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Most AI projects don't fail because models aren't good enough. They fail because inference economics don't scale. During #CXOSpice, Roman Chernin Nebius broke it down: ✅Closed models kill margins ✅Open models unlock control, if you can run them well ✅DataData only becomes a moat when inference is sustainable One...

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Helen Yu

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13,662 просмотров • 8 месяцев назад •via X (Twitter)

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The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.
1:22

The defining differentiator in AI right now isn't model performance, it's cost per token. Because in this market, efficiency is what turns demand into actual growth. Roman Chernin of Nebius outlines why inference economics are becoming the central challenge, as companies operating on thin margins need infrastructure that can scale without eroding profitability. Token Factory reflects that shift, focusing on orchestration, optimization, and full-stack control to make AI systems economically viable.

Six Five Media

294,803 просмотров • 5 месяцев назад

My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us
39:46

My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us

Elliot Garreffa

29,888 просмотров • 7 месяцев назад

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin
56:22

Interview with Nebius Co-Founder Roman Chernin Please like & share this video so that all $NBIS investors on X will see it! :) If you prefer watching on YouTube: Timestamps: 00:00 - Why AI Infrastructure Is So Hard to Understand 00:24 - Market Fragmentation and What Actually Differentiates Providers 01:30 - Consolidation, Segmentation, and the Future AI Cloud Landscape 02:56 - What Analysts and VCs Still Get Wrong About AI Infrastructure 05:34 - Nebius Cloud: Product Readiness and Customer Proof Points 07:42 - Why Inference Workloads Are Exploding 09:11 - Training vs. Inference: How AI Models Actually Reach Production 10:10 - Why Inference Market Share May Concentrate Around a Few Winners 12:36 - Customer Use Cases: Coding, Enterprise AI, and Real-World Adoption 14:01 - Why Integrated Training and Inference Matter Strategically 16:01 - Building Scalable AI Infrastructure With High Utilization 18:24 - Token Factory: Inference as a Managed Service 20:24 - Revolut Case Study: AI-Driven Product Enhancements 22:56 - Token Factory Performance Optimization and Competitive Advantage 25:07 - Scale, Capacity, and Efficiency as Growth Drivers 28:36 - Why Inference Capacity Could Become the Next Major Bottleneck 30:10 - How Nebius Benchmarks Performance Across Providers 33:14 - The Future Size and Shape of the Inference Market 36:38 - Value-Based Pricing: Moving Beyond Cost per GPU Hour 40:55 - How Nebius Wins Deals: Quality, Performance, and Customer Experience 44:53 - Autonomous AI Platforms and the Rise of Agent-Based Models 47:28 - Tavily, Agentic Applications, and the Next Layer of the AI Stack 50:45 - Strategic Trade-Offs: Scaling, Product Roadmap, and Customer Relevance 55:40 - Final Thoughts: Adapting to the Next Shift in AI Workloads Nebius Roman Chernin

Daniel Koss

204,842 просмотров • 5 месяцев назад

50% of my consulting work right now is helping companies use open-source models at scale. Everyone knows how to use an open-source LLM on their computers, but it's really hard to do this at scale for thousands of users. Here is how this plays out: 1. A team builds a prototype using DeepSeek. 2. Everything looks good. It works! 3. They follow an online guide to deploy the model online. 4. They ask 10 users to try the app. 5. Latency spikes everywhere. 6. The entire system halts. 7. They blame DeepSeek and try again using a new model. The problem is always with scaling inference, not the model. Here is one recommendation I give companies: Check out Nebius Token Factory if you don't want to ever think about deploying an open-source model again. This is a managed inference platform for deploying open-source LLMs at scale. This is not for prototypes or research experiments. This is for when you have a real application with real users. Three important notes about Token Factory: • You have complete control over how inference runs. • You have predictable tail latency (P99, not averages). • No surprise costs when you scale up. You can preplan your budget. Check it out here: Here are two codes you can use to get 100 hours of GPU usage on me: ymJLFa2ARYSKEdqb AdckcZaYjm7KqYY7 Thanks to the Nebius team for their continuous partnership.
2:40

