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Jensen Huang said Grok 5 will be 7 Trillion parameter model. On time to train, with the training window fixed at 1 month, the new Vera-Rubin GPU system of Nvidia needs 1/4 the number of systems compared with Blackwell to train the same frontier model. “factory throughput” improves by... show more

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As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

NVIDIA AI

16,053 Aufrufe • vor 8 Monaten

🔥 NVIDIA GTC 2026: The Vera Rubin platform just dropped, introducing 10x inference throughput per watt, at 1/10th the cost per token vs. Blackwell. Jensen Huang called it: every company will now run a "token factory." Here's the thing: Aethir has been running distributed token factories since day one, with nearly 440,000+ GPU containers across 94 countries, delivering 1.5B+ compute hours to enterprises, powered by ATH. When the world's most powerful AI chips land, you need a cloud that can actually deploy them at scale, without the centralized cloud limitations and bottlenecks. That's Aethir’s advantage over centralized compute providers. ⚡

🔥 NVIDIA GTC 2026: The Vera Rubin platform just dropped, introducing 10x inference throughput per watt, at 1/10th the cost per token vs. Blackwell. Jensen Huang called it: every company will now run a "token factory." Here's the thing: Aethir has been running distributed token factories since day one, with nearly 440,000+ GPU containers across 94 countries, delivering 1.5B+ compute hours to enterprises, powered by ATH. When the world's most powerful AI chips land, you need a cloud that can actually deploy them at scale, without the centralized cloud limitations and bottlenecks. That's Aethir’s advantage over centralized compute providers. ⚡

Aethir Ecosystem

15,500 Aufrufe • vor 6 Monaten

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.

Jensen Huang just doubled NVIDIA's demand forecast to $1 Trillion through 2027 🤯 Then spent two hours explaining why that number is conservative… Here's everything today from GTC: - NemoClaw: NVIDIA's open-source enterprise AI agent stack built around OpenClaw. Jensen called OpenClaw "the operating system for personal AI" and said every company needs a strategy for it. - Space-1: NVIDIA is putting Vera Rubin data centers in orbit. Not a concept. An actual system being designed for space deployment right now. - DLSS 5: 3D-guided neural rendering that blends raw graphics with generative AI. Jensen called it the future of real-time rendering. - AWS: Deploying 1 million+ NVIDIA GPUs starting this year. Azure was the first hyperscaler to power up Vera Rubin. - Vera Rubin: NVIDIA's next-gen AI supercomputer. 10x more performance per watt than Blackwell, 700 million tokens per second, shipping later this year. - Groq 3 LPU: First chip from NVIDIA's $20B Groq acquisition. A purpose-built inference accelerator that ships Q3. NVIDIA now owns training AND inference. -Feynman: The architecture after Rubin, coming 2028. New GPU, new LPU, new CPU. NVIDIA is on a 12-month chip cadence and the treadmill never stops. - Autonomous driving: BYD, Hyundai, Nissan, and Geely building Level 4 vehicles on NVIDIA. Uber deploying NVIDIA-powered robotaxis across 28 cities by 2028. The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is NVIDIA's world. Everyone else is just renting compute in it.

Josh Kale

45,875 Aufrufe • vor 6 Monaten

Watch Nvidia CEO Jensen Huang reveal what he described as the state of the art in AI hardware: the new Rubin computing architecture. “Vera Rubin is designed to address this fundamental challenge that we have: The amount of computation necessary for AI is skyrocketing.” Huang said. “Today, I can tell you that Vera Rubin is in full production.” According to Nvidia’s tests, Rubin architecture operates three and a half times faster than the previous Blackwell architecture on model-training tasks and five times faster on inference tasks, and that's just the start.

Watch Nvidia CEO Jensen Huang reveal what he described as the state of the art in AI hardware: the new Rubin computing architecture. “Vera Rubin is designed to address this fundamental challenge that we have: The amount of computation necessary for AI is skyrocketing.” Huang said. “Today, I can tell you that Vera Rubin is in full production.” According to Nvidia’s tests, Rubin architecture operates three and a half times faster than the previous Blackwell architecture on model-training tasks and five times faster on inference tasks, and that's just the start.

