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$NVDA is evolving into the platform behind the entire AI factory spanning compute, CPUs, networking, interconnect, software, rack-scale systems and now the financing that helps customers build them. Owning more of that stack lets Nvidia capture more of every dollar spent on AI infrastructure while sitting across the bottlenecks... show more

Shay Boloor

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152,747 views • 8 days ago •via X (Twitter)

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10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️

10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️

NVIDIA AI Infrastructure

23,393 views • 12 days ago

Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.

Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.

AMD

20,832 views • 7 months ago

$MU CEO Sanjay Mehrotra says AI is turning memory from a commodity into “strategic infrastructure.” AI cannot scale without more HBM DRAM and SSD capacity which is making memory far more valuable across the entire compute stack.

$MU CEO Sanjay Mehrotra says AI is turning memory from a commodity into “strategic infrastructure.” AI cannot scale without more HBM DRAM and SSD capacity which is making memory far more valuable across the entire compute stack.

Shay Boloor

192,619 views • 11 days ago

My biggest question heading into $NVDA earnings is whether demand keeps broadening beyond $MSFT, $AMZN and $GOOGL into ACIE and newer buyers like $SPCX. I also want to see whether Rubin can replace Blackwell at a higher system value allowing Nvidia to capture more of the rack across CPUs, networking, memory, interconnect and software while gross margin holds near 75%. If both hold then next leg of the story becomes more about Nvidia expanding both its customer base and the dollars it captures from every AI system.

My biggest question heading into $NVDA earnings is whether demand keeps broadening beyond $MSFT, $AMZN and $GOOGL into ACIE and newer buyers like $SPCX. I also want to see whether Rubin can replace Blackwell at a higher system value allowing Nvidia to capture more of the rack across CPUs, networking, memory, interconnect and software while gross margin holds near 75%. If both hold then next leg of the story becomes more about Nvidia expanding both its customer base and the dollars it captures from every AI system.

Shay Boloor

49,916 views • 7 days ago

$NVDA networking is now running at a $60B+ annualized pace, growing ~200% YoY and nearly the size of $AVGO total 2025 revenue. As AI clusters scale, the bottleneck becomes how fast GPUs, CPUs, memory and storage move data together because even the best chips underperform if the network cannot keep up. This is why Nvidia is selling the full AI factory stack.

$NVDA networking is now running at a $60B+ annualized pace, growing ~200% YoY and nearly the size of $AVGO total 2025 revenue. As AI clusters scale, the bottleneck becomes how fast GPUs, CPUs, memory and storage move data together because even the best chips underperform if the network cannot keep up. This is why Nvidia is selling the full AI factory stack.

Shay Boloor

61,104 views • 3 months ago

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

The Futurum Group

75,859 views • 3 months ago

AI workloads are evolving, and so is the infrastructure behind them. Hear Meta's Head of Infrastructure Santosh Janardhan discuss how Meta and AMD are co-engineering AI infrastructure across the full stack.

AI workloads are evolving, and so is the infrastructure behind them. Hear Meta's Head of Infrastructure Santosh Janardhan discuss how Meta and AMD are co-engineering AI infrastructure across the full stack.

AMD

31,330 views • 1 month ago

$NVDA CEO Jensen Huang saying $MRVL can become “the next trillion-dollar company” doesn’t sound crazy when you realize it sits directly inside one of the biggest bottlenecks of the AI buildout: networking. Marvell is trying to become the connective tissue of the AI factory with exposure to custom ASICs, optics, switching, photonics and data movement layer every large-scale AI system needs.

$NVDA CEO Jensen Huang saying $MRVL can become “the next trillion-dollar company” doesn’t sound crazy when you realize it sits directly inside one of the biggest bottlenecks of the AI buildout: networking. Marvell is trying to become the connective tissue of the AI factory with exposure to custom ASICs, optics, switching, photonics and data movement layer every large-scale AI system needs.

Shay Boloor

613,382 views • 3 months ago

$AMD is becoming a two-engine AI story with EPYC CPUs for the growing compute needed around agentic workloads and Instinct GPUs for hyperscalers that need a credible alternative to $NVDA. The CPU side still looks underappreciated as AI agents require more high-core-count server compute and 2nm Venice should help AMD keep taking share as $INTC works through roadmap and manufacturing challenges. The bridge is Helios where AMD can package EPYC CPUs, Instinct GPUs and Pensando networking into a full rack-scale AI platform backed by real demand from $META, $ORCL and OpenAI.

