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In the fast-paced world of technology, it's easy to overlook the backbone of high-performance tasks—servers equipped with NVIDIA GPUs. I recently came across an insightful article that breaks down five compelling reasons to consider a dedicated NVIDIA GPU server. Reflecting on my own experiences, I remember a time when our project was held back by inadequate computing power. We opted for a shared solution, but it turned out to be a bottleneck we hadn’t anticipated. Once we switched to a dedicated server, we unlocked levels of efficiency and performance we never thought possible. That switch taught us the importance of investing in the right technology. The key takeaway? Investing in robust infrastructure isn’t just about keeping up; it’s about paving the way for innovation and success. It has the potential to streamline processes and elevate project outcomes dramatically. What has been your experience with dedicated servers versus shared hosting? Let’s discuss! https://lnkd.in/ertbj_qi

If you thought Nvidia's 120 kW NVL72 racks were compute dense with 72 Blackwell accelerators, they have nothing on HPE Cray's latest EX systems, which will pack more than three times as many GPUs into a single cabinet. https://lnkd.in/ePnFf9vm

HPE crams 224 Nvidia Blackwell GPUs into latest Cray EX “If you thought Nvidia's 120 kW NVL72 racks were compute dense with 72 Blackwell accelerators, they have nothing on HPE Cray's latest EX systems, which will pack more than three times as many GPUs into a single cabinet. Announced ahead of next week's Super Computing conference in Atlanta, Cray's EX154n platform will support up to 224 Nvidia Blackwell GPUs and 8,064 Grace CPU cores per cabinet. That works out to just over 10 petaFLOPS at FP64 for HPC applications or over 4.4 exaFLOPS of FP4 for sparse AI and machine learning workloads, where precision usually isn't as big a deal.” https://lnkd.in/eE_q56NW

#GPUs need a home for your #AI and #HPC workloads to run #energyefficient #scalable #beautiful and that's how we arrived at Lenovo ThinkSystem SR680a V3 SR685a V3 and SR780a V3. All featuring 8 GPUs ... and the "a" stands for #accelerated 🚀 and the SR780a is #watercooled 💦 - in case this wasn't obvious? ☝ 😎 How can your AI and HPC workloads take #advantage? Dive into the details at our Lenovo Press article to learn more https://lnkd.in/ectAA6V8 #WeAreLenovo

The MECAI-GH200 measures just 450 x 445 x 87mm, so it’s small enough to fit under a desk, but it’s no slouch when it comes to performance. @Nvidia’s GH200 Grace Hopper Superchip combines the #NVIDIA Grace CPU and Hopper AI GPU architectures with an NVLink interconnect between them, and it’s super-fast, as recent tests show https://lnkd.in/gFxqQAZB

🚀 Exciting innovation alert! NVIDIA’s BlueField-3 DPU now comes in a “self-hosted” version, offering a powerful solution for storage and networking. With a 16-core Arm A78 CPU and impressive memory bandwidth, it’s designed to host applications directly, simplifying architectures and maximizing efficiency. This breakthrough bridges the gap between compute and data, paving the way for next-gen systems. #NVIDIA #BlueField3 #DPU #DataCenters #Innovation #EdgeComputing

Quick Take: NVIDIA's next-gen GB300 AI servers, launching at GTC 2025, will primarily be manufactured by Foxconn. Foxconn's deep vertical integration, encompassing chip modules, assembly, liquid cooling, and connectors, positions them as a strategic partner. Their involvement with NVIDIA dates back pre-2017, collaborating on AI server development from the first generation through the current GB200 and upcoming GB300. Foxconn already supplies 80-90% of GB200 components (excluding GPU and CPU). While Foxconn leads production, Quanta and Inventec also play key roles in the GB300 ecosystem. Quanta maintains its position as the second largest supplier after Foxconn. Inventec, too, has increased its order share compared to the GB200 generation. #NVIDIA #AI #Servers #GB300 #Foxconn #Quanta #Inventec #GTC2025

Nvidia is launching its next-generation AI products under the Blackwell architecture. These include GPUs (B100 series), Superchip platforms (GB200 series), and servers (NVL series). Analysts expect these to be significantly more expensive than the previous generation (Hopper). Individual B100 GPUs are estimated to cost around $30,000-$35,000, while the more powerful GB200 Superchips containing multiple GPUs and CPUs could reach $70,000. Server racks containing these parts are expected to cost millions, with the top-of-the-line NVL72 reaching a staggering $3 million. Despite the high costs, these products are generating a lot of interest from major tech companies due to their superior performance. This could lead to significant revenue growth for Nvidia and potentially propel them to the top of the most valuable company list. #nvidia #techgiants #b100 #blackwell #gpus #ai #gloudservers #datacenters #artificialintelligence #technology #innovation #chips #chipmaker #semiconductors #semiconductorindustry #semiconductormanufacturing

In one respect the ability rack and stack thousands of GPUs and get them networked is an impressive feat. But we've been doing that for decades. Part of my job 20 years ago was to do just that - build out large scale infrastructure. It isn't that hard. What these and many other articles fail to cover are the software and data sides of the discussion. What applications are in use? How much work is this cluster doing? What benefit will there be in expanding to 200,000 or 300,000 GPUs? This part of a supercomputer, to me, is much more interesting. How efficient is the system? How are failures managed? How much data is moving around and being generated by the infrastructure? Without some concrete value out of this system, the whole story is a marketing discussion, pure and simple (albeit an expensive one). https://lnkd.in/eswgAUYM