TechCrunch

76,412 Aufrufe • vor 8 Monaten

The #NVIDIAVeraRubin platform consists of five rack-scale systems for AI agents with a supply chain spanning over 350 factory sites across 30 countries with millions square feet of factory floor space. Vera Rubin is in full production, co-designed with NVIDIA DSX and DSX MaxLPS to deliver the lowest token cost and maximum tokens per watt. Congratulations to our partners who have their engineering racks up and running: CoreWeave, Dell Technologies, Microsoft, and Oracle Cloud.

The #NVIDIAVeraRubin platform consists of five rack-scale systems for AI agents with a supply chain spanning over 350 factory sites across 30 countries with millions square feet of factory floor space. Vera Rubin is in full production, co-designed with NVIDIA DSX and DSX MaxLPS to deliver the lowest token cost and maximum tokens per watt. Congratulations to our partners who have their engineering racks up and running: CoreWeave, Dell Technologies, Microsoft, and Oracle Cloud.

NVIDIA AI Infrastructure

16,931 Aufrufe • vor 2 Monaten

The NVIDIA Vera Rubin NVL72 scale-up fabric, built on the sixth-generation NVIDIA NVLink. The NVLink 6 Switch trays connect 72 Rubin GPUs in an all-to-all configuration. The NVLink spine—over 5,000 copper cables make up the vertical NVLink traverse. It securely moves more data than the entire bandwidth of the internet. The single-wide NVIDIA MGX rack design unlocks a cable-free, horizontal NVLink scale-up traverse, running directly on PCB for resiliency at scale. This is how NVIDIA Vera Rubin NVL72 works as one, designed to deliver the lowest token cost and 10x more performance per watt than the previous generation. #NVIDIAVeraRubin

The NVIDIA Vera Rubin NVL72 scale-up fabric, built on the sixth-generation NVIDIA NVLink. The NVLink 6 Switch trays connect 72 Rubin GPUs in an all-to-all configuration. The NVLink spine—over 5,000 copper cables make up the vertical NVLink traverse. It securely moves more data than the entire bandwidth of the internet. The single-wide NVIDIA MGX rack design unlocks a cable-free, horizontal NVLink scale-up traverse, running directly on PCB for resiliency at scale. This is how NVIDIA Vera Rubin NVL72 works as one, designed to deliver the lowest token cost and 10x more performance per watt than the previous generation. #NVIDIAVeraRubin

NVIDIA AI Infrastructure

39,134 Aufrufe • vor 2 Monaten

Sachin Katti says OpenAI had Astra optimize its own inference on NVIDIA's Rubin chip, adding another 2x in 72 hours "It's a testament to NVIDIA's hardware that out of the box we got a 3x improvement in throughput. We literally took the same code that we had optimized for Blackwell, ran it on Rubin, got 3x improvement." "Then, can we actually make Astra run agents using Astra as the base model, go optimize this inference of Astra itself on Rubin? It's a recursive loop. The model is figuring out how to optimize its own inference serving on Rubin chips, and over 72 hours the model produced another 2x improvement on the throughput." "Things that would take weeks, if not months, to optimize in the past, we now are getting models that are very capable of actually producing over a weekend." _______ Link and more from Sachin Katti's conversation:

Sachin Katti says OpenAI had Astra optimize its own inference on NVIDIA's Rubin chip, adding another 2x in 72 hours "It's a testament to NVIDIA's hardware that out of the box we got a 3x improvement in throughput. We literally took the same code that we had optimized for Blackwell, ran it on Rubin, got 3x improvement." "Then, can we actually make Astra run agents using Astra as the base model, go optimize this inference of Astra itself on Rubin? It's a recursive loop. The model is figuring out how to optimize its own inference serving on Rubin chips, and over 72 hours the model produced another 2x improvement on the throughput." "Things that would take weeks, if not months, to optimize in the past, we now are getting models that are very capable of actually producing over a weekend." _______ Link and more from Sachin Katti's conversation:

