanox, Creating Major Force Driving Next-Gen Data Centers. Eyal Waldman, founder and CEO of Mellanox, said: “This is a powerful, complementary combination of cultures, technology and ambitions. Our people are enormously enthusiastic about the many opportunities ahead. As Mellanox steps into the next exciting phase of its journey, we will continue to offer cutting-edge solutions and innovative products to our customers and partners. We look forward to bringing NVIDIA products and solutions into our markets, and to bringing Mellanox products and solutions into NVIDIA’s markets. Together, our technologies will provide leading solutions into compute and storage platforms wherever they are required.
Source: NVIDIA Corporation
nvidianews.nvidia.com (11/16/20): [Excerpt] NVIDIA Announces Mellanox InfiniBand for Exascale AI Supercomputing.Today’s announcement builds on Mellanox InfiniBand’s lead as the industry’s most robust solution for AI supercomputing. The NVIDIA Mellanox NDR 400G InfiniBand offers 3x the switch port density and boosts AI acceleration power by 32x. In addition, it surges switch system aggregated bi-directional throughput 5x, to 1.64 petabits per second, enabling users to run larger workloads with fewer constraints
Source: NVIDIA Corporation
investor.nvidia.com (8/23/23): [Excerpt] NVIDIA Announces Financial Results For Second Quarter Fiscal 2024. NVIDIA GPUs connected by our Mellanox networking and switch technologies and running our CUDA AI software stack make up the computing infrastructure of generative AI.
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☑️ #178 Nov 22, 2024
Jensen Huang for the 1st time named several supply chain partners in Taiwan
@dnystedt: Nvidia CEO Jensen Huang for the 1st time named several supply chain partners in Taiwan, media report, including semiconductor packaging and testing firms, SPIL (Siliconware Precision, a subsidiary of ASE Technology), and testing firm KYEC (King Yuan Electronics Co.), in addition to TSMC and AI server and systems makers Foxconn, Quanta Computer, Wiwynn. $TSM$NVDA$ASX$HXSCL$MU#semiconductor#semiconductorshttps://money.udn.com/money/story/5612/8376680
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@dnystedt: Note: Nvidia’s CEO also named SK Hynix and Micron Technology, both suppliers of HBM (high bandwidth memory) chips to Nvidia. He did not name Samsung Electronics, a possible indication it still has not yet passed Nvidia’s verification process.
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@dnystedt: Nvidia CEO Jensen Huang (from Q3 call): “And we've got great partners, everybody from, of course, TSMC and Amphenol, the connector company, incredible company; Vertiv and SK Hynix and Micron; SPIL, Amkor; KYEC; and there's Foxconn and the factories that they've built; and Quanta and Wiwynn; and, gosh, Dell and HP, and Super Micro, Lenovo. And the number of companies is just really quite incredible. Quanta. And I'm sure I've missed partners that are involved in the ramping of Blackwell, which I really appreciate.” https://fool.com/earnings/call-transcripts/2024/11/20/nvidia-nvda-q3-2025-earnings-call-transcript/
Here are 10 critical things new investors often miss, based on my experiences:
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X As a hedge fund analyst at Millennium and Citadel, you are grilled and rewarded for your ability to drive alpha. Hedge Funds do diligence very differently than long-only and thematic funds. This is how they generate alpha. It all starts with a thesis and proving/disproving it.
X While it is important to have your investment idea be supported by a long term trend (e.g. AI) that is not what generates alpha. To generate alpha, you need to have a specific way you are going to get from point A to point B. Here is an example:
While the above will generally have to be true, they can't be as easily proven/disproven and may (and are often) already priced in.
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X Begin by developing an initial view and gather data points. Here is what to look for:
A. Blackwell: B100 vs. H100 order patterns, customer and supplier comments re capacity additions. $TSM$VRT$MSFT$DELL$HPE$SMCI Foxconn
B. Performance improvement (scaling laws): customers' comments on bottlenecks, their path for product development, etc. Follow @karpathy@elonmusk@lexfridman@AravSrinivas for data points:
X Once you develop a conceptual overview of the business and your thesis, next is to begin modeling or developing a process for quantifying everything.
