Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Agentic workloads generate 20-50x more tokens than chat. That multiplier hits compute, storage, and orchestration simultaneously. Google Cloud's Mark Lohmeyer (who owns all of Compute) and Patrick Moorhead break down how GC is building infrastructure to absorb that pressure across the full AI Hypercomputer stack from Google Cloud Next 2026.

Six Five Media

3,969 subscribers

162,434 views • 3 months ago •via X (Twitter)

News & Politics Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Pushing the limits of cloud and AI performance takes deep integration across the entire stack, from power and compute to network and storage. Mahesh Thiagarajan (Mahesh Thiagarajan) of Oracle shares how AMD Infinity Fabric is helping customers unlock more value and AI performance by moving data closer to the accelerators. #AdvancingAI

Pushing the limits of cloud and AI performance takes deep integration across the entire stack, from power and compute to network and storage. Mahesh Thiagarajan (Mahesh Thiagarajan) of Oracle shares how AMD Infinity Fabric is helping customers unlock more value and AI performance by moving data closer to the accelerators. #AdvancingAI

AMD

16,427 views • 1 year ago

Building the world's largest AI compute DePIN means has tens of thousands of independent node operators around the world. Providing alternatives to centralized cloud providers is more than bringing transactions for compute on-chain, it is tapping into the distributed compute that already exists in the world and making it available to innovators and builders advancing AI and ML. video from our community member leeland アブス ✳️

Building the world's largest AI compute DePIN means has tens of thousands of independent node operators around the world. Providing alternatives to centralized cloud providers is more than bringing transactions for compute on-chain, it is tapping into the distributed compute that already exists in the world and making it available to innovators and builders advancing AI and ML. video from our community member leeland アブス ✳️

io.net

966,323 views • 2 years ago

Had a great conversation with Richard Seroter, Senior Director and Chief Evangelist at Google Cloud, during Google I/O Connect India in Bangalore today. We spoke about how he uses Gemini in his day to day work, how Google is handling the massive compute demand for AI, why defining goals matters more than writing perfect specs, and what's next for Gemini.

Had a great conversation with Richard Seroter, Senior Director and Chief Evangelist at Google Cloud, during Google I/O Connect India in Bangalore today. We spoke about how he uses Gemini in his day to day work, how Google is handling the massive compute demand for AI, why defining goals matters more than writing perfect specs, and what's next for Gemini.

AshutoshShrivastava

13,866 views • 23 days ago

INTERNET COMPUTER SET TO DEMO "CLOUD ENGINES" DFINITY Foundation prepares to unveil "cloud engines" on May 10, 2026, marking the fifth anniversary of the Internet Computer network. According to the Founder's statement, the protocol will also reveal agentic builds, AI nodes, alongside other new features... The demonstration features a full-stack compute technology, including autonomous agentic builds and dedicated AI nodes designed for decentralized scaling. "This is a very big deal for ICP, cloud and agentic R&D. Engines will be the 1st frontier cloud/compute technology the wild." This launch positions $ICP as a primary infrastructure provider for frontier cloud R&D by enabling machine-to-machine logic at the protocol level. The release aims to transition traditional cloud workloads into a trustless, GPU-backed environment for the next generation of autonomous applications.

INTERNET COMPUTER SET TO DEMO "CLOUD ENGINES" DFINITY Foundation prepares to unveil "cloud engines" on May 10, 2026, marking the fifth anniversary of the Internet Computer network. According to the Founder's statement, the protocol will also reveal agentic builds, AI nodes, alongside other new features... The demonstration features a full-stack compute technology, including autonomous agentic builds and dedicated AI nodes designed for decentralized scaling. "This is a very big deal for ICP, cloud and agentic R&D. Engines will be the 1st frontier cloud/compute technology the wild." This launch positions $ICP as a primary infrastructure provider for frontier cloud R&D by enabling machine-to-machine logic at the protocol level. The release aims to transition traditional cloud workloads into a trustless, GPU-backed environment for the next generation of autonomous applications.

BSCN

21,373 views • 3 months ago

The pilot phase of enterprise AI is ending. What separates the organizations that scale from those that stall is infrastructure, data readiness, and governance built before models are ever deployed. Patrick Moorhead and Daniel Newman sit down with Varun Chhabra of Dell Technologies and Jason Schroedl of NVIDIA at Dell Technologies World 2026 to break down new approaches to agentic AI deployment, investments in data readiness, and how enterprises can expand AI capabilities while maintaining control over security, costs and operations.

