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✨ The #NVIDIARubin platform is engineered to master multi-step problem-solving and massive long-context reasoning workflows at scale. Built using extreme co-design, Rubin delivers more AI tokens per watt and lower cost per token compared to the previous architecture. ✔️ 50 PFLOPS of NVFP4 inference ✔️ 22 TB/s of HBM4... show more

NVIDIA AI Infrastructure

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10,471 views • 4 months ago •via X (Twitter)

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NVIDIA Vera Rubin is in full production and arrives just in time for the next frontier of AI. The #NVIDIARubin platform uses extreme co-design across six new chips to accelerate agentic AI, advanced reasoning and massive-scale MoE model inference. NVIDIA Vera Rubin NVL72: Six new chips, one AI supercomputer — see how we built it.

NVIDIA Vera Rubin is in full production and arrives just in time for the next frontier of AI. The #NVIDIARubin platform uses extreme co-design across six new chips to accelerate agentic AI, advanced reasoning and massive-scale MoE model inference. NVIDIA Vera Rubin NVL72: Six new chips, one AI supercomputer — see how we built it.

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“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️

“NVIDIA’s cost per token is the lowest in the world.” — Jensen Huang, Founder & CEO of NVIDIA Token generation cost is a direct result of architecture excellence and extreme co-design, not just compute cost. Lowest cost per token and highest performance per watt are definitive measures of AI economics and the key to unlocking maximum profitability and AI revenue. ➡️

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The NVIDIA Vera Rubin platform is a multi-rack pod-scale system, built to process agentic AI and is now in full production. Built through extreme co-design, Vera Rubin unifies five connected rack-scale systems — the NVIDIA Vera Rubin NVL72, Vera CPU rack, Groq 3 LPX, Vera BlueField-4 STX, and Spectrum-6 SPX Ethernet — all purpose-built for global-scale agentic AI infrastructure. Vera Rubin delivers intelligence at scale, with the performance, efficiency and security needed to power the next industrial revolution. Congratulations to Microsoft, Dell Technologies, and CoreWeave for each standing up their Vera Rubin NVL72 engineering racks. Learn more:

The NVIDIA Vera Rubin platform is a multi-rack pod-scale system, built to process agentic AI and is now in full production. Built through extreme co-design, Vera Rubin unifies five connected rack-scale systems — the NVIDIA Vera Rubin NVL72, Vera CPU rack, Groq 3 LPX, Vera BlueField-4 STX, and Spectrum-6 SPX Ethernet — all purpose-built for global-scale agentic AI infrastructure. Vera Rubin delivers intelligence at scale, with the performance, efficiency and security needed to power the next industrial revolution. Congratulations to Microsoft, Dell Technologies, and CoreWeave for each standing up their Vera Rubin NVL72 engineering racks. Learn more:

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"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth

"The inflection point for inference has arrived." — Jensen Huang, Founder & CEO of NVIDIA We’ve officially crossed a new milestone in the inference era — where widespread adoption of AI shifts from learning to doing. The breakthrough: extreme codesign across hardware and software driving down cost per token. Lower cost → more inference More inference → more users and applications More users and applications → exponential AI revenue growth

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81,778 views • 2 months ago

Nvidia just revealed Vera Rubin. Ships H2 2026. The numbers are wild: → 10x more performance per watt vs Blackwell → 10x cheaper inference token cost → 4x fewer GPUs to train the same MoE model Energy was the biggest bottleneck in AI. Nvidia just made it 10x cheaper.

Nvidia just revealed Vera Rubin. Ships H2 2026. The numbers are wild: → 10x more performance per watt vs Blackwell → 10x cheaper inference token cost → 4x fewer GPUs to train the same MoE model Energy was the biggest bottleneck in AI. Nvidia just made it 10x cheaper.

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284,837 views • 3 months ago

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85,035 views • 1 month ago

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 ➡️

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16,053 views • 4 months ago

The Nvidia NVlink Spine can locally process the entire bandwidth of data equivalent of the Internet for GPU use on AI processing.