X1. Start and end with the 10Q & 10K Read (at minimum) the last reported reports.
Important to read a blackline version! It shows changes that could flag issues.
Don't forget the Balance Sheet. Upside comes from the IS, but issues are revealed in the BS.
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X Very important to read the "Critical Audit Matters" section.
This is where auditors reveal where they had to make assumptions or judgment calls and relied on management.
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X2. Read transcripts from the last 3-4 qtrs of earnings calls. Specifically, pay attention to the Q&A section! The Q&A section generally reveals the key discussion/thesis points - opportunities and pain points. Look for volume, pricing, and margin comments.
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X3. Due diligence your assumptions (this is the most time-consuming and ongoing). Here are some ways:
Cross-check volume assumptions with $TSMC CoWos capacity additions and comments (up the value chain)
X5. PIE SHARING. Analyze company growth vs. competitors' growth and make reasonable assumptions.
ASICs are likely to take 1/2 of the CSP market ($AVGO estimates the entire market)
AMD is likely to get to ~10% market share.
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X 6. Begin putting your financial model based on: - IS - BS - CF statements I recommend 5Y back annually, 2Y quarterly and 2 yrs out projections.
Quarterly models let you see when a company has tough/easy comps.
In this case, revenue growth is the most important metric the market cares about ⬇️
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X7. Profitability framework - understand the unit economics and how each line item scales w/ growth (i.e. Op. leverage
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X8. Put together a table of comparable companies. Use multiples EV/EBITDA, EV/Revenue, PE and FCF yield to give context about where the company stands compared to competitors.
Multiples can also be helpful for timing your investment and determining a floor.
The Future of AI with NVIDIA Founder and CEO Jensen Huang
@wwt_inc : Watch WWT Co-Founder and CEO @jimpkavanaugh and @NVIDIA Founder and CEO Jensen Huang talk about the evolution and future of AI. During the discussion, Jim and Jensen will also provide practical tips for implementing #AI at scale within the enterprise.
CoreWeave Pushes Boundaries with NVIDIA GB200 NVL72 Cluster Bring-Up, New Cutting-Edge GPU Instances, and AI-Optimized Storage Solutions
coreweave.com: [Excerpt] Today, we are thrilled to announce several groundbreaking advancements that strengthen CoreWeave's position as one of the leading AI cloud services providers. CoreWeave is proud to be one of the first major cloud providers to bring up an NVIDIA GB200 NVL72 cluster, showcasing our continued commitment to pushing the boundaries of AI infrastructure. Additionally, we’re introducing new GPU instance types, including the NVIDIA GH200 Grace Hopper Superchip and NVIDIA L40, and L40S GPUs, that are now generally available. Rounding out these innovations is the preview of CoreWeave AI Object Storage, a next-generation cloud storage solution purpose-built for accelerated computing workloads. These updates strengthen CoreWeave’s role as the AI Hyperscaler™ and deliver a comprehensive suite of cloud services to empower AI labs and enterprises to scale their AI development like never before. In this blog, we’ll dive into the technical highlights of these offerings and explain how they integrate with CoreWeave’s managed cloud services to help customers accelerate their AI initiatives.
coreweave.com (8/28/24): CoreWeave Is the First Cloud Provider to Deploy NVIDIA H200 Tensor Core GPUs.
Mission Control
High-performance GPU infrastructure consists of numerous cutting-edge technologies coming together, such as the latest semiconductor process nodes, powerful compute chips, high bandwidth memory, high speed chip-to-chip interconnect and node-to-node connectivity. These technologies deliver an unprecedented amount of compute capabilities, with each generation providing 2-3X more raw compute power than the prior generation. This increase in performance is fundamental in pushing the capabilities of generative AI applications, but comes with additional overhead and complexities when dealing with node and system failures.
