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Showing posts with label Nvidia. Show all posts
Showing posts with label Nvidia. Show all posts

Friday, June 06, 2025

10 Possible "AI-Era Ciscos" (Infra Giants in the Making)

 


Here’s a list of 10 possible “Ciscos” for the emerging AI era — companies that are (or could be) to AI infrastructure what Cisco was to the Internet: the backbone builders, the connective tissue, the enablers of scale.

🧠 10 Possible "AI-Era Ciscos" (Infra Giants in the Making)

  1. NVIDIA
    Why: Already the GPU kingpin. But it's now expanding into networking (e.g., Mellanox), AI cloud infra, and full-stack AI systems. Becoming the "hardware+software fabric" of the AI age.

  2. TSMC
    Why: The invisible foundation of AI — fabs that make the chips. If NVIDIA is the architect, TSMC is the builder. As AI demand grows, TSMC becomes more geopolitically and economically critical.

  3. AMD
    Why: Rising challenger to NVIDIA, with competitive AI and data center chips (like MI300). May power alternative AI infrastructure providers looking to avoid Nvidia lock-in.

  4. Broadcom
    Why: Quietly dominates custom silicon, networking chips, and infrastructure software. Their tech powers AI data centers even if they’re not front-and-center.

  5. Arista Networks
    Why: Modern data center networking, low-latency fabrics, and AI cluster connectivity. Like Cisco in the 90s — building the roads for AI traffic.

  6. Lambda Labs
    Why: The "DIY NVIDIA stack" for startups and mid-size orgs. Affordable AI servers, cloud GPU access, and full-stack ML infra. Positioning itself as the dev-friendly infra layer.

  7. CoreWeave
    Why: Ex-GPU crypto miner turned AI cloud. One of the fastest-scaling alternatives to AWS for AI workloads. Building infra-as-a-service for inference and training at scale.

  8. Graphcore (or another chip startup)
    Why: Betting on novel compute paradigms. If they crack the "post-GPU" architecture (e.g., IPUs, TPUs), they could be the dark horse Cisco of new AI hardware.

  9. Celestial AI / Lightmatter / Ayar Labs
    Why: Optical and photonic interconnects — essential for scaling AI clusters beyond today's thermal/electrical limits. Could power the next-generation AI data highways.

  10. Anthropic / OpenAI Infra Division
    Why: Building internal, vertically integrated superclusters (custom racks, interconnects, scheduling). Their infra efforts may birth the AWS of AGI — or be spun out into infra-first giants.

🚀 Bonus Mentions

  • Amazon / Microsoft / Google (Infra Arms) – They’re still the cloud backbones, increasingly offering custom AI infra (e.g., Trainium, Azure Maia, Google TPUv5).

  • SiFive / RISC-V startups – Open hardware standards may drive new AI infra designed from the ground up.



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Saturday, May 31, 2025

31: Nvidia

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

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Friday, May 30, 2025

30: AI Intimacy

“The world has fundamentally changed” - Nvidia CEO Jensen Huang wants it to be so much more than a chip maker an ambitious road map for the development of AI infrastructure. ....... Nvidia is “not a technology company only anymore, in fact, we’re an essential infrastructure company”. ......... Huang explained that just like electricity and the internet, AI, or “intelligence” will move from being a useful tool, to an essential part of life; “In 10 years time you will look back and you will realize that AI has now integrated into everything and in fact we need AI everywhere.” ........... As such, Nvidia isn’t building data centres anymore. Well, not in the same way. Huang described data centres as ‘data centres of the past,’ that provide information and storage. These are similar to what he envisions in that they come from the same industry, but in the future, these will “emerge as something completely different”. ......... These new AI data centres will be less like the data centres we know now, and more like “AI factories”, Huang argued. ........... “Agentic AI is basically; Understand, think, and act.” Huang explained. “Agentic AI is basically a robot in a digital form. These are going to be really important in the coming years, we're seeing enormous progress in this area.” .......... Moving forward, AI is going to be thinking incredibly fast, as Huang noted, “what used to be oneshot AI is now going to be thinking AI, reasoning AI, inference time scaling AI and that's going to take a lot more computation.” .......... “Nvidia has been scaling computing by about a million times every 10 years and we're still on that track,” Huang. ............ Huang also announced that in partnership with the Taiwanese Government, Foxconn, and TSMC, Nvidia is going to build the first “giant AI supercomputer, here for the AI infrastructure in the AI ecosystem of Taiwan.” ........ Nvidia is leading the charge in AI chips, and the continued investments into infrastructure and components outlines the ambitions for the company to widen its market share of a range of technologies .......... Finally, for corporations without in-house building capabilities, Nvidia is offering detailed blueprints to accelerate the process of building AI factories and infrastructure, helping remove the friction and ensure a seamless transition into the coming “Age of AI.”

