Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

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...

YuHelenYu's profile picture

Helen Yu

59,078 subscribers

13,662 Aufrufe • vor 5 Monaten •via X (Twitter)

#CXOSpice
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

⚡️Today marks a big milestone for Nebius. We’re launching Nebius Token Factory, the evolution of Nebius AI Studio, built to make open-source AI production-grade. Token Factory transforms raw open models into governed, scalable systems with dedicated inference, sub-second latency, 99.9% uptime and zero-retention compliance. It’s where inference, post-training and governance converge, turning raw compute into reliable intelligence. Run AI inference at scale:
0:53

⚡️Today marks a big milestone for Nebius. We’re launching Nebius Token Factory, the evolution of Nebius AI Studio, built to make open-source AI production-grade. Token Factory transforms raw open models into governed, scalable systems with dedicated inference, sub-second latency, 99.9% uptime and zero-retention compliance. It’s where inference, post-training and governance converge, turning raw compute into reliable intelligence. Run AI inference at scale:

Nebius Token Factory

259,764 Aufrufe • vor 7 Monaten

$NBIS co-founder Roman Chernin explained why the next AI bottleneck is running inference efficiently at scale. That’s why Nebius is building Token Factory around model optimization, orchestration & agentic AI deployment instead of acting like a basic GPU rental business.
1:42

$NBIS co-founder Roman Chernin explained why the next AI bottleneck is running inference efficiently at scale. That’s why Nebius is building Token Factory around model optimization, orchestration & agentic AI deployment instead of acting like a basic GPU rental business.

Shay Boloor

129,512 Aufrufe • vor 1 Monat

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering
27:58

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

Madison Kanna

119,268 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 2 Monaten

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

29,119 Aufrufe • vor 4 Monaten

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

203,301 Aufrufe • vor 1 Monat

$META is betting on $AMD for AI inference. As AI models become more commoditized, total cost of ownership for token generation matters more than ever. $AMD GPUs are far more cost-efficient than $NVDA for inference. With 3.5B daily users ready to use its AI models, $META is going all-in on $AMD
3:17

$META is betting on $AMD for AI inference. As AI models become more commoditized, total cost of ownership for token generation matters more than ever. $AMD GPUs are far more cost-efficient than $NVDA for inference. With 3.5B daily users ready to use its AI models, $META is going all-in on $AMD

Daniel

20,558 Aufrufe • vor 4 Monaten

A glimpse into one of our core features 👀 Chat, Search, Compare, & Run #Inference for all AI models on ONE Platform: No more complicated setups, just seamless #AI magic. 💫 Be the first to experience - join waitlist now: #Inferium #AI #Inference
0:43

A glimpse into one of our core features 👀 Chat, Search, Compare, & Run #Inference for all AI models on ONE Platform: No more complicated setups, just seamless #AI magic. 💫 Be the first to experience - join waitlist now: #Inferium #AI #Inference

Inferium

1,079,129 Aufrufe • vor 1 Jahr

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”
0:52

IN NEWS: Baseten raises a $150M series D round. Tuhin Srivastava (Founder & CEO, Baseten) on the future of inference: “I think the token price goes down and inference should get cheaper over time. And that really just means there is going to be more inference.” “Every time we lower prices or optimize models to make it cheaper, four months later customers are spending more anyway.” “Inference prices will go down, but if the world is run by AI in 10 years, there is going to be a lot of inference. It better be cheap.”

TBPN

17,057 Aufrufe • vor 9 Monaten

AkashML is live! A fully managed AI inference service on Akash Network. Deploying open models like DeepSeek shouldn’t cost $50K/month or require complex setup. AkashML makes inference simple, affordable, and automatic, so developers can run AI workloads on decentralized GPUs with ease. Try it out here 👇 Read More about it Here 👇
0:39

AkashML is live! A fully managed AI inference service on Akash Network. Deploying open models like DeepSeek shouldn’t cost $50K/month or require complex setup. AkashML makes inference simple, affordable, and automatic, so developers can run AI workloads on decentralized GPUs with ease. Try it out here 👇 Read More about it Here 👇

Akash Network

37,017 Aufrufe • vor 6 Monaten

$NVDA CEO Jensen Huang praised $NBIS during his keynote. Nebius is positioning itself as a cloud utility for the agentic AI era built to deliver inference and AI compute efficiently at scale.
0:21

$NVDA CEO Jensen Huang praised $NBIS during his keynote. Nebius is positioning itself as a cloud utility for the agentic AI era built to deliver inference and AI compute efficiently at scale.

Shay Boloor

345,249 Aufrufe • vor 19 Tagen

From POC to production inference. 🤖🌁 Join us at #DOdeploy in San Francisco where we'll discuss inference-first architectures, predictable inference economics, operational simplicity at scale, & more.
0:26

From POC to production inference. 🤖🌁 Join us at #DOdeploy in San Francisco where we'll discuss inference-first architectures, predictable inference economics, operational simplicity at scale, & more.