50% of my consulting work right now is helping companies use open-source models at scale. Everyone knows how to use an open-source LLM on their computers, but it's really hard to do this at scale for thousands of users. Here is how this plays out: 1. A team builds a prototype using DeepSeek. 2. Everything looks good. It works! 3. They follow an online guide to deploy the model online. 4. They ask 10 users to try the app. 5. Latency spikes everywhere. 6. The entire system halts. 7. They blame DeepSeek and try again using a new model. The problem is always with scaling inference, not the model. Here is one recommendation I give companies: Check out Nebius Token Factory if you don't want to ever think about deploying an open-source model again. This is a managed inference platform for deploying open-source LLMs at scale. This is not for prototypes or research experiments. This is for when you have a real application with real users. Three important notes about Token Factory: • You have complete control over how inference runs. • You have predictable tail latency (P99, not averages). • No surprise costs when you scale up. You can preplan your budget. Check it out here: Here are two codes you can use to get 100 hours of GPU usage on me: ymJLFa2ARYSKEdqb AdckcZaYjm7KqYY7 Thanks to the Nebius team for their continuous partnership.

Santiago

48,605 просмотров • 7 месяцев назад

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. Timestamps 00:00 – Dylan's role at Nebius and Nebius Token Factory 01:48 – Dylan's investing philosophy and portfolio approach 05:07 – How working in AI infrastructure influences his investing 08:29 – Training vs. inference and why inference demand could explode 13:22 – Enterprise AI adoption: from POCs to production 18:02 – Open-source vs. closed/frontier models 24:44 – The economics of open vs. closed AI models 29:27 – Where the next AI infrastructure bottlenecks could emerge 31:22 – Dylan's AI Bottlenecks project and approach to stock selection 34:06 – Closing thoughts Key Insights (AI Summary, so you don't have to copy paste and prompt for exactly that ;D) “I seem to like areas where the demand really looks kind of secular, but the supply is genuinely hard to create.” → Implication: The most attractive AI trades may sit in physical bottlenecks where supply cannot quickly respond to demand. “The bottleneck is who has the pricing power and kind of what might get commoditized and where the concentrate might move next.” → Implication: Value capture across the AI stack will keep shifting as individual layers become scarce or commoditized. “Training creates the intelligence and then the inference actually monetizes and distributes.” → Implication: Training and inference are complementary, rather than one ultimately replacing the other. “One user action can become dozens or hundreds of model calls, tools calls, and like verification steps, retries.” → Implication: Agentic AI can drive token consumption far faster than user growth alone would suggest. “The best infra for making any model and the best infra for serving a billion interactions are not necessarily the same.” → Implication: Training and inference could increasingly require different hardware and infrastructure architectures. “The Frontier Labs might be incentivized to run more and more of the inference of these models for internal research instead of providing it to external people.” → Implication: The most capable models and their compute could increasingly be used internally to accelerate frontier research rather than monetized externally. “Enterprise AI adoption is actually much further along than a lot of people kind of think. But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.
34:29