$AMD is becoming a two-engine AI story with EPYC CPUs for the growing compute needed around agentic workloads and Instinct GPUs for hyperscalers that need a credible alternative to $NVDA. The CPU side still looks underappreciated as AI agents require more high-core-count server compute and 2nm Venice should help AMD keep taking share as $INTC works through roadmap and manufacturing challenges. The bridge is Helios where AMD can package EPYC CPUs, Instinct GPUs and Pensando networking into a full rack-scale AI platform backed by real demand from $META, $ORCL and OpenAI.

Shay Boloor

89,204 views • 1 month ago

📈 NVIDIA tops AI leaderboards and benchmarks with open models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.

📈 NVIDIA tops AI leaderboards and benchmarks with open models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.

NVIDIA

85,667 views • 4 months ago

The future of AI depends on how compute, storage, networking, and software work together. Junchen Jiang, Co-founder of Tensormesh, on what it takes to build the next generation of AI infrastructure.

The future of AI depends on how compute, storage, networking, and software work together. Junchen Jiang, Co-founder of Tensormesh, on what it takes to build the next generation of AI infrastructure.

AI at AMD

116,017 views • 2 months ago

$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.

$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.

Shay Boloor

261,814 views • 3 months ago

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

NVIDIA CEO Jensen Huang explains the architecture of the AI revolution AI is fundamentally about thinking—and that requires supercomputers. We’ve created a new industry: AI infrastructure. It will be as foundational as the electricity and internet infrastructures—permanent for every society. This is a transformative time; how we deliver and produce intelligence is changing. Each layer of the AI stack is growing: 1. Energy 2. Chips 3. Infrastructure 4. AI models 5. AI applications We’re witnessing a complete revolution across the stack, with opportunities at every layer. NVIDIA intends to participate across the entire stack.

Ask Perplexity

64,013 views • 10 months ago

We are innovating the full-stack AI infrastructure platform on a one-year rhythm — pushing the boundaries of inference performance across chips, systems, and software. NVIDIA Blackwell is the most advanced AI platform ever built — continuously optimized for record-breaking performance. Groundbreaking advancements include the NVIDIA-designed NVFP4 format, enabling high inference performance and accuracy. #GTCParis at #VivaTech

We are innovating the full-stack AI infrastructure platform on a one-year rhythm — pushing the boundaries of inference performance across chips, systems, and software. NVIDIA Blackwell is the most advanced AI platform ever built — continuously optimized for record-breaking performance. Groundbreaking advancements include the NVIDIA-designed NVFP4 format, enabling high inference performance and accuracy. #GTCParis at #VivaTech

NVIDIA

80,625 views • 1 year ago

The AI economy is not just software. It is energy. Chips. Infrastructure. Models. Applications. And every layer needs people to build it. At NVIDIA’s Five-Layer AI event, co-founder Chris Malachowsky, former secretary of commerce Gina Raimondo and Mike Rowe discussed why the future of AI means opportunity for workers across the entire stack.

The AI economy is not just software. It is energy. Chips. Infrastructure. Models. Applications. And every layer needs people to build it. At NVIDIA’s Five-Layer AI event, co-founder Chris Malachowsky, former secretary of commerce Gina Raimondo and Mike Rowe discussed why the future of AI means opportunity for workers across the entire stack.

NVIDIA

28,786 views • 5 months ago

$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

Trade Whisperer

180,418 views • 1 month ago

“Nvidia is the only AI company that works with every AI company” - Nvidia's $NVDA Senior Director of AI Infrastructure Dion Harris

“Nvidia is the only AI company that works with every AI company” - Nvidia's $NVDA Senior Director of AI Infrastructure Dion Harris

Evan

26,293 views • 6 months ago

Jensen Huang says Vera Rubin is now in full production and the supply chain is already twice the size of Grace Blackwell. $NVDA is officially ramping full AI factory systems with CPUs, GPUs and co-packaged optics built directly into the rack.

Jensen Huang says Vera Rubin is now in full production and the supply chain is already twice the size of Grace Blackwell. $NVDA is officially ramping full AI factory systems with CPUs, GPUs and co-packaged optics built directly into the rack.

Shay Boloor

70,283 views • 3 months ago

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

Melvin

34,306 views • 1 month ago