How Voltron Data's Theseus solves data movement challenge

The race to build a distributed GPU runtime is heating up. 🚀 As Mike Beaumont highlights in our recent blog post, the bottleneck at datacenter scale isn’t FLOPS, it’s data movement. That’s why we built Theseus with a data-movement first design, overlapping compute with I/O and prefetching exact byte ranges. In head-to-head, cost-matched cloud tests, Theseus outperforms Photon by up to 4X and completes 100TB TPC-H/DS with just two DGX A100 640 GB nodes. And it now runs on AMD via ROCm-DS/hipDF, bringing cross-platform performance into the conversation. Read more in our latest blog: https://lnkd.in/gRPGb-U7 #DistributedSystems #GPUs #DataEngineering #ApacheArrow #SQL #AI

Mike Beaumont drops big time knowledge on the race to build a distributed GPU runtime. It's well worth the read. Voltron Data.

Running LLMs efficiently starts with the right hardware. But choosing the right GPU goes beyond just “more power”. From VRAM size to tensor performance and virtualization support, these specs matter when scaling inference tasks, especially in production environments. Looking for a GPU cloud built with AI workloads in mind? Discover Deka GPU by Lintasarta Cloudeka: https://lnkd.in/gQXz4ET9 #EmpoweringYourFuture #LintasartaCloudeka #GPUCloud #GPUMerdeka #CloudSolutions #DigitalTransformation #CloudForEnterprise #3reakthrough7ourney #ServicePortalCloudeka

Identify and understand CPU, memory, and storage features before you deploy. Get started using Azure virtual machines. https://lnkd.in/gUGYrppA Build and run everything from simple web apps to AI supercomputing by matching each workload to the right Azure VM in minutes. Know exactly what you’re provisioning by decoding the naming format to see CPU type, memory, storage, and features before deployment. Use free tools like Azure Migrate to right-size and plan. #VMSetup #azure #microsoftazure #azuretutorial #azurevm #virtualmachine

Great post by Nishtha Jain and Dan Eawaz on Google Kubernetes Engine (GKE) and Lustre! GKE is a powerful platform for orchestrating scalable AI and high-performance computing (HPC) workloads. But as clusters grow and jobs become more data-intensive, storage I/O can become a bottleneck. Your powerful GPUs and TPUs can end up idle, while waiting for data, driving up costs and slowing down innovation. This is where Lustre comes in. Learn about best practices for taking advantage of high-performance storage on GKE https://lnkd.in/gfHKR7dF

We launched GKE Managed Lustre in July this year. Within a few months, we have seen customers use Lustre for several use cases - whether it is AI/ML training, inference workloads, developer workflows or data-intensive HPC applications. We picked up a few tips along the way! Learn how to make the most out of your storage and compute with these best practices for your GKE cluster. CC: Dan Eawaz Poonam Lamba Shri Borde Yuchen Tian Rishita Golla

Identify and understand CPU, memory, and storage features before you deploy. Get started using Azure virtual machines. https://lnkd.in/gNf69NWM Build and run everything from simple web apps to AI supercomputing by matching each workload to the right Azure VM in minutes. Know exactly what you’re provisioning by decoding the naming format to see CPU type, memory, storage, and features before deployment. Use free tools like Azure Migrate to right-size and plan. #VMSetup #azure #microsoftazure #azuretutorial #azurevm #virtualmachine

Every AI project hits the same wall: Inference is expensive GPUs are scarce Cloud vendors lock you in Enter StreamdataX Nodes — decentralized compute on demand, directly tied to your data pipelines. Core advantages: 1️⃣ On-demand inference — tap GPU/CPU when you need it 2️⃣ Privacy-first — run jobs near the data, without exposing files 3️⃣ Scalable & trustless — workloads validated on-chain With StreamdataX Nodes, inference moves from bottleneck ➝ breakthrough. Be early and get listed for presale. https://lnkd.in/eKrJ76mG

We found a flaw in how many GPU runtimes handle data: they ignore compression. Every byte you move across storage, network, or memory adds cost and latency and at scale, that drag compounds fast. In our latest blog, we dive into why compression matters for GPU runtimes: ➡️ Less data to move = faster queries ➡️ Smarter use of memory + bandwidth ➡️ Lower compute + cloud costs Theseus was designed with this in mind because raw FLOPS don’t win if your pipeline can’t keep up. From our team: Joost Hoozemans, William Malpica, and Mike Beaumont 👉 Read the full post: https://lnkd.in/gZJXHVP2 #GPUs #HighPerformanceComputing #DataInfrastructure #Compression

Vultr Launches AMD MI355X GPUs for Global Customers Vultr now delivers AMD Instinct MI355X GPUs on its cloud platform to power demanding AI training, inference, and high-performance computing workloads. Check out Ron Westfall's analysis via the link in the comments 👇