Quick chat with dylan ツ (Dylan Bristot, GTM @ $NBIS). Also on YouTube (link in first comment) for those who prefer to watch/listen there. Timestamps 00:00 – Dylan's role at Nebius and Nebius Token Factory 01:48 – Dylan's investing philosophy and portfolio approach 05:07 – How working in AI infrastructure influences his investing 08:29 – Training vs. inference and why inference demand could explode 13:22 – Enterprise AI adoption: from POCs to production 18:02 – Open-source vs. closed/frontier models 24:44 – The economics of open vs. closed AI models 29:27 – Where the next AI infrastructure bottlenecks could emerge 31:22 – Dylan's AI Bottlenecks project and approach to stock selection 34:06 – Closing thoughts Key Insights (AI Summary, so you don't have to copy paste and prompt for exactly that ;D) “I seem to like areas where the demand really looks kind of secular, but the supply is genuinely hard to create.” → Implication: The most attractive AI trades may sit in physical bottlenecks where supply cannot quickly respond to demand. “The bottleneck is who has the pricing power and kind of what might get commoditized and where the concentrate might move next.” → Implication: Value capture across the AI stack will keep shifting as individual layers become scarce or commoditized. “Training creates the intelligence and then the inference actually monetizes and distributes.” → Implication: Training and inference are complementary, rather than one ultimately replacing the other. “One user action can become dozens or hundreds of model calls, tools calls, and like verification steps, retries.” → Implication: Agentic AI can drive token consumption far faster than user growth alone would suggest. “The best infra for making any model and the best infra for serving a billion interactions are not necessarily the same.” → Implication: Training and inference could increasingly require different hardware and infrastructure architectures. “The Frontier Labs might be incentivized to run more and more of the inference of these models for internal research instead of providing it to external people.” → Implication: The most capable models and their compute could increasingly be used internally to accelerate frontier research rather than monetized externally. “Enterprise AI adoption is actually much further along than a lot of people kind of think. But probably less mature than the headlines suggest.” → Implication: Enterprise demand is real, but deployment maturity still has significant room to improve. “The POC problem might be solved for a lot of companies, but the production problem isn’t yet.” → Implication: The enterprise bottleneck is shifting from proving AI works to deploying it reliably, securely and economically at scale. “They feel like it’s time for them to actually not only integrate AI, but build some sort of moat out of the AI.” → Implication: Enterprises increasingly want proprietary AI systems built around their own data rather than simply consuming generic models. “The more autonomous the software becomes, the more infra discipline you need underneath it.” → Implication: Agents increase the importance of inference cost, reliability and infrastructure optimization. “Maybe I have fifteen different versions of very different LLMs, fine tuned on fifteen different kinds of tasks that I’m operating across my business, instead of having a one model fits all.” → Implication: Enterprise AI could evolve toward many specialized models rather than one frontier model handling every workload. “I don’t necessarily think it’s open versus closed. That might be the wrong framing.” → Implication: Open and closed models can coexist because they optimize for different customer needs. “Historically the problem was that that control came with a massive operational tax.” → Implication: Better inference infrastructure can make open models materially more competitive by removing the complexity traditionally associated with running them. “I don’t think open needs to beat the best closed model on every single benchmark. It just basically needs to be good enough for the workload of the given customer while offering a much better combination of control, cost, and deployment flexibility.” → Implication: For production AI, workload-specific economics may matter more than having the absolute smartest model. “Maybe actually the bulk of tokens generated in the future might come from open models.” → Implication: Frontier intelligence could remain dominated by closed labs even while open models capture most production inference volume. “I could really imagine frontier intelligence being really concentrated while most of the production inference becomes super fragmented.” → Implication: AI could consolidate at the intelligence layer while fragmenting heavily at the inference layer across models, GPUs, providers and regions. “I don’t think that necessarily means the margins of open source will be much worse than the ones of closed source.” → Implication: Optimization can potentially make open-model inference highly profitable despite lower pricing. “I think now we’re probably in the middle of phase two... everything feeding the accelerator.” → Implication: The AI trade is broadening beyond GPUs toward networking, packaging, data centers, electrical equipment and power. “It’s no longer about the megawatts, about energized megawatts.” → Implication: Available power on paper matters less than how quickly that power can actually be delivered to operating AI infrastructure. “It’s increasingly about utilisation and conversion now and like how efficiently you convert expensive infra into actual useful AI work.” → Implication: Infrastructure efficiency and utilization become increasingly important as the absolute amount of deployed AI infrastructure grows. “The market tends to really notice demand before it notices what demand breaks.” → Implication: Second-order bottlenecks may offer some of the most interesting opportunities in the next phase of the AI buildout. “The interesting question now is which part of the mine breaks next?” → Implication: Finding the next constraint in the AI supply chain may matter more than simply identifying continued AI demand.

Daniel Koss

49,970 просмотров • 25 дней назад

"Inference, if you look at it as a market, will be much, much bigger than cloud computing was pre-ChatGPT." Lightspeed’s Bucky Moore says inference is an underrated investment category in AI, and expects the market to break up into large, specialized platforms for each modality: "The GPU supply crunch that we're seeing right now is largely, as Dylan Patel has said on the show before, due to the fact that not only these consumer products, but also B2B products like Claude Code and Codex are just really taking off and creating insane demand for inference." "We're talking hundreds of billions in spend every year. And if that's true, I think there will be very, very large inference platforms built in each modality." "So there will be an inference platform for real-time video models, there will be an inference platform for open-source and custom language models, there will be an inference platform built specifically for long-running agents." "So I think we're just going to see that industry, which today looks like one industry, break up into many because of how big it is and how much room for specialization there is."
1:02

"Inference, if you look at it as a market, will be much, much bigger than cloud computing was pre-ChatGPT." Lightspeed’s Bucky Moore says inference is an underrated investment category in AI, and expects the market to break up into large, specialized platforms for each modality: "The GPU supply crunch that we're seeing right now is largely, as Dylan Patel has said on the show before, due to the fact that not only these consumer products, but also B2B products like Claude Code and Codex are just really taking off and creating insane demand for inference." "We're talking hundreds of billions in spend every year. And if that's true, I think there will be very, very large inference platforms built in each modality." "So there will be an inference platform for real-time video models, there will be an inference platform for open-source and custom language models, there will be an inference platform built specifically for long-running agents." "So I think we're just going to see that industry, which today looks like one industry, break up into many because of how big it is and how much room for specialization there is."