The pilot phase of enterprise AI is ending. What separates the organizations that scale from those that stall is infrastructure, data readiness, and governance built before models are ever deployed. Patrick Moorhead and Daniel Newman sit down with Varun Chhabra of Dell Technologies and Jason Schroedl of NVIDIA at Dell Technologies World 2026 to break down new approaches to agentic AI deployment, investments in data readiness, and how enterprises can expand AI capabilities while maintaining control over security, costs and operations.

Six Five Media

148,133 views • 2 months ago

“People are already building things that weren’t possible before.” With Mainnet and TGE live, Acurast is focused on what comes next: from scaling real-world compute to new infrastructure use cases, including Agentic AI. Watch the founders’ update 👇

“People are already building things that weren’t possible before.” With Mainnet and TGE live, Acurast is focused on what comes next: from scaling real-world compute to new infrastructure use cases, including Agentic AI. Watch the founders’ update 👇

Acurast

26,828 views • 6 months ago

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

Cristiano R. Amon

15,607 views • 1 month ago

"You end up having to use AI to fight AI." — Francis DeSouza, COO and President of Security Products, Google Cloud shared that AI can take 30-minute investigations down to one minute. That's threat hunting at a scale that humans can't match. Listen to the full conversation with Patrick Moorhead and Google's Francis DeSouza on why the SOC just changed permanently.

"You end up having to use AI to fight AI." — Francis DeSouza, COO and President of Security Products, Google Cloud shared that AI can take 30-minute investigations down to one minute. That's threat hunting at a scale that humans can't match. Listen to the full conversation with Patrick Moorhead and Google's Francis DeSouza on why the SOC just changed permanently.

Six Five Media

67,875 views • 3 months ago

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Sam Altman says the AI infrastructure race is not about bigger chatbots. It is about shifting the planet's intellectual output from human brains to AI brains. "If people knew what we could do with compute, they would want way, way more." "The thing I am personally most excited about is to use AI and lots of compute to discover new science." "A recent cool example here is we built the Sora Android app using Codex... in less than a month." "You can imagine that way much further, where entire companies can build their products using lots of compute." Then he gives the scale: "Let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models." "We're going to have these models at a company be outputting more tokens per day than all of humanity put together, and then 10 times that, and then 100 times that." The bottleneck is moving from software talent to compute supply, inference speed, product integration, and where all that machine intelligence gets pointed.

Karl Mehta

16,619 views • 1 month ago

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

KanekoaTheGreat

39,709 views • 1 month ago

Extra outtake clip from latest Bg2 Pod with Jensen Brad Gerstner Contrary to popular hypersensationalist rhetoric -- that we are in a massive AI glut -- we are likely in a stretch of structural compute shortage. Google announced in May that tokens had grown 50x y/y, and doubled again by July 2025 (100x) to 1 quadrillion monthly tokens. In that period - algorithmic and hardware advances improved efficiencies by ~10-15x - which means Google had to increase accelerated compute dedicated to token generation by 3-10x. Our estimate is that Google increased accelerated compute by ~3x during the period - which means that they had to pull compute from training, recommenders, etc to allocate to token generation. Significant algorithmic advancements (Flash Attention, quantization, MoE), and infrastructure investments (prompt caching, batching) have driven much of that efficiency, but counting on the hardware to get better is something the industry is counting / relying on. We used to be able to ride Moore's Law / Dennard scaling to improve compute per watt. But now... we have to rely on $NVDA / hardware ecosystem (Google, $AMD, etc) to drive 2-4x improvement per generation (Huang's Law). People underestimate the strain of exponentials on human systems - that are hard coded to think linearly... The total global accelerated compute base is probably ~8 GW of installed capacity on my math and analysts have estimated growth to increase to 10x to ~80 GW globally by 2030. Even assuming all of that capex gets done, and Nvidia continues to push yearly roadmap (generating 2x y/y performance uplift), the 10x power increase should equate to ~50x increase in compute. We just had 100x AI usage increase in 1 year from Google's testimony. OpenAI, Google, Anthropic are in structural shortage of compute - each bit they bring online is fully invested in serving their users. And that is without even expanding into true video / world models, robotics, or long horizon thinking to find novel breakthroughs. What could change this trajectory? If the algorithmic efficiencies we are gaining from new breakthroughs outstrip the exponential increase in current demand -- and FUTURE demand. Investors hyperventilated at DeepSeek's release earlier this year, but their gains were outweighed by the increase in demand created by reasoning.