CoreWeave Mission Control offers customers a suite of services that ensure the optimal setup and functionality of hardware components through a streamlined workflow. It verifies that hardware is correctly connected, up to date, and thoroughly tested for peak performance. Advanced validation processes deliver healthier, higher-quality infrastructure to clients faster. Proactive health checking runs automatically when idle nodes are detected and swaps out problematic nodes for healthy ones before they cause failures. Extensive monitoring identifies hardware failures more quickly, shortening the amount of time to troubleshoot and restart training jobs, meaning interruptions are shorter and less expensive. The following image summarizes how CoreWeave’s Fleet LifeCycle Controller (FLCC) and Node LifeCycle Controller (NLCC) collectively work to provide AI infrastructure with industry-leading reliability and resilience, enabling us to build some of the largest NVIDIA GPU clusters in the world.
Source: CoreWeave, Inc.
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☑️ #174 Nov 18, 2024
IonQ to Advance Hybrid Quantum Computing with New Chemistry Application and NVIDIA CUDA-Q
ionq.com: [Excerpt] IonQ uses the NVIDIA CUDA-Q platform alongside IonQ Forte to demonstrate an end-to-end application workflow. The work reaffirms IonQ’s focus on making quantum acceleration as simple and ubiquitous as GPU acceleration for on-prem and hybrid deployments.
Since 2023, IonQ has supported NVIDIA CUDA-Q, a powerful, unified, open-source software stack. CUDA-Q is a hybrid quantum-classical computing platform that enables the integration and programming of QPUs and GPUs in a single workflow. This demonstration was performed using a combination of IonQ Forte, the IonQ Hybrid Services suite, CUDA-Q, and NVIDIA A100 Tensor Core GPUs, which can be deployed to cloud and on-prem environments.
@NVIDIA: Join NVIDIA Founder and CEO Jensen Huang, NVIDIA VP and GM for Hyperscale and HPC Ian Buck, Argonne National Laboratory Computational Science Leader Arvind Ramanathan, King Abdullah University of Science and Technology Professor of Applied Mathematics and Computational Sciences David Keyes, as they discuss the latest innovations in scientific computing on Monday, November 18 at 10:30 a.m. PT, 1:30 p.m. ET. Watch it online: https://www.nvidia.com/sc24
+ Related content:
blogs.nvidia.com: [Excerpts] [TL;DR]AI Will Drive Scientific Breakthroughs, NVIDIA CEO Says at SC24.
Expanding Production With Foxconn Collaboration As demand for AI systems like the Blackwell supercomputer grows, NVIDIA is scaling production through new Foxconn facilities in the U.S., Mexico and Taiwan.
Foxconn is building the production and testing facilities using NVIDIA Omniverse to bring up the factories as fast as possible.
theinformation.com: [Excerpt] Nvidia Customers Worry About Snag With New AI Chip Servers.
Nvidia is grappling with new problems related to its much-anticipated Blackwell graphics processing units for artificial intelligence: how to prevent them from overheating when connected together in the customized server racks it has designed. In recent months, Nvidia has asked its suppliers ...
@NVIDIA: Agentic AI is transforming every enterprise, using sophisticated reasoning and iterative planning to solve complex, multi-step problems. Learn how NVIDIA AI Blueprints help turn data into knowledge and knowledge into action by automating processes, tapping into real-time insights, and improving workflow efficiency at scale.
supremecourt.gov: [Excerpt] Oral Arguments - NVIDIA Corp. v. E. Ohman J:or Fonder AB (23-970).
Docket for 23-970: Title:NVIDIA Corporation, et al., Petitioners v. E. Ohman J:or Fonder AB, et al. No. 23-970 E. Ohman J:or Fonder AB, et al. United States Court of Appeals for the Ninth Circuit Application (23A578) to extend the time to file a petition for a writ of certiorari from February 13, 2024.
