You can put a data center at your house—but who really pays?
The idea of asking homeowners to host boxes full of GPUs is a symptom of the woeful dearth of data center space needed for AI computing.
Towards Data Science·
In an age of constrained compute, learn how to optimize GPU efficiency through understanding architecture, bottlenecks, and fixes ranging from simple PyTorch commands to custom kernels. The post A Guide to Understanding GPUs and Maximizing GPU Utilization appeared first on Towards Data Science.
Read full articleThe idea of asking homeowners to host boxes full of GPUs is a symptom of the woeful dearth of data center space needed for AI computing.
Writing code has always been the most time- and resource-intensive task in software development. AI is changing that, and faster than most engineering organizations are prepared for. Tools like Claude Code and Cursor are already handling significant parts of code construction, freeing developers to spend more time on requirements, architecture, and design. But that shift creates a new challenge nobody is talking about enough. As AI takes on the heavy lifting, the skills that matter most are moving upstream: how to provide the right context for a prompt, how to evaluate what the model produces, and how to understand a problem deeply enough that you can’t be fooled by a confident but wrong answer. This piece explores those three skills and why developers who master them will have a significant edge over those who don’t. Beyond coding: Mastering the art of the prompt Software translation tools such as compilers and assemblers map a high-level description of code to a lower-level represent
Silicon photonics is emerging as a way move massive amounts of data among GPUs and CPUs in HPC systems, but what if you could compute purely with light and photonics? […] The post Lumai’s Photonic Chip Harnesses Light for Big AI Compute Speedup appeared first on AIwire.
A step-by-step road map for building the enterprise architecture required to deploy AI safely, quickly, and at scale.
A review of the Cross-Stage Partial Network paper — and a from-scratch PyTorch implementation The post CSPNet Paper Walkthrough: Just Better, No Tradeoffs appeared first on Towards Data Science.
NaNs don’t crash your training — they quietly destroy it. The post PyTorch NaNs Are Silent Killers — So I Built a 3ms Hook to Catch Them at the Exact Layer appeared first on Towards Data Science.
This Spring Astronomy Day, here’s a look at how AI and GPUs are helping astronomers work through unprecedented volumes of cosmic data.
In the race to incorporate AI into many industries, GPUs have become one of the most sought-after resources in enterprise computing. They are expensive, hard to find, and increasingly seen […] The post Companies Are Racing to Buy GPUs. Many Sit Idle appeared first on AIwire.