DigitalOcean

12,195 Aufrufe • vor 3 Monaten

BOOM! Huge update for AI app developers by Hugging Face you can now deploy models directly from Hugging Face with Gradio while choosing your preferred inference provider from SambaNova, Hyperbolic, Together AI, Fireworks AI, Replicate, fal, Nebius, Novita AI Additionally, you can choose to require Space users to log in, ensuring that inference usage is billed to their accounts
0:30

BOOM! Huge update for AI app developers by Hugging Face you can now deploy models directly from Hugging Face with Gradio while choosing your preferred inference provider from SambaNova, Hyperbolic, Together AI, Fireworks AI, Replicate, fal, Nebius, Novita AI Additionally, you can choose to require Space users to log in, ensuring that inference usage is billed to their accounts

AK

59,346 Aufrufe • vor 1 Jahr

AI answers your questions in seconds, but behind that speed is something called inference—the compute-intensive process where trained models generate responses. At AWS, we've built custom chips like Trainium, intelligent routing systems, and unified infrastructure to make inference faster and more affordable. As AI agents handle complex multi-step tasks, inference accounts for 80-90% of AI computing power. We're engineering at planetary scale to keep those milliseconds reliable.
0:25

AI answers your questions in seconds, but behind that speed is something called inference—the compute-intensive process where trained models generate responses. At AWS, we've built custom chips like Trainium, intelligent routing systems, and unified infrastructure to make inference faster and more affordable. As AI agents handle complex multi-step tasks, inference accounts for 80-90% of AI computing power. We're engineering at planetary scale to keep those milliseconds reliable.

Amazon Web Services

27,021 Aufrufe • vor 3 Monaten

Ready to experience the future of #AI? #Inferium's MVP is coming. Be among the first to: 🧠 Evaluate cutting-edge AI models ⚡ Run #inference on AI models 💰 Earn rewards & top the leaderboard Join waitlist now to secure early access and exclusive perks: #Inferium #MVP #AI #Inference
0:30

Ready to experience the future of #AI? #Inferium's MVP is coming. Be among the first to: 🧠 Evaluate cutting-edge AI models ⚡ Run #inference on AI models 💰 Earn rewards & top the leaderboard Join waitlist now to secure early access and exclusive perks: #Inferium #MVP #AI #Inference

Inferium

37,311 Aufrufe • vor 1 Jahr

2026: the year of explosive AI innovation. Fireworks AI Co-founder & CEO Lin Qiao speaks on how the massive expansion of customized inference and models is creating speed of light production to scale timelines.
0:20

2026: the year of explosive AI innovation. Fireworks AI Co-founder & CEO Lin Qiao speaks on how the massive expansion of customized inference and models is creating speed of light production to scale timelines.

NVIDIA

35,716 Aufrufe • vor 1 Monat

Jim Cramer on Leopold Aschenbrenner’s $NBIS stake “When this man touches something, it turns to gold.” The next AI bottleneck is efficient inference at scale which is why Nebius Token Factory is built around optimization, orchestration and agentic deployment rather than basic GPU rental.
0:42

Jim Cramer on Leopold Aschenbrenner’s $NBIS stake “When this man touches something, it turns to gold.” The next AI bottleneck is efficient inference at scale which is why Nebius Token Factory is built around optimization, orchestration and agentic deployment rather than basic GPU rental.

Shay Boloor

288,386 Aufrufe • vor 19 Tagen

ROCm 7 is here! ✅MI350 Series support ✅Enhanced usability and performance ✅Advanced AI features (distributed inference, large-scale training) ✅Easier enterprise AI deployment
0:35

ROCm 7 is here! ✅MI350 Series support ✅Enhanced usability and performance ✅Advanced AI features (distributed inference, large-scale training) ✅Easier enterprise AI deployment

AMD

24,099 Aufrufe • vor 11 Monaten

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

48,443 Aufrufe • vor 4 Monaten

BG2. Ep 17. Double $NVDA! System Level Comp Moat, “Insane Demand”, Inference Explosion 1 B x, Memphis Supercluster, OpenAI, & more. Brad Gerstner Clark Tang Bill Gurley (00:00) Intro (1:50) The Evolution of AGI and Personal Assistants (06:03) NVIDIA's Competitive Moat (15:51 ) The Future of Inference and Training in AI (19:01) Building the AI Infrastructure (31:35) Inventing a New Market in an AI Future (38:40) The Impact of OpenAI (43:25) The Future of AI Models (46.44) and Memphis Supercluster (51:21) Distributed Computing and Inference Scaling (55:54) Inference Time Reasoning and Its Importance (01:00:46) AI's Role in Growing Business and Improving Productivity (01:08:00) Ensuring Safe AI Development (01:12:31) The Balance of Open Source and Closed Source AI
1:21:13

BG2. Ep 17. Double $NVDA! System Level Comp Moat, “Insane Demand”, Inference Explosion 1 B x, Memphis Supercluster, OpenAI, & more. Brad Gerstner Clark Tang Bill Gurley (00:00) Intro (1:50) The Evolution of AGI and Personal Assistants (06:03) NVIDIA's Competitive Moat (15:51 ) The Future of Inference and Training in AI (19:01) Building the AI Infrastructure (31:35) Inventing a New Market in an AI Future (38:40) The Impact of OpenAI (43:25) The Future of AI Models (46.44) and Memphis Supercluster (51:21) Distributed Computing and Inference Scaling (55:54) Inference Time Reasoning and Its Importance (01:00:46) AI's Role in Growing Business and Improving Productivity (01:08:00) Ensuring Safe AI Development (01:12:31) The Balance of Open Source and Closed Source AI

Bg2 Pod

487,379 Aufrufe • vor 1 Jahr