+ Related content:
reuters.com (11/4/24): [Excerpt] The Supreme Court on Nov. 13 is due to hear arguments in Nvidia's bid to scuttle a securities class action accusing the Santa Clara, California-based company of misleading investors about how much of its sales went to the volatile cryptocurrency industry.
The 2018 suit, led by the Stockholm-based investment management firm E. Ohman J:or Fonder AB, accused Nvidia of violating the Securities Exchange Act by making statements in 2017 and 2018 that falsely downplayed how much of the company's revenue growth came from crypto-related purchases.
A private right of action refers to the ability of a private person or group to sue for an alleged harm.
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☑️ #169 Nov 12, 2024
NVIDIA AI Summit Japan
nvidia.com: [Excerpts] NVIDIA AI Summit Japan—NVIDIA today announced a series of collaborations with SoftBank Corp. designed to accelerate Japan’s sovereign AI initiatives and further its global technology leadership while also unlocking billions of dollars in AI revenue opportunities for telecommunications providers worldwide.
SoftBank First to Receive Blackwell, Plans for Grace Blackwell
SoftBank plans to use its Blackwell-powered DGX SuperPOD for its own generative AI development and AI-related business, as well as that of universities, research institutions and businesses throughout Japan.
AI-RAN Reaches New Milestone
Working closely with NVIDIA, SoftBank has achieved a technology milestone — the development of a new kind of telecommunications network that can run AI and 5G workloads at the same time, known by the industry as artificial intelligence radio access network, or AI-RAN.
@markets: Huang, Son Joke About SoftBank's Early Stake in Nvidia.
blogs.nvidia.com (11/7/24): Jensen Huang to Discuss AI’s Future With Masayoshi Son at AI Summit Japan.
softbank.jp (update; 11/13/24): [Excerpt] Press Releases on AI-RAN Development. Recently, SoftBank achieved a high-performance, carrier-grade virtual Radio Access Network (vRAN) on the NVIDIA GH200 Grace Hopper Superchip. Additionally, SoftBank developed an orchestrator capable of operating both vRAN and AI on the same virtualized platform, thereby realizing the AI-RAN concept by providing vRAN and diverse AI applications on a GPU-based computing infrastructure. Furthermore, SoftBank has introduced this AI-RAN solution as "AITRAS" with plans for deployment within its commercial network and expansion to telecommunications operators domestically and internationally.
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☑️ #168 Nov 4, 2024
Wall Street creates $11bn debt market for AI groups buying NVidia chips
@StockMKTNewz: Nvidia $NVDA will be entering the Dow Jones as the ~22nd largest holding out of 30 representing ~2% of the index.
+ Related content:
spglobal.com (pdf): [Excerpt] NVIDIA and Sherwin-Williams Set to Join Dow JonesIndustrial Average; Vistra to Join Dow Jones Utility Average.
NEW YORK, November 1, 2024: S&P Dow Jones Indices will make the following changes to the Dow Jones Industrial Average (DJIA) and Dow Jones Utility Average (DJUA) effective prior to the open of trading on Friday, November 8:
NVIDIA Corp. (NASD:NVDA) will replace Intel Corp. (NASD:INTC)
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☑️ #166 Nov 1, 2024
H100 vs H200
@AethirCloud: We dive into the key differences between the H100 and H200 & how they will push AI to new heights. You'll find:
Inside the AI Chip Market: NVIDIA, TSMC, Custom ASICs
Uncover Alpha: []Excerpt] I am sharing my research findings on the AI semiconductor industry in this article. I analyzed more than 100 interviews from different industry insiders. Former employees from Nvidia, AMD, Amazon's AWS unit, Microsoft, Google, Apple, GlobalFoundries, Qualcomm, Intel, and smaller startup AI semi-companies such as Groq.
