Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

NVIDIA cuPhoton is now available on GitHub 🔭 Working with data from telescopes, X-ray sources and laser experiments often means spending months or years just getting the data ready to use. cuPhoton moves that pipeline onto the GPU to speed up that process, from ingest and cleaning to analysis,...

254,976 Aufrufe • vor 1 Monat •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

62,015 Aufrufe • vor 10 Monaten

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.

Ricardo

641,147 Aufrufe • vor 6 Monaten