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Brian Roemmele

377,174 views • 6 months ago

GPU tradeoff series: A100 is not much more powerful than 4090 🫠 GPU Perf and Price: - 4090: 330 fp16 TFLOPs, $1,749 - A100 (80GB): 312 fp16 TFLOPs, $20,000 > A100 is 11.4X more pricy Training speed for GPT-2(124M) with llm.c: - 4090: 153K tokens/s - A100 (80GB): 195K tokens/s > A100 is only 1.3X faster (both trained using a single card, A100 llm.c training is shown in the video, 4090 video is in the quoted tweet) Conclusion: 4090 has a much better cost vs performance ratio Why: As in the H100 vs. 4090 comparison, the biggest difference between A100 and 4090 is their GPU memory size/bandwidth and cross-GPU communication bandwidth, which does not matter too much if your model can fit into a single 4090. Specs: 4090: - GPU memory size: 24GB - memory bandwidth: 1 TB/s - communication bandwidth: 64 GB/s A100: - GPU memory size: 80GB - memory bandwidth: 2 TB/s - communication bandwidth: 900 GB/s Nvidia killed off NVLink (a high-speed communication link that connects GPUs) on 4090. (Jensen Huang smiling face) If multiple 4090s could be interconnected via NVLink, their performance would be closer to datacenter-grade A100 GPUs, even for training larger models. Additionally, 4090 isn't allowed in datacenters, that's how Nvidia makes 💰💰💰

GPU tradeoff series: A100 is not much more powerful than 4090 🫠 GPU Perf and Price: - 4090: 330 fp16 TFLOPs, $1,749 - A100 (80GB): 312 fp16 TFLOPs, $20,000 > A100 is 11.4X more pricy Training speed for GPT-2(124M) with llm.c: - 4090: 153K tokens/s - A100 (80GB): 195K tokens/s > A100 is only 1.3X faster (both trained using a single card, A100 llm.c training is shown in the video, 4090 video is in the quoted tweet) Conclusion: 4090 has a much better cost vs performance ratio Why: As in the H100 vs. 4090 comparison, the biggest difference between A100 and 4090 is their GPU memory size/bandwidth and cross-GPU communication bandwidth, which does not matter too much if your model can fit into a single 4090. Specs: 4090: - GPU memory size: 24GB - memory bandwidth: 1 TB/s - communication bandwidth: 64 GB/s A100: - GPU memory size: 80GB - memory bandwidth: 2 TB/s - communication bandwidth: 900 GB/s Nvidia killed off NVLink (a high-speed communication link that connects GPUs) on 4090. (Jensen Huang smiling face) If multiple 4090s could be interconnected via NVLink, their performance would be closer to datacenter-grade A100 GPUs, even for training larger models. Additionally, 4090 isn't allowed in datacenters, that's how Nvidia makes 💰💰💰

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234,951 views • 1 year ago

📣 CoreWeave and NVIDIA are accelerating the build out of AI factories—token generators that directly drive revenue. This includes adoption of and support for: ✅ NVIDIA Vera Rubin deployment, starting in 2H 2026 ✅ NVIDIA Vera CPU and BlueField-4 STX ✅ NVIDIA Dynamo for inference and NVIDIA Agent Toolkit and OpenShell for long-running agents ✅ NVIDIA DSX Air for digital twins of AI factories Learn more ➡️

📣 CoreWeave and NVIDIA are accelerating the build out of AI factories—token generators that directly drive revenue. This includes adoption of and support for: ✅ NVIDIA Vera Rubin deployment, starting in 2H 2026 ✅ NVIDIA Vera CPU and BlueField-4 STX ✅ NVIDIA Dynamo for inference and NVIDIA Agent Toolkit and OpenShell for long-running agents ✅ NVIDIA DSX Air for digital twins of AI factories Learn more ➡️