@nvidia (10/28/24): .@xAI's Colossus in Memphis, the world's largest AI supercomputer with 100,000 NVIDIA Hopper GPUs, achieves new heights with NVIDIA Spectrum-X Ethernet. A testament to NVIDIA's dedication to #AI progress. Read more: https://nvda.ws/4fnuuWG
+ Related content:
nypost.com (update; 11/1/24): [Excerpt] Chipmaking giant Nvidia in talks with Elon Musk over investing in xAI: source. Nvidia — which under CEO Jensen Huang last week surpassed Apple to become the world’s most valuable company with a market capitalization of more than $3.5 trillion — declined to comment when contacted by The Post.
The company had strongly denied similar rumors in the spring.
Musk is expecting in January to hold a major new fund-raising round that could value xAI at as much as $75 billion, two sources said.
It’s not unusual for chipmakers like Nvidia to co-invest with their customers on projects, according to industry insiders
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☑️ #163 Oct 25, 2024
The Future of Compute: NVIDIA's Crown is Slipping
Small Fish Big Pond: The AI crown lies on NVIDIA's head. Trends in AI demand, custom silicon, and distributed training look to unseat it.
Jensen Huang in Denmark: Blackwell was functional, but yield was low
@wallstengine: $NVDA CEO Jensen Huang says, "We had a design flaw in Blackwell chips... The Blackwell flaw was 100% Nvidia's fault." Adds, "TSMC helped recover from that and resume work at incredible pace."
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@khandeliagroup: At the launch of Denmark’s new supercomputer GEFION with 1528 Nvidia H100 GPUs. Looking forward to number crunching on the machine soon. Promising perspectives from the CEO of NVIDIA J. Huang in conversation with the CEO of DCAI Nadia Carlsten.
Taiwan Semiconductor Manufacturing Company (TSMC) uncovered the reported design flaw, which affects the processor die connecting two Blackwell GPUs on a single board.
@LuxAlgo (10/16/24): JUST IN: Relations are reportedly strained between Nvidia and their manufacturer, $TSM. Tensions are rising as engineers find the latest $NVDA Blackwell chip fails under data center conditions. The bull market is at stake!
@hedgevision: Druckenmiller says selling Nvidia was a "big mistake."
"I've made so many mistake in my investment career, one of them was I sold all my Nvidia probably somewhere between $800 and $950."
"Yes, I think Nvidia is a wonderful company and were the price to come down we'd get involved again. But right now, I'm licking my wounds from a bad sale there."
Says he owns 0 shares today compared to 6.17 million at the beginning of the year.
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☑️ #159 Oct 15, 2024
Dell AI Factory Transforms Data Centers with Advanced Cooling, High Density Compute and AI Storage Innovations
dell.com: [Excerpt] New integrated rack, server and storage enhancements power high-performance AI workloads.
Dell Technologies introduces AI-ready platforms designed for the Dell IR7000:
Part of the Dell AI Factory with NVIDIA, the Dell PowerEdge XE9712 offers high-performance, dense acceleration for LLM training and real-time inferencing of large-scale AI deployments. Designed for industry-leading GPU density with NVIDIA GB200 NVL72, this platform connects up to 36 NVIDIA Grace CPUs with 72 NVIDIA Blackwell GPUs in a rack-scale design. The 72 GPU NVLinkdomain acts as a single GPU for up to 30x faster real-time trillion-parameter LLM inferencing. The liquid cooled NVIDIA GB200 NVL72 is up to 25x more efficient than the air-cooled NVIDIA H100-powered systems.
US Weighs Capping Nvidia, AMD AI Semiconductor Sales to Some Countries
@markets: Bloomberg has learned that officials in the Biden administration have discussed capping sales of advanced AI chips from Nvidia Corp. and other American companies on a country-specific basis. Annabelle Droulers reports on Bloomberg Television.
