GPUs Need Reliable Energy: HIVE Digital Technologies

Rashid Attar breaking down the HBM technology advancements Qualcomm is innovating, different from our competition, in a way everyone can understand!! Less than 90sec, love it!

The way Qualcomm Engineers think and develop is what attracted me to this company. So thoughtful, innovative and forward thinking across all our technologies! We're dramatically improving Compute, Data Centers, wireless, automotive and so much more. #thefutureofcompute

The next era of AI infrastructure won't be defined solely by faster processors, but by how efficiently data reaches compute. Qualcomm High Bandwidth Compute (HBC) is designed to address that challenge. Read more from Qualcomm's Rashid Attar to learn why the future of AI may be won not just through raw speed, but through smarter data movement: https://bit.ly/44py3Z5

AI impact becomes measurable only when infrastructure is designed for scale, governance and performance. This infographic connects the layers that matter: GPU computing, storage, networking, access control and business metrics. Check out our latest infographic to understand how AI infrastructure creates real impact across Indian enterprises: https://lnkd.in/gS9VCQss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . #NetwebTyrone #AIInfrastructure #FutureOfTech #InnovationLeadership #AIScaling #DigitalTransformation

AI Data Centres: Power and Cooling Become the New Bottleneck. The AI infrastructure race is no longer just about chips — it's about whether facilities can be powered and cooled fast enough to keep up. At Data Centre World 2026, engineering leaders from Oracle, Nvidia, and Google described a shift from general-purpose IT environments to tightly integrated compute systems, with changes showing up across every layer of infrastructure — from power and cooling architectures to network design and construction timelines. Read the full article here: https://lnkd.in/dKJreewu

High Bandwidth Flash: A New Memory For AI Data Centers And Edge Computing Artificial intelligence is on a relentless march across the computing landscape. While about one in seven data centers today is equipped to host AI workloads, that’s expected to approach 70 percent by 20301. AI is migrating from hyperscale to enterprise data centers and out to the network perimeter, where edge AI applications are projected to generate nearly $66.5 billion by the end of the decade2. The fuel for the new computing era is data — staggeringly large volumes that must be fed at high speed to demanding and rapidly scaling AI computing infrastructure. By: Alper Ilkbahar, Sandisk CTO https://lnkd.in/dqB_crbR

Grrr, data centers make you angry, takes land, water, electricity and more. Takes your health, wealth and quality of living. Ulshe AI we don't need 10s of 100s of data centers. Our #AI is made to be built and run on devices such as a cellphone. Larger sure, no problem, consumer grade hardware. What's that? You own an #Nvidia server or #AMD server rack?!?! Well, my friend, you can make a whole town worth of AI models, tens of thousands of them, built and run right from that server rack. That's right, while other companies want money just to burn it on compute. We get to spend our money on real things and asset level compute ✌️🤓😉

Scaling AI infra is becoming a three-level problem now: 1. Scale Up: The Silicon Level This means making a single processing unit as powerful as possible by upgrading to the latest flagship GPU. You squeeze more compute into the same physical footprint. But you eventually hit the wall of physics. There is a hard limit to how much heat you can dissipate before the silicon melts. 2. Scale Out: The Rack and Cluster Level This involves adding more GPUs and tying them together. You pack dense server racks and use high-speed switches to make thousands of separate GPUs act like one massive brain. But the bottleneck shifts to network latency. Getting all those chips to communicate without dropping data is incredibly difficult. 3. Scale Across: The Data Center Level This means spreading the system across different geographic locations. Once a single facility maxes out the local power grid, you build a second data center elsewhere and distribute workloads across both. But now you're now fighting the speed of light, making data synchronization an orchestration nightmare.

M5 Max 128GB Performance Benchmarks for Local AI Workloads 📌 New benchmarks reveal the massive potential of the M5 Max with 128GB of unified memory for running heavy LLMs locally. By testing architectures like Gemma 4 and DeepSeek, this data highlights a powerful shift toward decentralized AI development. These findings suggest that high-memory edge hardware could become a vital, energy-efficient alternative to massive, power-hungry data centers. 🔗 Read more: https://lnkd.in/dug-DfH2 #M5max #Localllm #Unifiedmemory #Largelanguagemodels

For over 14 years, Raspberry Pi has made low-cost computing available to experts and aspiring developers alike. Now, with Ultralytics YOLO and #OpenVINO, you can run AI vision workloads directly on Raspberry Pi devices. Together we’re helping developers run AI where the data is generated and build faster, more reliable computer vision applications. Learn more in our blog post: http://ms.spr.ly/6043v4tFW