Extra outtake clip from latest Bg2 Pod with Jensen Brad Gerstner Contrary to popular hypersensationalist rhetoric -- that we are in a massive AI glut -- we are likely in a stretch of structural compute shortage. Google announced in May that tokens had grown 50x y/y, and doubled again by July 2025 (100x) to 1 quadrillion monthly tokens. In that period - algorithmic and hardware advances improved efficiencies by ~10-15x - which means Google had to increase accelerated compute dedicated to token generation by 3-10x. Our estimate is that Google increased accelerated compute by ~3x during the period - which means that they had to pull compute from training, recommenders, etc to allocate to token generation. Significant algorithmic advancements (Flash Attention, quantization, MoE), and infrastructure investments (prompt caching, batching) have driven much of that efficiency, but counting on the hardware to get better is something the industry is counting / relying on. We used to be able to ride Moore's Law / Dennard scaling to improve compute per watt. But now... we have to rely on $NVDA / hardware ecosystem (Google, $AMD, etc) to drive 2-4x improvement per generation (Huang's Law). People underestimate the strain of exponentials on human systems - that are hard coded to think linearly... The total global accelerated compute base is probably ~8 GW of installed capacity on my math and analysts have estimated growth to increase to 10x to ~80 GW globally by 2030. Even assuming all of that capex gets done, and Nvidia continues to push yearly roadmap (generating 2x y/y performance uplift), the 10x power increase should equate to ~50x increase in compute. We just had 100x AI usage increase in 1 year from Google's testimony. OpenAI, Google, Anthropic are in structural shortage of compute - each bit they bring online is fully invested in serving their users. And that is without even expanding into true video / world models, robotics, or long horizon thinking to find novel breakthroughs. What could change this trajectory? If the algorithmic efficiencies we are gaining from new breakthroughs outstrip the exponential increase in current demand -- and FUTURE demand. Investors hyperventilated at DeepSeek's release earlier this year, but their gains were outweighed by the increase in demand created by reasoning.

Clark Tang

176,622 views • 10 months ago

The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.

The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.

Drey

13,996 views • 2 months ago

Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

Aethir

12,781 views • 4 months ago

“When we start consuming 4 to 5 quintillion tokens per year, 70% of the tokens are going to be generated by agents,” DigitalOcean CEO Paddy Srinivasan said. AI token demand is projected to surge by 2030, with agents expected to be one of the biggest drivers, according to Srinivasan. That shift means agentic workloads could become “multiple orders of magnitude bigger” than human-driven workflows, with agents requiring “a significant amount of compute: GPUs, CPUs, databases, memory, and cloud primitives all in one cloud.” Supported by DigitalOcean.

“When we start consuming 4 to 5 quintillion tokens per year, 70% of the tokens are going to be generated by agents,” DigitalOcean CEO Paddy Srinivasan said. AI token demand is projected to surge by 2030, with agents expected to be one of the biggest drivers, according to Srinivasan. That shift means agentic workloads could become “multiple orders of magnitude bigger” than human-driven workflows, with agents requiring “a significant amount of compute: GPUs, CPUs, databases, memory, and cloud primitives all in one cloud.” Supported by DigitalOcean.

The Information

16,907 views • 1 month ago

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

Dylan on why Anthropic leading on models doesn't mean OpenAI loses. Even the tier 2 and 3 labs will sell out of tokens: "Everyone's like Anthropic won. They had Mythos, Opus 4.7 and their revenue is adding $10 billion a month all before OpenAI's alleged Spud release. So clearly Anthropic is in the lead and OpenAI is cooked. Anthropic has such bounds on compute, they can only grow it so fast. Dario used to gloat about how OpenAI was being too aggressive on compute and Anthropic was more sensible in their scaling. Now Anthropic is like I wish we had a lot more compute. OpenAI just raised a ton of money to get incremental compute in addition to the irresponsible levels of compute that they were buying. By the end of the year, let's say a 4.6 Opus tier model, the economy would spend $100 billion on. Anthropic won't have enough compute to do that. And presumably OpenAI and Google will hit that tier soon. Anthropic may get to charge 70%+ gross margins but if OpenAI hits it next, they charge 50% gross margins. They still get all of this incremental demand. There is such demand for these tokens and such limitations on compute. It's pretty clear even the tier 2 or tier 3 lab are going to be sold out of tokens. The economic value that the best model can deliver is growing faster than our ability to actually serve those tokens to people via the infrastructure."