TBPN

33,278 просмотров • 6 месяцев назад

Baseten’s CEO on why every company will want to own its AI / Tuhin Srivastava on the rise of AI agents, the demand for inference, and building a new kind of hyperscaler. Lately, I’ve been spending a lot of time thinking about the AI inference market and how big it could get. Agents like Muse, Instinct, Town, and Grok Bot aggressively use browsers, run their own computers, and burn through far more tokens than a traditional chatbot. As more people put them to work (Muse is number two in the App Store), the demand for inference, or the computing needed to run these models, should grow enormously. Tuhin and I discuss the rise of agents using browsers and virtual machines to get things done, and Baseten's recent acquisition to help power that shift. We also talk about the data center backlash, why companies are embracing Chinese open models, his plans for Baseten’s new research lab, and why he thinks inference becomes the only market left after AGI. Timestamps: 00:00 What Is AI Inference? 06:05 Competing With the Cloud Giants 09:24 Why Companies Want to Own Their AI 15:30 Building Baseten Before the AI Boom 23:45 DeepSeek and the Race for Open AI Models 29:32 Baseten’s Growth and Expansion 33:05 AI Agents and the Blaxel Acquisition 38:32 Data Centers and the AI Backlash 43:25 What Happens to Inference After AGI? 45:12 When AI Agents Become Customers Thanks to the show's premier sponsors: Atlassian, Granola, and Mercury.
48:16

Baseten’s CEO on why every company will want to own its AI / Tuhin Srivastava on the rise of AI agents, the demand for inference, and building a new kind of hyperscaler. Lately, I’ve been spending a lot of time thinking about the AI inference market and how big it could get. Agents like Muse, Instinct, Town, and Grok Bot aggressively use browsers, run their own computers, and burn through far more tokens than a traditional chatbot. As more people put them to work (Muse is number two in the App Store), the demand for inference, or the computing needed to run these models, should grow enormously. Tuhin and I discuss the rise of agents using browsers and virtual machines to get things done, and Baseten's recent acquisition to help power that shift. We also talk about the data center backlash, why companies are embracing Chinese open models, his plans for Baseten’s new research lab, and why he thinks inference becomes the only market left after AGI. Timestamps: 00:00 What Is AI Inference? 06:05 Competing With the Cloud Giants 09:24 Why Companies Want to Own Their AI 15:30 Building Baseten Before the AI Boom 23:45 DeepSeek and the Race for Open AI Models 29:32 Baseten’s Growth and Expansion 33:05 AI Agents and the Blaxel Acquisition 38:32 Data Centers and the AI Backlash 43:25 What Happens to Inference After AGI? 45:12 When AI Agents Become Customers Thanks to the show's premier sponsors: Atlassian, Granola, and Mercury.

Alex Heath

25,513 просмотров • 10 дней назад

“We want to make sure smaller LLMs can work for smaller regions as well.” FLock.io’s jsun sees a major gap in the sovereign AI conversation: many countries want AI infrastructure of their own, but lack the compute, talent and data needed to build it. And global frontier models don’t necessarily prioritize their languages or local context. In Sarawak, Malaysia, Jiahao's team worked on decentralized inference for regional dialects — using smaller, locally adapted models for language use cases that might otherwise be overlooked. For Jiahao, the future of AI isn’t only about making models more powerful; it’s also about making them relevant to more people.
1:11

“We want to make sure smaller LLMs can work for smaller regions as well.” FLock.io’s jsun sees a major gap in the sovereign AI conversation: many countries want AI infrastructure of their own, but lack the compute, talent and data needed to build it. And global frontier models don’t necessarily prioritize their languages or local context. In Sarawak, Malaysia, Jiahao's team worked on decentralized inference for regional dialects — using smaller, locally adapted models for language use cases that might otherwise be overlooked. For Jiahao, the future of AI isn’t only about making models more powerful; it’s also about making them relevant to more people.