Fireside Alpha

40,013 Aufrufe • vor 11 Tagen

📢 BREAKING: Nvidia, Microsoft, and Anthropic announce new strategic AI partnerships of roughly $45 Billion. They struck a 3-way deal where Anthropic commits $30B for Azure compute with capacity up to 1GW, and NVIDIA and Microsoft plan to invest up to $10B and $5B in Anthropic. Claude will scale on Azure using NVIDIA systems, which expands access for enterprises and makes Claude available across the major clouds. Anthropic and NVIDIA will co-design to tune both model stacks and future chips for performance, efficiency, and total cost of ownership, so the models run faster per dollar. The first wave targets NVIDIA Grace Blackwell and Vera Rubin systems, which pair high-bandwidth memory with dense interconnect to push training and inference throughput. Microsoft will expose Anthropic models in Azure AI Foundry, including Claude Sonnet 4.5, Claude Opus 4.1, and Claude Haiku 4.5, so teams can pick latency and cost profiles. Claude will also remain available across the Copilot family, which means coding, document automation, and workflow agents can call the same model line. The 1GW commitment signals many thousands of GPUs worth of capacity, which reduces queue times and stabilizes unit economics for long-running training runs. The joint optimization loop, models shaping chips and chips shaping models, typically yields double-digit efficiency gains over off-the-shelf deployments.

📢 BREAKING: Nvidia, Microsoft, and Anthropic announce new strategic AI partnerships of roughly $45 Billion. They struck a 3-way deal where Anthropic commits $30B for Azure compute with capacity up to 1GW, and NVIDIA and Microsoft plan to invest up to $10B and $5B in Anthropic. Claude will scale on Azure using NVIDIA systems, which expands access for enterprises and makes Claude available across the major clouds. Anthropic and NVIDIA will co-design to tune both model stacks and future chips for performance, efficiency, and total cost of ownership, so the models run faster per dollar. The first wave targets NVIDIA Grace Blackwell and Vera Rubin systems, which pair high-bandwidth memory with dense interconnect to push training and inference throughput. Microsoft will expose Anthropic models in Azure AI Foundry, including Claude Sonnet 4.5, Claude Opus 4.1, and Claude Haiku 4.5, so teams can pick latency and cost profiles. Claude will also remain available across the Copilot family, which means coding, document automation, and workflow agents can call the same model line. The 1GW commitment signals many thousands of GPUs worth of capacity, which reduces queue times and stabilizes unit economics for long-running training runs. The joint optimization loop, models shaping chips and chips shaping models, typically yields double-digit efficiency gains over off-the-shelf deployments.

Rohan Paul

39,781 Aufrufe • vor 10 Monaten

Most speculative decoding still drafts tokens one at a time. That's not parallel generation — it just hides the serial loop behind a smaller model. UC San Diego's z-lab just drew a clear line between the two. They released DFlash — a lightweight block diffusion model that drafts a whole block of tokens in a single forward pass, then lets the target model verify the block in parallel. Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell. No token-by-token drafting anywhere in the speculative path. Here's what's actually interesting: → The drafter is conditioned on the target model's own hidden features, injected into the Key/Value cache of every draft layer — so acceptance length scales with draft depth instead of diluting away → A 5-layer drafter replaces the 7B diffusion drafters that capped earlier methods near 3–4× → MATH-500 speedup: 6.08× vs. 1.81× for EAGLE-3 (4.86× average vs. 1.76×, Qwen3-8B, greedy) → Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell — at the same interactivity target → Lossless: the target still verifies every token, so output quality is preserved Full analysis: Paper: NVIDIA's metrics: Project: Model weights: Repo: NVIDIA AI NVIDIA AIDev

Most speculative decoding still drafts tokens one at a time. That's not parallel generation — it just hides the serial loop behind a smaller model. UC San Diego's z-lab just drew a clear line between the two. They released DFlash — a lightweight block diffusion model that drafts a whole block of tokens in a single forward pass, then lets the target model verify the block in parallel. Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell. No token-by-token drafting anywhere in the speculative path. Here's what's actually interesting: → The drafter is conditioned on the target model's own hidden features, injected into the Key/Value cache of every draft layer — so acceptance length scales with draft depth instead of diluting away → A 5-layer drafter replaces the 7B diffusion drafters that capped earlier methods near 3–4× → MATH-500 speedup: 6.08× vs. 1.81× for EAGLE-3 (4.86× average vs. 1.76×, Qwen3-8B, greedy) → Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell — at the same interactivity target → Lossless: the target still verifies every token, so output quality is preserved Full analysis: Paper: NVIDIA's metrics: Project: Model weights: Repo: NVIDIA AI NVIDIA AIDev