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10,213 views • 1 month ago

Vera Rubin is in full production. We just kicked off the next generation of AI infrastructure with the NVIDIA Rubin platform, bringing together six new chips to deliver one AI supercomputer built for AI at scale. Here are the top 5 things to know 🧵

Vera Rubin is in full production. We just kicked off the next generation of AI infrastructure with the NVIDIA Rubin platform, bringing together six new chips to deliver one AI supercomputer built for AI at scale. Here are the top 5 things to know 🧵

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815,863 views • 5 months ago

“This is the most advanced accelerator we've ever built!” - Dr. Lisa Su. Helios' "heart": The MI400 Series. It runs trillion-plus parameter models, delivers up to 40 PFLOPS of FP4 performance, features 432 GB of HBM4, and supports 300 GB/s of scale-out bandwidth to connect across racks and clusters. Now, this is how we power the future of AI. #AdvancingAI

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68,513 views • 10 months ago

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 about 10x over Blackwell, and Blackwell itself was about 10x over Hopper. i.e. overall Rubin is roughly 100x Hopper in factory throughput per watt, which matters hugely, because a 1 GW, $50B data center is power-limited and revenue scales with throughput per watt. --- From Nvidia YT channel

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 about 10x over Blackwell, and Blackwell itself was about 10x over Hopper. i.e. overall Rubin is roughly 100x Hopper in factory throughput per watt, which matters hugely, because a 1 GW, $50B data center is power-limited and revenue scales with throughput per watt. --- From Nvidia YT channel

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165,770 views • 5 months ago

NVIDIA HAS TAPED OUT 6 RUBIN CHIPS AT TSMC - New CPU (Vera - 88 ARM cores with SMT) - New GPU variants (R100 with HBM4) - Scale-up NVLink switch - Silicon photonics processor - Networking silicon - Complete chipset overhaul Reminder that Rubin is on-schedule for mass production in the 2nd half of 2026

NVIDIA HAS TAPED OUT 6 RUBIN CHIPS AT TSMC - New CPU (Vera - 88 ARM cores with SMT) - New GPU variants (R100 with HBM4) - Scale-up NVLink switch - Silicon photonics processor - Networking silicon - Complete chipset overhaul Reminder that Rubin is on-schedule for mass production in the 2nd half of 2026

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78,410 views • 9 months ago

A new era for video has arrived. 🎬 Congratulations to Runway for showcasing the first demonstration of video generation on the NVIDIA Rubin platform. See what happens when Runway Gen-4.5 meets our next-gen platform in this exclusive early look. Learn More: #NVIDIARubin

A new era for video has arrived. 🎬 Congratulations to Runway for showcasing the first demonstration of video generation on the NVIDIA Rubin platform. See what happens when Runway Gen-4.5 meets our next-gen platform in this exclusive early look. Learn More: #NVIDIARubin

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911,926 views • 5 months ago

🔥 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. ⚡

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13,359 views • 3 months ago

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

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80,342 views • 1 year ago

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

76,384 views • 5 months ago

💡Industries are scaling AI like never before. To help organizations extract maximum value, NVIDIA systems are built to deliver unmatched performance at AI factory scale. ⚙️ The latest InferenceMAX v1 benchmarks confirm: 💥 10× more performance per watt, translating to lower costs and greater energy efficiency 📈 15x more ROI across manufacturing, energy, healthcare, and beyond. Learn more:

💡Industries are scaling AI like never before. To help organizations extract maximum value, NVIDIA systems are built to deliver unmatched performance at AI factory scale. ⚙️ The latest InferenceMAX v1 benchmarks confirm: 💥 10× more performance per watt, translating to lower costs and greater energy efficiency 📈 15x more ROI across manufacturing, energy, healthcare, and beyond. Learn more:

NVIDIA AI

114,261 views • 8 months ago

Introducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks. Learn more:

Introducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks. Learn more:

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225,379 views • 14 days ago