YZi Labs

32,029 просмотров • 15 дней назад

BREAKING: SambaNova Announces First Close of $1 Billion Series E at $11B Valuation Led by General Atlantic, with participation from Seligman Ventures, T. Rowe Price, & Capital Group SambaNova CEO Rodrigo Liang (Rodrigo Liang) says AI is entering a new era of "premium inference" "The world of inference is running into an economics problem." Enter: new SambaNova "SM50" "A cloud-scale RDU that allows us to deliver premium inference at significantly higher performance than you can get with GPUs, at a fraction of the cost & a fraction of the power." Why does that matter? "When you're running faster with the high-accuracy models, you can charge more at a lower cost, allowing our service providers to generate more margin." Liang says the shift is just beginning: "The world's moving into inference." And after today's funding announcement: "We're using this capital to accelerate our supply chain and make sure we can deliver the racks to customers worldwide." Full interview from Paris coming soon.
2:14

BREAKING: SambaNova Announces First Close of $1 Billion Series E at $11B Valuation Led by General Atlantic, with participation from Seligman Ventures, T. Rowe Price, & Capital Group SambaNova CEO Rodrigo Liang (Rodrigo Liang) says AI is entering a new era of "premium inference" "The world of inference is running into an economics problem." Enter: new SambaNova "SM50" "A cloud-scale RDU that allows us to deliver premium inference at significantly higher performance than you can get with GPUs, at a fraction of the cost & a fraction of the power." Why does that matter? "When you're running faster with the high-accuracy models, you can charge more at a lower cost, allowing our service providers to generate more margin." Liang says the shift is just beginning: "The world's moving into inference." And after today's funding announcement: "We're using this capital to accelerate our supply chain and make sure we can deliver the racks to customers worldwide." Full interview from Paris coming soon.

Molly O’Shea

14,162 просмотров • 2 месяцев назад

When the team from Forbes came to our San Jose headquarters, the conversation quickly became about more than just chips. It became a discussion about the future of AI and what it actually takes to run it at scale. As AI adoption accelerates, the challenge is no longer training models, it’s inference: running models efficiently, reliably, and fast enough to power the next generation of agentic applications. As our CEO and Founder Rodrigo Liang put it: “It's much lower power, much smaller footprint, and it's faster than anybody else. What's not to like?” And as Dharmesh Thakker General Partner of Battery Ventures explained: "The technology that SambaNova has is very complimentary to what Intel has because most AI data centers need a combination of CPUs and GPUs." A big thank you to Anton McGonnell and Vasanth Mohan for helping bring this story to life and for the thoughtful conversations throughout the day. We’re excited about what this shift means in practice, from premium inference and agentic AI to more efficient, sustainable data center infrastructure that lets enterprises deploy AI at scale, not just experiment with it. And thank you to Kirsten Taggart and the team at Forbes for spending the time with us and taking a deeper look at what we’re building. 🎥 Watch the full feature below.
11:43

When the team from Forbes came to our San Jose headquarters, the conversation quickly became about more than just chips. It became a discussion about the future of AI and what it actually takes to run it at scale. As AI adoption accelerates, the challenge is no longer training models, it’s inference: running models efficiently, reliably, and fast enough to power the next generation of agentic applications. As our CEO and Founder Rodrigo Liang put it: “It's much lower power, much smaller footprint, and it's faster than anybody else. What's not to like?” And as Dharmesh Thakker General Partner of Battery Ventures explained: "The technology that SambaNova has is very complimentary to what Intel has because most AI data centers need a combination of CPUs and GPUs." A big thank you to Anton McGonnell and Vasanth Mohan for helping bring this story to life and for the thoughtful conversations throughout the day. We’re excited about what this shift means in practice, from premium inference and agentic AI to more efficient, sustainable data center infrastructure that lets enterprises deploy AI at scale, not just experiment with it. And thank you to Kirsten Taggart and the team at Forbes for spending the time with us and taking a deeper look at what we’re building. 🎥 Watch the full feature below.