How Trump's trade war is upending the global economy February 1 - Trump imposes 25% tariffs on Mexican and most Canadian imports and 10% on goods from China, demanding they curb the flow of fentanyl and illegal immigrants into the United States. .......... February 10 - Trump raises tariffs on steel and aluminum to a flat 25% "without exceptions or exemptions". .......... April 9 - Trump pauses for 90 days most of his country-specific tariffs that kicked in less than 24 hours earlier following an upheaval in financial markets that erased trillions of dollars from bourses around the world. .......... Trump says he will raise the tariff on Chinese imports to 125% from the 104% level that took effect a day earlier. This pushes the extra duties on Chinese goods to 145%, including the fentanyl-related tariffs imposed earlier. .......... May 12 - The U.S. and China agree to temporarily slash reciprocal tariffs. Under the 90-day truce, the U.S. will cut the extra tariffs it imposed on Chinese imports to 30% from 145%, while China's duties on U.S. imports will be slashed to 10% from 125%. .......... May 28 - A U.S. trade court blocked Trump's tariffs from going into effect in a sweeping ruling that the president overstepped his authority by imposing across-the-board duties on imports from U.S trade partners. The Trump administration said it would appeal the ruling. ......... May 29 - A federal appeals court temporarily reinstates the most sweeping of Trump's tariffs, saying it was pausing the lower court's ruling to consider the government's appeal, and ordered the plaintiffs in the cases to respond by June 5 and the administration by June 9.

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

Velocity Money: Crypto, Karma, and the End of Traditional Economics
The Next Decade of Biotech: Convergence, Innovation, and Transformation
Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

Remote Work Productivity Hacks
How to Make Money with AI Tools
AI for Beginners

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Key Bottlenecks Affecting Dell's Supply Chain

 

Dell Technologies is currently experiencing significant challenges in meeting the surging demand for its AI servers, primarily due to supply chain bottlenecks and production constraints.(Investing.com)

Key Bottlenecks Affecting Dell's Supply Chain:

  1. High Demand for AI Servers:
    Dell has reported an unprecedented $12.1 billion in AI server orders this quarter alone, surpassing all of fiscal 2025’s shipments, and leaving a backlog of $14.4 billion.

  2. Component Shortages:
    The availability of critical components, especially high-performance GPUs from suppliers like NVIDIA and AMD, remains a significant constraint. Any disruptions or shortages in the supply chain could lead to delays in shipments and potentially impact Dell’s ability to capitalize on the current AI boom.

  3. Technical Challenges with Advanced AI Racks:
    Dell and its partners have faced technical issues with Nvidia's flagship GB200 AI data center racks, including overheating, liquid cooling leaks, software bugs, and chip connectivity problems due to the system's complex design. These issues had previously disrupted production but have since been resolved, enabling increased shipments.

  4. Manufacturing and Assembly Constraints:
    Dell's manufacturing strategy has been impacted by chipset supply shortages, leading to increased reliance on Level 5 (L5) assembly, which involves chassis integration without motherboards. This shift has significantly affected operational costs and the company's ability to meet customer demand promptly.

  5. Supply Chain Resilience Efforts:
    In response to these challenges, Dell has instructed its semiconductor suppliers to diversify their fabrication and backend facilities by the end of 2024 to improve supply chain resilience. This move aims to mitigate future disruptions and continue supporting its global business.

Despite these hurdles, Dell continues to adapt its strategies to address supply chain issues and meet the growing demand for AI infrastructure.

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Thursday, May 22, 2025

From CPUs to GPUs—And Beyond: The Evolution of Computing Power



From CPUs to GPUs—And Beyond: The Evolution of Computing Power

In the ever-accelerating world of computing, the distinctions between CPUs and GPUs have become both more pronounced and more blurred. As artificial intelligence, gaming, and data science reshape the tech landscape, the hardware driving these revolutions is evolving just as quickly. What began as a straightforward division of labor between Central Processing Units (CPUs) and Graphics Processing Units (GPUs) is now giving way to a new era of specialized chips—where AI accelerators and quantum processors are starting to make their mark.