Marktechpost AI

23,328 Aufrufe • vor 3 Monaten

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 Aufrufe • vor 4 Monaten

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours

Noisy

80,087 Aufrufe • vor 3 Monaten

$NBIS is showing just how tight AI compute still is as its first Blackwell capacity auction cleared 15% above the company’s previous record price and management says it could already sell out planned 2027 capacity today. That scarcity is translating directly into pricing power with long-term deals yielding ~$25M per MW while 6-month capacity is clearing at ~$50M per MW. That pricing power is now feeding directly into the model with Q2 revenue up 454% YoY, ARR reaching $3B backlog topping $40B and AI cloud margins near 50% while management raises contracted power to more than 5 GW and targets over 1 GW of new capacity annually from 2027.

$NBIS is showing just how tight AI compute still is as its first Blackwell capacity auction cleared 15% above the company’s previous record price and management says it could already sell out planned 2027 capacity today. That scarcity is translating directly into pricing power with long-term deals yielding ~$25M per MW while 6-month capacity is clearing at ~$50M per MW. That pricing power is now feeding directly into the model with Q2 revenue up 454% YoY, ARR reaching $3B backlog topping $40B and AI cloud margins near 50% while management raises contracted power to more than 5 GW and targets over 1 GW of new capacity annually from 2027.

Shay Boloor

68,480 Aufrufe • vor 1 Monat

🚨 🇰🇷 Jensen Huang hints at deeper NVIDIA-LG collaboration in robotics and AI data centers NVIDIA CEO Jensen Huang visited LG Twin Towers on June 8 and emphasized the company’s growing partnership with LG after meeting with LG Chairman Koo Kwang-mo and other executives. Huang said LG is a world-class company across many important future technologies and noted that NVIDIA and LG are working together on many areas. He highlighted robotics as one of the most important fields, describing it as the fusion of electronics, mechanical systems, and artificial intelligence. He said NVIDIA is working with LG on motor technology and mechanical systems, which could support future humanoid robotics. Huang also pointed to next-generation data centers, saying future facilities will scale to gigawatt levels and require advanced cooling, power, and infrastructure technologies. He added that there will be “many more announcements” in the near future, suggesting NVIDIA and LG’s cooperation in AI, robotics, and data centers could become more concrete soon.

🚨 🇰🇷 Jensen Huang hints at deeper NVIDIA-LG collaboration in robotics and AI data centers NVIDIA CEO Jensen Huang visited LG Twin Towers on June 8 and emphasized the company’s growing partnership with LG after meeting with LG Chairman Koo Kwang-mo and other executives. Huang said LG is a world-class company across many important future technologies and noted that NVIDIA and LG are working together on many areas. He highlighted robotics as one of the most important fields, describing it as the fusion of electronics, mechanical systems, and artificial intelligence. He said NVIDIA is working with LG on motor technology and mechanical systems, which could support future humanoid robotics. Huang also pointed to next-generation data centers, saying future facilities will scale to gigawatt levels and require advanced cooling, power, and infrastructure technologies. He added that there will be “many more announcements” in the near future, suggesting NVIDIA and LG’s cooperation in AI, robotics, and data centers could become more concrete soon.

TeslaZoa

35,216 Aufrufe • vor 3 Monaten

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk

Milk Road AI

69,744 Aufrufe • vor 3 Monaten

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 Aufrufe • vor 3 Monaten

From rockets to AI. Nine years after the original NVIDIA DGX-1 handoff, Jensen Huang delivered a brand-new DGX Spark to Elon Musk at Starbase. 🚀✨ The DGX Spark is a petaflop powerhouse built for creators, researchers, and developers — a desktop-sized supercomputer with five times the performance of the first DGX-1. Filmed at SpaceX’s rocket factory in Texas, this moment captures a conversation between the two visionariesI. ➡️ 📹 #SparkSomethingBig

NVIDIA

3,160,581 Aufrufe • vor 11 Monaten