SambaNova

226,753 просмотров • 3 месяцев назад

"Mac Minis for example are a very good fit" - Andrej Karpathy Andrej Karpathy shouted out my work on EXO Labs in his keynote at Y Combinator AI SUS! Here's the breakdown: Right now most AI workloads run in the cloud where requests from different users are continuously batched together. These workloads are FLOPS-bound and favors hardware with the best unit economics of $ per FLOP, i.e. enterprise GPUs. The personal computing revolution will shift these workloads to personal devices with lower batch sizes (mostly batch_size=1). batch_size=1 inference is memory-bound, because all of the model parameters need to be loaded into the GPU every time a token is generated. Apple Silicon with its Unified Memory architecture has a lot of memory and memory bandwidth per $ compared to other hardware: - M4 Pro Mac Mini, 24GB @ 273GB/s, $58.33/GB, $5.13/GB/s - H100, 80GB @ 3350GB/s, $625/GB, $14.93/GB/s The unit economics of Apple Silicon are becoming more compelling with every release of the Mac. The future of AI inference looks more like open weights models (OpenAI open weights model soonTM) run at low batch_size on personal devices.
0:20

"Mac Minis for example are a very good fit" - Andrej Karpathy Andrej Karpathy shouted out my work on EXO Labs in his keynote at Y Combinator AI SUS! Here's the breakdown: Right now most AI workloads run in the cloud where requests from different users are continuously batched together. These workloads are FLOPS-bound and favors hardware with the best unit economics of $ per FLOP, i.e. enterprise GPUs. The personal computing revolution will shift these workloads to personal devices with lower batch sizes (mostly batch_size=1). batch_size=1 inference is memory-bound, because all of the model parameters need to be loaded into the GPU every time a token is generated. Apple Silicon with its Unified Memory architecture has a lot of memory and memory bandwidth per $ compared to other hardware: - M4 Pro Mac Mini, 24GB @ 273GB/s, $58.33/GB, $5.13/GB/s - H100, 80GB @ 3350GB/s, $625/GB, $14.93/GB/s The unit economics of Apple Silicon are becoming more compelling with every release of the Mac. The future of AI inference looks more like open weights models (OpenAI open weights model soonTM) run at low batch_size on personal devices.

Alex Cheema

103,604 просмотров • 1 год назад

Today we're incredibly excited to announce Dreamscale Labs (YC F26) (YC F26)! We're an applied research company putting robot brains in the cloud. Robot AI models are growing larger and on-board compute is getting increasingly inaccessible due to cost. We're taking a contrarian bet that the future is moving inference off the robot and into the cloud, while still meeting real-time deadlines required by robot motion. We're building the real-time inference infrastructure that enables frontier physical AI models to run smoothly on your robot. We optimize for low latency across the full stack: from the model, down to the GPU kernels, and over the network. Our mission is to unlock a future of safe and intelligent robots doing meaningful work, and we think this is only possible by letting you deploy and run robot models in the cloud. Reach out to us at founders@dreamscalelabs.com or visit our website at to learn more. Kevin L Antoine Nguyen Eric R
1:50

Today we're incredibly excited to announce Dreamscale Labs (YC F26) (YC F26)! We're an applied research company putting robot brains in the cloud. Robot AI models are growing larger and on-board compute is getting increasingly inaccessible due to cost. We're taking a contrarian bet that the future is moving inference off the robot and into the cloud, while still meeting real-time deadlines required by robot motion. We're building the real-time inference infrastructure that enables frontier physical AI models to run smoothly on your robot. We optimize for low latency across the full stack: from the model, down to the GPU kernels, and over the network. Our mission is to unlock a future of safe and intelligent robots doing meaningful work, and we think this is only possible by letting you deploy and run robot models in the cloud. Reach out to us at [email protected] or visit our website at to learn more. Kevin L Antoine Nguyen Eric R

Chris Yoo

47,304 просмотров • 8 дней назад

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."
1:34

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."