The CPU: The Classic Workhorse

What it is:
The Central Processing Unit (CPU) is often described as the "brain" of the computer. It handles all general-purpose tasks—running the operating system, executing application code, and managing system resources.

History:

  • 1940s-1950s: Early CPUs were massive machines built from vacuum tubes.

  • 1971: Intel introduced the Intel 4004, the first commercially available microprocessor.

  • 1980s-1990s: x86 architecture from Intel and AMD took off, dominating the PC era.

  • 2000s–present: CPUs evolved to include multiple cores, integrated memory controllers, and hyper-threading.

Top Players Today:

  • Intel: Dominant in PCs and data centers for decades.

  • AMD: Competitive with its Ryzen and EPYC lines.

  • Apple: Disrupted the market with its ARM-based M1, M2, and M3 chips, integrating CPU, GPU, and neural engines.

The GPU: Parallel Power for the Data Age

What it is:
Originally designed to render graphics, the Graphics Processing Unit (GPU) specializes in parallel processing—performing thousands of calculations simultaneously, making it perfect for graphics and, later, machine learning.

History:

  • 1999: NVIDIA introduced the GeForce 256, the first GPU marketed as such.

  • 2006: With the launch of CUDA, NVIDIA enabled developers to use GPUs for general-purpose computing.

  • 2010s–present: GPUs became essential for deep learning, gaming, video editing, and cryptocurrency mining.

Top Players Today:

  • NVIDIA: Unquestionably dominant in AI and high-performance computing.

  • AMD: Known for Radeon GPUs and increasing traction in data centers.

  • Intel: Entered the GPU market with Arc and Xe products.

The Present and Future: What Comes After GPUs?

As demand for computing power explodes—fueled by AI, big data, and simulation—the industry is moving beyond general-purpose CPUs and GPUs to even more specialized hardware:

1. AI Accelerators / Neural Processing Units (NPUs)

Purpose: Tailored for machine learning tasks like training neural networks or running inference models.

Key Players:

  • Google: Tensor Processing Units (TPUs), powering Google Search and Google Cloud AI.

  • Apple: Neural engines in its M-series chips optimize on-device AI.

  • Tesla: Dojo supercomputer for self-driving AI.

  • Amazon: Inferentia and Trainium chips for AWS cloud AI workloads.

2. FPGAs and ASICs

Field Programmable Gate Arrays (FPGAs): Flexible hardware you can program for specific tasks.

Application-Specific Integrated Circuits (ASICs): Hard-wired chips for ultra-efficient task execution, like Bitcoin mining or edge AI.

Key Players:

  • Intel (Altera): Leader in FPGAs.

  • Xilinx (AMD): Competes with high-performance programmable logic.

  • Bitmain: Dominates ASIC-based crypto mining.

3. Quantum Processors

Still in early stages, quantum computing promises to outperform classical systems in certain tasks like optimization, chemistry simulations, and encryption.

Key Players:

  • IBM: Quantum roadmap to 1,000+ qubits.

  • Google: Achieved “quantum supremacy” in 2019.

  • D-Wave: Specializes in quantum annealing for optimization.

4. Optical and Photonic Computing

Computing using light instead of electricity, offering faster speeds and lower power consumption.

Key Players:

  • Lightmatter, Ayar Labs, and Intel’s photonic research labs are pioneering this frontier.

The Convergence: Heterogeneous Computing

The future isn’t about CPUs vs. GPUs—it’s about everything working together. Apple’s M-series chips already integrate CPUs, GPUs, and NPUs on a single SoC (system on chip). Cloud giants like Google and Amazon are optimizing workloads by dynamically routing tasks to the most efficient hardware: CPU for logic, GPU for parallelism, TPU for deep learning, and FPGA for real-time edge inferencing.

Conclusion: Silicon Is Just the Start

We are entering a post-Moore’s Law world, where architecture innovation matters more than raw transistor count. The real breakthroughs will come from custom silicon, hardware-software co-design, and new physics.

The CPU isn’t dead. The GPU isn’t obsolete. But the age of hyper-specialized computing is upon us—and what comes next will be as much about use case and context as about raw performance.

Which chip will power your future? The answer is: all of them. Seamlessly. Invisibly. Intelligently. Welcome to the era of compute convergence.






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Monday, January 27, 2025

27: NVIDIA

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Thursday, December 19, 2024

19: Nvidia

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