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☑️ #157 Oct 14, 2024
R&D expenses
@techfund1$NVDA is widening the R&D gap with its closest competitor. Annualizing last quarter's R&D spend, NVDA is investing now almost at twice the level of $AMD. And the latter will have to split R&D between GPUs, CPUs and FPGAs, whereas NVDA can fully focus on building out the AI stack.
🔹Related content:
amd.com (10/10/24): [Excerpt] AMD Delivers Leadership AI Performance with AMD Instinct MI325X Accelerators. Built on the AMD CDNA™ 3 architecture, AMD Instinct MI325X accelerators are designed for exceptional performance and efficiency for demanding AI tasks spanning foundation model training, fine-tuning and inferencing. Together, these products enable AMD customers and partners to create highly performant and optimized AI solutions at the system, rack and data center level.
u/textcortex: Dear community I am currently exploring the feasibility of replacing Nvidia GPUs with AMD GPUs for LLM inference. Can anyone share their experience or point me to any relevant research on the performance differences between these GPUs for this task? Are there any particular hardware or software limitations that may affect the feasibility of such a switch? Thank you for your insights!
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u/cjbprime: Since the amount of VRAM is low (max 24GB), one question to look into before investing might be whether there's support for chaining multiple cards together.
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u/textcoretex: I would be interested in running inference on Instinct GPUs(MI250 with 128gb)BUT I can’t find any cloud provider to spin up a machine. It seems they are not yet available or cloud providers are not interested in supporting AMD hardware..
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☑️ #156 Oct 11, 2024
$2 H100s: How the GPU Bubble Burst
Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened?
@ftr_investors: $NVDA BREAKING Nvidia's Blackwell chip is sold out for the next 12 months!
In a meeting with Morgan Stanley, Nvidia executives revealed that the Blackwell chip is sold out for the next 12 months. Morgan Stanley predicts Nvidia's market share will continue to grow in 2025.
🔹Related content:
nvidia.com: [Excerpts] NVIDIA Blackwell Architecture. The engine of the new industrial revolution.
Look Inside the Technological Breakthroughs
A New Class of AI Superchip. Blackwell-architecture GPUs pack 208 billion transistors and are manufactured using a custom-built TSMC 4NP process. All Blackwell products feature two reticle-limited dies connected by a 10 terabytes per second (TB/s) chip-to-chip interconnect in a unified single GPU.
investors.com (10/10/24) [Excerpt] Nvidia Chief Executive Jensen Huang and members of his management team provided the reassuring guidance in meetings with investment bank Morgan Stanley.
The commentary highlighted the magnitude and length of the accelerated computing runway, Morgan Stanley analyst Joseph Moore said in a client note Thursday.
"Blackwell chips are expected to see 450,000 units produced in the fourth quarter of 2024, translating into a potential revenue opportunity exceeding $10 billion for Nvidia" - Morgan Stanley
Sep 12, 2024 "We're in full volume production of Blackwell" - Jensen Huang
Aug 2, 2024 "Nvidia's upcoming artificial intelligence chips will be delayed by three months or more due to design flaws" - The Information
morganstanley.com (7/29/24): [Excerpt] Nvidious position. The other threat will come if any of Nvidia's four largest customers (Microsoft, Amazon, Alphabet and Meta, which currently comprise around 40% of its revenues) succeed in their efforts to design a better priced alternative to the H100 or its next generation follow-up chips Blackwell (2025) and Rubin (2026). Economics undergraduate principles spring to mind: abnormal profits first attract abnormal speculation and then abnormal competition. Moreover, there’s an inherent cyclicality of demand for capital expenditure beneficiaries — whatever today’s trend might be — as shown by the history of massive 50%-90% drawdowns in Nvidia’s otherwise very successful history, in 2002, 2008, 2018 and 2022.
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☑️ #154 Oct 8, 2024
We're fabbing at a TSMC because it's the world's best
@Tech Fund: Jensen on Nvidia's ability to change fabs if needed - e.g. a geopolitical event happening around Taiwan.