TBPN

16,294 просмотров • 7 месяцев назад

great shoutout from Chamath Palihapitiya on the all in pod today. "you need to go to a vendor that you trust, could be Nebius" perfect recap of why, if your data actually matters, an open source inference path like Nebius Token Factory is the way to go his point, in plain english: you paste a protein design, a deal strategy, a process that is the company into a shared chat api. The vendor says “zero data retention", but that usually means best efforts, not a hard guarantee. then someone hits like / thumbs-up, or the terms allow training on “insights,” not names, or the next model version just… knows the shape of the answer you thought was private (like in the Navier Stokes case) the scary leak ends up being the solution pattern showing up somewhere you don’t control. so the move isn’t “never use ai”, it’s stop putting crown jewel work on a shared frontier api stand it up on your terms: open weight models, inference on hardware that’s you can control, a vendor that provisions your stack, the lane Chamath pointed at (aws class, Nebius Token Factory, Fireworks, etc.) Boards are late to this. “We signed zdr” is starting to look thin next to “do we actually control where this runs.” That’s when the CIO who took the easy api deal becomes the problem
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great shoutout from Chamath Palihapitiya on the all in pod today. "you need to go to a vendor that you trust, could be Nebius" perfect recap of why, if your data actually matters, an open source inference path like Nebius Token Factory is the way to go his point, in plain english: you paste a protein design, a deal strategy, a process that is the company into a shared chat api. The vendor says “zero data retention", but that usually means best efforts, not a hard guarantee. then someone hits like / thumbs-up, or the terms allow training on “insights,” not names, or the next model version just… knows the shape of the answer you thought was private (like in the Navier Stokes case) the scary leak ends up being the solution pattern showing up somewhere you don’t control. so the move isn’t “never use ai”, it’s stop putting crown jewel work on a shared frontier api stand it up on your terms: open weight models, inference on hardware that’s you can control, a vendor that provisions your stack, the lane Chamath pointed at (aws class, Nebius Token Factory, Fireworks, etc.) Boards are late to this. “We signed zdr” is starting to look thin next to “do we actually control where this runs.” That’s when the CIO who took the easy api deal becomes the problem

dylan ツ

35,113 просмотров • 13 дней назад

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers
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🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

Vikas gupta

14,138 просмотров • 15 дней назад

Connecting all intelligence with ORA ⏺️ So, how does it work? Let's dive in 👇 At the heart of ORA is the Onchain AI Oracle (OAO), designed to empower dApp developers by seamlessly integrating verifiable AI inference into their applications without sacrificing decentralization. At the center of ORA is Optimistic Machine Learning (opML). This technology ensures that AI inferences originate from the correct models and that results remain untampered. A robust network of nodes independently verifies each AI inference and can raise challenges if tampering is suspected. Want to be part of this decentralized movement? You can easily run your own node using Tora, our user-friendly node software. Built on Optimistic Machine Learning (opML), our Onchain AI Oracle connects the best open-source AI models to the blockchain. This means any dApp can easily become an AI dApp! With our Initial Model Offering (IMO), developers can tokenize open-source models, effectively overcoming the cold start problem by securing essential funding for their projects. ORA is integrated with several L2s, including Mantle Network, Linea.eth, and Arbitrum. This allows you to build on low-cost chains that best suit your needs. ORA is committed to bringing decentralized, verifiable AI to the blockchain. Are you ready to join us? Use OAO: Read more about OAO: Learn about IMO: Use Tora:
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Connecting all intelligence with ORA ⏺️ So, how does it work? Let's dive in 👇 At the heart of ORA is the Onchain AI Oracle (OAO), designed to empower dApp developers by seamlessly integrating verifiable AI inference into their applications without sacrificing decentralization. At the center of ORA is Optimistic Machine Learning (opML). This technology ensures that AI inferences originate from the correct models and that results remain untampered. A robust network of nodes independently verifies each AI inference and can raise challenges if tampering is suspected. Want to be part of this decentralized movement? You can easily run your own node using Tora, our user-friendly node software. Built on Optimistic Machine Learning (opML), our Onchain AI Oracle connects the best open-source AI models to the blockchain. This means any dApp can easily become an AI dApp! With our Initial Model Offering (IMO), developers can tokenize open-source models, effectively overcoming the cold start problem by securing essential funding for their projects. ORA is integrated with several L2s, including Mantle Network, Linea.eth, and Arbitrum. This allows you to build on low-cost chains that best suit your needs. ORA is committed to bringing decentralized, verifiable AI to the blockchain. Are you ready to join us? Use OAO: Read more about OAO: Learn about IMO: Use Tora:

ORA

453,792 просмотров • 2 лет назад

NEW: Premium Inference 101 The Economics & Infrastructure Behind Running Trillion Parameter Models Rodrigo Liang, CEO & Co-Founder of SambaNova "Inference has arrived. 70-80% of those racks are running inference." "[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable." "With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks." SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW. They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis. The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens. Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic. We Cover: › Why inference will need orders of magnitude more chips than training ever did › The 10kW rack vs the 130kW rack, & why air cooling decides geography › Running a 1T parameter model in one rack at full precision, no quantization › The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone › Revenue per rack, & why inference providers have revenue but no margin › JPMorgan, sovereignty, & the move back to on-prem Filmed at the RAISE Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems (00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation (03:00) The Inference problem nobody saw coming (04:52) SambaNova's chip evolution (07:19) Running a trillion-parameter model on a single rack (11:00) Do $100 billion data centers actually make sense? (14:14) What "premium inference" really means (18:28) Speed is about to become AI's biggest price tag (20:43) Starlink, edge computing, & AI reaching every corner of the planet (24:27) Working alongside NVIDIA & rival chipmakers (27:49) How customers actually measure inference performance (32:07) The biggest bottlenecks in AI's global land grab (35:12) Justifying the billion-dollar AI valuations (37:53) Why SambaNova refuses to build its own cloud (41:03) The "AI sovereignty" debate (43:48) Data privacy fears are driving the return to on-prem AI (47:55) How to actually get ROI out of AI spend (51:16) The one question every business should be asking about AI (56:09) The mentors & lessons behind a 32-year career in chips (58:02) Unveiling SambaNova's newest chip, the SN50
59:56

NEW: Premium Inference 101 The Economics & Infrastructure Behind Running Trillion Parameter Models Rodrigo Liang, CEO & Co-Founder of SambaNova "Inference has arrived. 70-80% of those racks are running inference." "[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable." "With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks." SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW. They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis. The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens. Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic. We Cover: › Why inference will need orders of magnitude more chips than training ever did › The 10kW rack vs the 130kW rack, & why air cooling decides geography › Running a 1T parameter model in one rack at full precision, no quantization › The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone › Revenue per rack, & why inference providers have revenue but no margin › JPMorgan, sovereignty, & the move back to on-prem Filmed at the RAISE Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems (00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation (03:00) The Inference problem nobody saw coming (04:52) SambaNova's chip evolution (07:19) Running a trillion-parameter model on a single rack (11:00) Do $100 billion data centers actually make sense? (14:14) What "premium inference" really means (18:28) Speed is about to become AI's biggest price tag (20:43) Starlink, edge computing, & AI reaching every corner of the planet (24:27) Working alongside NVIDIA & rival chipmakers (27:49) How customers actually measure inference performance (32:07) The biggest bottlenecks in AI's global land grab (35:12) Justifying the billion-dollar AI valuations (37:53) Why SambaNova refuses to build its own cloud (41:03) The "AI sovereignty" debate (43:48) Data privacy fears are driving the return to on-prem AI (47:55) How to actually get ROI out of AI spend (51:16) The one question every business should be asking about AI (56:09) The mentors & lessons behind a 32-year career in chips (58:02) Unveiling SambaNova's newest chip, the SN50

Molly O’Shea

134,960 просмотров • 2 месяцев назад

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.
4:44

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 просмотров • 2 месяцев назад

🇨🇳 🇺🇸 CHINA'S AI STACK IS GETTING DANGEROUSLY COMPETITIVE. Huawei's Ascend chips are now powering DeepSeek at a fraction of Western inference costs. The result: DeepSeek just made its 75% API price cut permanent on DeepSeek-V4-Pro. This is not a promotion. This is the new floor. The full vertical stack outside US sanctions and outside Nvidia's margins. DeepSeek can keep cutting. Chinese tech gets cheaper every quarter. A massive global undercut is no longer a question of if, only when. Western AI labs are running on $30,000 Nvidia chips at full markup. DeepSeek is running on Huawei silicon at cost. The unit economics aren't even in the same conversation. Every export ban that pushed China toward Huawei is paying off, for China.
0:22

🇨🇳 🇺🇸 CHINA'S AI STACK IS GETTING DANGEROUSLY COMPETITIVE. Huawei's Ascend chips are now powering DeepSeek at a fraction of Western inference costs. The result: DeepSeek just made its 75% API price cut permanent on DeepSeek-V4-Pro. This is not a promotion. This is the new floor. The full vertical stack outside US sanctions and outside Nvidia's margins. DeepSeek can keep cutting. Chinese tech gets cheaper every quarter. A massive global undercut is no longer a question of if, only when. Western AI labs are running on $30,000 Nvidia chips at full markup. DeepSeek is running on Huawei silicon at cost. The unit economics aren't even in the same conversation. Every export ban that pushed China toward Huawei is paying off, for China.

CryptoGoos

43,232 просмотров • 4 месяцев назад