HEAVY.AI

We're proud to announce today a new partnership with Ookla, a leader in network intelligence. With Ookla's wide array of unique crowdsourced and drive test data, combined with the GPU-accelerated HEAVY.AI analytics platform, organizations can optimize, plan, and pinpoint issues in their networks faster than ever before. Read the press release to learn more: https://lnkd.in/emU6G-c8

Leveraging the supercomputing power of HEAVY.AI on the new NVIDIA Grace Hopper Superchip, running on Vultr Cloud, you can now interactively explore over 20 billion records of ship AIS data in our new demo. Check out the blog from Mike Waldrop to learn more, and then play with the data for yourself to see what you can find! https://lnkd.in/e-9iWVbA

HEAVY.AI processes billions of records in real-time with Vultr Cloud GPUs, including NVIDIA GH200 Grace Hopper™ Superchip instances, delivering faster analytics at lower cost. 🏊 📝 Dive into this manufacturing & energy use case to explore: ✅ Optimizing logistics and supply chains with real-time data ✅ Improving pipeline management and production planning ✅ Analyzing telematics data for predictive maintenance 💡 “With the NVIDIA GH200 Grace Hopper™ Superchip, we can achieve better performance than A100 machines for 30-40% less cost.” – Todd Mostak, CTO, HEAVY.AI #AI #DataAnalytics #GPU #Cloud #Manufacturing #Energy Read the case study: https://lnkd.in/ggerrU6N

HEAVY.AI announced the availability of its analytics platform with the NVIDIA Grace Hopper Superchip... The launch is part of the broader 8.2 release of the HEAVY.AI accelerated analytics platform, and means that users will be able to process faster with more cost savings and more availability to scale to larger datasets. See more: https://carah.io/HEAVYAI Todd Mostak, Robert Deppisch, Shane Shaneman, Anthony Robbins, Jimmie Adkins, Craig Weinstein, Geoff Fancher, Carrie Almeida, Michael Adams

We are excited to announce the availability of the HEAVY.AI GPU-accelerated analytics platform on the Nvidia Grace Hopper GH200 Superchip. By leveraging the ultra-fast NVLink interconnect from GPU-CPU, customers can benefit from significantly better performance while spending less on hardware. “The NVIDIA Grace Hopper Superchip changes the game in terms of being able to provide best-in-class performance over our customers’ largest datasets,” said Todd Mostak, Co-Founder and CEO of HEAVY.AI. “Now customers no longer have to choose between faster performance and lower cost; with HEAVY.AI running on NVIDIA Grace systems, they can have both.” Read the full release here: https://lnkd.in/gvVhtCCd

I've been diving deeper into the Foursquare Places dataset that was released two weeks ago, and am finding it great not only for accessing individual POIs, but also for performing large-scale analysis of geographic trends. Here I'm using HEAVY.AI to join the Places dataset to the NASA Gridded Population of the World (GPW) dataset, containing population estimates per square km for the entire world, using Uber H3 hexagonal spatial indexes as join keys. This allows for easy calculation and visualization of the number of POIs by category per capita. See below for two visualizations comparing the number of places of worship with the number of bars per 1,000 people. You can easily see the Bible belt light up for the former category, but why the high prevalence of both places of worship and drinking establishments in the Upper Midwest?

It truly was an awesome surprise this morning to see that Foursquare had open sourced over 100M Points-of-Interest (POIs) from its Places dataset. Having such a dataset under a permissive Apache license is going to be a huge boon to developers and open geo enthusiasts alike, as currently there is little high-quality open POI data available. Curious, I pulled the Parquet files from S3 and loaded them into HEAVY.AI, which took about 10 minutes, after which I spent another 10 minutes or so building a dashboard in Heavy Immerse to start interactively exploring the data. As you can see in the quick screen recording here, the dataset is quite stunning. I did a quick sanity check of the number of global McDonald's (roughly 40K), and then even drilled down to the streets of SF to find the locations of my favorite food truck (Señor Sisig !). Major kudos to the Foursquare Places team for their significant contribution to the open geospatial community, and we look forward at HEAVY.AI to exploring this data more and using it to enrich other spatial datasets.

Check out our new 20 billion record AIS ship location demo, running on a single-GPU NVIDIA Grace Hopper GH200 instance on Vultr Cloud!

If you are attending #SC24 in Atlanta, we'd love to show you the combined power of HEAVY.AI and NVIDIA Grace Hopper running on Vultr. Swing by to see how you can visualize your largest geospatial datasets.

I'm excited to present tomorrow (10/31) at 10AM PT/1PM ET with NVIDIA and Vultr on the huge performance gains we're seeing at HEAVY.AI running on the new Nvidia Grace Hopper Superchip. With an ultra-fast NVLink interconnect between CPU and GPU, we no longer have to move data over the "thin straw" of PCIe, dramatically accelerating large-scale data analytics workloads. Join us at the webinar to learn more! https://lnkd.in/egzdU2pG