Patrick OShaughnessy

295,226 views • 3 months ago

AI agents move fast. So should your search foundation. Introducing the next generation of Amazon OpenSearch Serverless. We rebuilt OpenSearch Serverless from the ground up for agentic AI and unpredictable workloads: - Create resources in seconds - Autoscale up to 20x faster than before - Usage-based pricing with decoupled storage and compute - No idle costs with scale to zero - Up to 60% cost savings vs. provisioned clusters for peak capacity Learn more.

AI agents move fast. So should your search foundation. Introducing the next generation of Amazon OpenSearch Serverless. We rebuilt OpenSearch Serverless from the ground up for agentic AI and unpredictable workloads: - Create resources in seconds - Autoscale up to 20x faster than before - Usage-based pricing with decoupled storage and compute - No idle costs with scale to zero - Up to 60% cost savings vs. provisioned clusters for peak capacity Learn more.

Amazon Web Services

11,941 views • 2 months ago

“It’s about $1 million a megawatt all-in, between infrastructure and compute, to build a Bitcoin mining site. The AI site, just the infrastructure without compute, is $10 to $15 million a megawatt.” $MARA CEO Fred Thiel points out the math of Bitcoin miners pivoting to AI: An AI data center costs 10-15x more per megawatt to build than a Bitcoin mine but it earns far more per unit of power. And because the tenants are companies like Google, banks will actually finance it. The pricier build is the easier one to fund. Don’t miss the full interview streaming now.

“It’s about $1 million a megawatt all-in, between infrastructure and compute, to build a Bitcoin mining site. The AI site, just the infrastructure without compute, is $10 to $15 million a megawatt.” $MARA CEO Fred Thiel points out the math of Bitcoin miners pivoting to AI: An AI data center costs 10-15x more per megawatt to build than a Bitcoin mine but it earns far more per unit of power. And because the tenants are companies like Google, banks will actually finance it. The pricier build is the easier one to fund. Don’t miss the full interview streaming now.

Natalie Brunell ⚡️

45,375 views • 13 days ago

"They're not spending $30 billion on an acquisition for charity." — Daniel Newman, CEO and Chief Analyst, Futurum Patrick Moorhead and Daniel Newman called it like it is at Google Cloud Next 2026. The Wiz acquisition is not a security product play. It's a strategic bet Google is making that agent-native security will become the most critical infrastructure layer for every large enterprise operating at a global scale. They're going for security that's built into the platform, not bolted on. That distinction matters more than most people realize right now.

"They're not spending $30 billion on an acquisition for charity." — Daniel Newman, CEO and Chief Analyst, Futurum Patrick Moorhead and Daniel Newman called it like it is at Google Cloud Next 2026. The Wiz acquisition is not a security product play. It's a strategic bet Google is making that agent-native security will become the most critical infrastructure layer for every large enterprise operating at a global scale. They're going for security that's built into the platform, not bolted on. That distinction matters more than most people realize right now.

Six Five Media

62,371 views • 3 months ago

💡 Inference is hitting a major inflection point in 2025. The future of AI computing will be shaped by who can deliver performance and efficiency at scale. At AMD Advancing AI, Daniel Newman & Patrick Moorhead sat down with Andrew Dieckmann and Anush Elangovan to explore how AMD is strategically positioned to lead in both AI training and inferencing—two of the fastest-growing segments in AI compute. Learn how they're scaling real-world deployments across both training and inference. Catch the full conversation:

💡 Inference is hitting a major inflection point in 2025. The future of AI computing will be shaped by who can deliver performance and efficiency at scale. At AMD Advancing AI, Daniel Newman & Patrick Moorhead sat down with Andrew Dieckmann and Anush Elangovan to explore how AMD is strategically positioned to lead in both AI training and inferencing—two of the fastest-growing segments in AI compute. Learn how they're scaling real-world deployments across both training and inference. Catch the full conversation:

Six Five Media

29,588 views • 1 year 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

179,812 views • 14 days ago