In the event that we have to shift from one fab to another, we have the ability to do it. Maybe the process technology is not as great. Maybe we won't be able to get the same level of performance or cost but we will be able to provide the supply.
And so I think the - in the event anything were to happen, we should be able to pick up and fab it somewhere else. We're fabbing at a TSMC because it's the world's best. And it's the world's best not by a small margin, it's the world's -- it's by a lot - not by chance, incredible margin.
And so not only just the long history of working with them, the great chemistry, their agility, the fact that they could scale. Remember, NVIDIA's last year's revenue had a major hockey stick. That major hockey stick wouldn't have been possible if not for the supply chain responding. And so the agility of that supply chain, including TSMC, is incredible. And in just less than 1 year, we've scaled up CoWoS capacity tremendously. And we're going to have to scale it up even more next year and scale it up even more the year after that. But nonetheless, the agility and their capability to respond to our needs is just incredible. And so we use them because they re great. But if necessary, of course, we can always bring up the others.
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☑️ #153 Oct 8, 2024
Efficiently serve optimized AI models with NVIDIA NIM microservices on GKE
cloud.google.com: [Excerpt] In the rapidly evolving landscape of AI, efficiently serving AI models is critical to ensure the platform delivers value at optimal performance and cost. But the complexities of optimizing and operating an increasing variety of AI models prevents many organizations from fully realizing AI’s value. We’ve been partnering closely with NVIDIA to bring the power of the NVIDIA AI accelerated computing platform to Google Kubernetes Engine (GKE) to address these complexities. Today, we’re thrilled to announce the availability of NVIDIA NIM, part of the NVIDIA AI Enterprise software platform, on GKE, letting you deploy NIM microservices directly from the GKE console.
NVIDIA and Google Cloud > Google Kubernetes Engine: [Excerpt] The most scalable and fully automated Kubernetes service. Put your containers on autopilot and securely run your enterprise workloads at scale—with little to no Kubernetes expertise required.
nvidia.com: [Excerpt] Instantly Deploy Generative AI With NVIDIA NIM. Explore the latest community-built AI models with APIs optimized and accelerated by NVIDIA, then deploy anywhere with NVIDIA NIM™ inference microservices.
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☑️ #152 Oct 8, 2024
TSMC and NVIDIA Transform Semiconductor Manufacturing With Accelerated Computing
blogs.nvidia.com: [Excerpt] The NVIDIA cuLitho computational lithography platform is moving to production at TSMC.
The use of optical proximity correction in semiconductor lithography is now three decades old. While the field has benefited from numerous contributions over this period, rarely has it seen a transformation quite as rapid as the one provided by the twin technologies of accelerated computing and AI. These together allow for the more accurate simulation of physics and the realization of mathematical techniques that were once prohibitively resource-intensive.
This enormous speedup of computational lithography accelerates the creation of every single mask in the fab, which speeds the total cycle time for developing a new technology node. More importantly, it makes possible new calculations that were previously impractical.
The largest GB200 production facility on the planet
@dnystedt: Foxconn, the iPhone and AI server assembly giant, plans to build “the largest GB200 production facility on the planet” in Mexico to manufacture Nvidia GB200 modules, parts, AI servers, media report, adding it is planning capacity for 20,000 Nvidia NVL72 servers in 2025. Foxconn Chairman Young Liu said demand for Nvidia’s next-generation Blackwell chips is “crazy”. $NVDA$AMD$INTC#Blackwell#semiconductor#semiconductors
US AI Companies to Move Hardware Production to Mexico (4/3/24): [Excerpt] Foxconn, the Taiwanese electronics giant specializing in the development of AI hardware used by companies such as Amazon, Microsoft, and NVIDIA, recently announced an expansion of its AI server production in Mexico, with an investment of approximately US$27 million in Jalisco. This investment is part of a broader effort that has seen Foxconn invest around US$690 million in Mexico over the last four years.
