Hardware
We track 18 posts about Hardware from 9 engineering blogs. Most active: Nvidia, Jessie Frazelle, Jeff Atwood. Latest post: Oct 1, 2026.
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Recent posts
How NVIDIA GPUs Help Accelerate OpenAI’s GPT-6 Astra Ultrafast (opens on the source site)
Nvidia ·
GPT-6 Astra Ultrafast, running on NVIDIA Blackwell GPUs, is available now in the OpenAI API and to eligible ChatGPT Work and Codex users. Accelerated by inference optimizations through OpenAI’s models that tap into the capabilities of the NVIDIA Blackwell architecture, Ultrafast offers up to 8x faster token generation than the Astra Standard mode. For developers, […]
Productive, Durable, Fungible: How NVIDIA AI Factories Maximize Return on Investment (opens on the source site)
Nvidia ·
AI factories are built by the megawatt, even by the gigawatt. Each megawatt factory costs roughly $60 million, and AI factory operators will only commit capital on that scale with a clear view of the return on investment. Three key things shape AI factory returns: Earning capacity: What the factory could earn in a year […]
Your phone is AI’s newest hardware (opens on the source site)
Ryan sits down with Div Garg, CEO at AGI Inc., to talk about running AI agents entirely on mobile devices, optimizing models for edge computing chips, and building safety mechanisms into autonomous app interactions.
Old Capacitors, New Problems: Booting an Apple IIe for the First Time in 30+ Years (opens on the source site)
While helping clean out the garage of my grandfather’s friend, I found something that caused me to nearly drop what I was carrying. I found an Apple IIe, Monitor II, and an Apple ImageWriter, all stored in their original packaging! Apple released the Apple IIe in January of 1983 as the “enhanced” version of the […] The post Old Capacitors, New Problems: Booting an Apple IIe for the First Time in 30+ Years appeared first on Atomic Spin.
Sakeena Fiza Helps NVIDIA Hardware Succeed at Scale (opens on the source site)
Nvidia ·
When Sakeena Fiza describes her work as a validation engineer at NVIDIA, she does so in terms more befitting a detective story than a world-class engineering lab. “Validation engineers look in the shadows and shine a light into every corner,” Fiza said. “Every time we get a system, our first thought is: how can it […]
How I massively improved my AI inference performance without buying new hardware (opens on the source site)
Red Hat ·
Let me paint you a picture. You have 16 NVIDIA H200 GPUs spread across 2 nodes. That is, conservatively, several 100,000 dollars of silicon sitting in a data center, connected by RDMA/InfiniBand, running Kubernetes, and serving a large language model. You're living the agentic dream (not really, but it's a great start). Except your time to 1st token (TTFT) is thousands of milliseconds. The post How I massively improved my AI inference performance without buying new hardware appeared first on Red Hat Developer.
Why Deploying Physical AI at Scale Demands Safety at Every Layer (opens on the source site)
Nvidia ·
Physical AI is moving rapidly from research to large-scale deployment. By 2035, ABI Research projects an installed base of 49 million level 3-5 autonomous vehicles (AVs), while Omdia estimates that roughly 60 million industrial robots will be deployed between 2026 and 2035. As these machines enter roads, factories, warehouses and other environments shared with people, […]
NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut (opens on the source site)
Nvidia ·
System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments. […]
Home Hardware, Part 2: Width Matters (opens on the source site)
We’re back! Not with rework. This time we’re talking about impedance and review. Apparently I can get into a heated argument about a board before it even gets here. In part one, I wrote about using Codex to design my own hardware because I’m sick of Control4, Savant, and the rest of that shit. The useful part wasn’t one-shotting a PCB. It was having the agent work through datasheets, compare parts, and check the design. Then I had to notice that the connectors were facing the wrong fucking way. There was more to review. Which board are we arguing about? I was talking through the Sensor Hub’s…
Home Hardware, Part 1: My Control4 Revenge Arc (opens on the source site)
Fuck1 it, we’re doing consumer hardware for ourselves now and getting fucked by economies of scale, I guess. This all started with me buying some Olimex Bluetooth proxies for Home Assistant. Then I started looking at the components and wondering if I could get better range. Now I’m designing boards, displays, and enclosures, and emailing manufacturers because their bezels are enormous. I’ve ordered the first two boards: a Bluetooth sensor hub and a little maintenance display. I’ve been working on this in Codex, and this post is going to go through what it was good at, where it got stuck or…
NVIDIA NVLink Fusion Expands With NVHBM Custom High-Bandwidth Memory (opens on the source site)
Nvidia ·
The next wave of AI is placing new demands on infrastructure. As AI agents and trillion-parameter workloads become mainstream, the performance of AI infrastructure depends not only on compute, but on how compute, memory, storage, networking and software are designed together as a unified system. To help hyperscalers and AI innovators build the next generation […]
How XPUs Meet a World-Class AI Factory (opens on the source site)
Nvidia ·
To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators. Hyperscalers and AI-native companies building custom XPUs must consider […]
Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents (opens on the source site)
Nvidia ·
According to OpenRouter data, agentic AI workloads consume 15x more tokens than a simple chat request. Why? Consider what happens when an AI agent researches a company for an investment decision. The agent queries financial databases, searches news and filings, invokes a sub-agent to run peer comparisons and model valuations, then synthesizes everything into a […]
Why Scaling AI Compute Performance Requires a New Power Architecture (opens on the source site)
Nvidia ·
Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just wattage. It’s how power gets from the grid to the GPU. In traditional power delivery, electricity travels from the grid as an alternating current (AC) […]
Announcing Web Serial Support in Firefox (opens on the source site)
Support for Web Serial in Firefox 151 for Desktop Firefox can now connect directly to microcontrollers, development boards, 3D printers, power meters, and other serial-connected hardware from the web. Starting in Firefox 151 for Desktop, support for the Web Serial API allows web applications to communicate with compatible devices without requiring native software. Web Serial […] The post Announcing Web Serial Support in Firefox appeared first on Mozilla Hacks - the Web developer blog.
What CI/CD strategies work for embedded or IoT projects that require hardware testing? (opens on the source site)
Embedded and IoT teams face a very different CI/CD reality than traditional SaaS teams. While most continuous integration and continuous delivery pipelines assume everything can run in the cloud, embedded systems depend on physical hardware, constrained environments, and real world signals. If you search forums like Reddit (r/embedded, r/devops), Stack Overflow, or vendor communities, the […] The post What CI/CD strategies work for embedded or IoT projects that require hardware testing? appeared first on Semaphore.
It's Never a Hardware Bug, Until it is. (opens on the source site)
HubSpot ·
Edit, April 30 2026: The OpenJDK now contains a mitigation for the hardware bug described in this article, which will appear in OpenJDK 27. Thank you to Evgeny Astigeevich at AWS for his work on the mitigation. Most software developers have heard the maxim, “it’s never a compiler bug,” and its companion, “it’s never a hardware bug.” But if you investigate enough bugs, you’ll eventually find an exception to this rule, and that’s what we found recently at HubSpot. I discovered how a hardware bug was affecting our users’ experience.
Building a PC, Part IX: Downsizing (opens on the source site)
Hard to believe that I’ve had the same PC case since 2011, and my last serious upgrade was in 2015. I guess that’s yet another sign that the PC is over, because PC upgrades have gotten really boring. It took 5 years for me to muster
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This page is generated automatically from the engineering blogs we follow. Every post links to its source, where it was published. See all sources.