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📣 Get a first look at the NVIDIA Photonics co-packaged optics switch with Lambda. At NVIDIA GB300 NVL72 scale, the network doesn't just move data between GPUs — it determines how fast your cluster thinks. Co-packaged optics cut switch power, reduce failure points, and deliver more tokens per watt.... show more

NVIDIA AI Infrastructure

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83,431 次观看 • 12 天前 •via X (Twitter)

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First look at $NVDA photonics co-packaged optics switch with Lambda. As GB300 NVL72 scales, networking becomes the layer that determines how fast the cluster thinks while co-packaged optics cut power and improve tokens per watt.

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Nvidia CEO Jensen Huang talks about NVIDIA Co-Packaged Optics at GTC 2026

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The first-ever hydrogen-powered NVIDIA GB300 NVL72 systems are online. Training & inference at scale — with zero emissions, zero water. Lambda × Supermicro × ECL →

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17,276 次观看 • 9 个月前

Nvidia just spent $4 billion on a technology 99% of people have never heard of. But in 3 years, every AI data center on Earth will need it. And Nvidia just LOCKED UP the supply. Here's what happened: Nvidia invested $2 billion in Coherent and $2 billion in Lumentum. You probably never heard of these companies. They make photonics technology. Systems that transmit data using LIGHT instead of electricity. Sounds like sci-fi. But this is the most important infrastructure bet in AI right now. Here's the problem Nvidia just solved for itself: AI data centers are hitting a wall that has nothing to do with chips, energy, or money... Copper wiring is dying. Every data center on Earth moves data between GPUs using copper cables. But at the speeds AI now demands, copper physically cannot keep up. Signal degrades. Heat explodes. Power consumption skyrockets. Right now, 30% of the electricity in an AI data center is wasted just MOVING data from point A to point B. An MIT researcher said: "Copper's not going to cut it. It gets too hot. Too much power consumption and loss." Jensen Huang admitted it himself too: "We use copper as far as we can, about a meter or two. But where data centers are the size of a stadium, we need something else." That something else is photonics. Replacing copper with laser-powered fiber optics built directly into the chip. The numbers are insane: - 3.5x more power efficient - 10x better network reliability - Data moving at 102 terabits per second Wells Fargo estimates the photonics market will hit $10-12 billion by 2030. And Nvidia just bought privileged access to the two companies that make the advanced lasers every single one of these systems will need. This is the Nvidia playbook on repeat. They did this with CoreWeave. Invested $2 billion, locked up GPU capacity, created a dependent customer. They did this with memory suppliers. Secured HBM allocations years in advance while competitors scrambled. Now they're doing it with photonics. Invest early. Lock up supply. Make the entire ecosystem dependent on companies that are dependent on Nvidia. By the time competitors realize photonics is the bottleneck, Nvidia already OWNS the supply chain. Every data center, AI factory, and GPU cluster will need this technology to function at scale. Nvidia will become even more important.

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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 bandwidth ✔️ 3.6 TB/s of NVIDIA NVLink bandwidth per GPU Learn more about NVIDIA Rubin ➡️

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10,471 次观看 • 4 个月前

Jensen Huang shows off the NVIDIA GB200 NVL72: a data center superchip with 72 Blackwell GPUs, 1.4 exaFLOPS of compute and 130 trillion transistors

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56,400 次观看 • 1 个月前

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

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AI has turned data centers from 5–10 kW racks into power-hungry AI factories. At that scale, the question isn’t “who has the most FLOPs?” It’s: Who can deliver the most intelligence per watt and per dollar? Nvidia’s GPUs still dominate, but inside Google there’s a different answer: the Tensor Processing Unit (TPU):

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winkle.

8,360,024 次观看 • 25 天前

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 次观看 • 9 个月前

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NVIDIA $NVDA CEO, Jensen Huang just unveiled the N1X chip at Asia's largest tech expo Co-developed with MediaTek, this beast is built on the Blackwell architecture and runs the entire NVIDIA software stack flawlessly. "A chip that would take 33 years to build." Looks like future of AI hardware has officially arrived.

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22,857 次观看 • 21 天前