标签：#Hardware

What does an AI company do after one of those not-acqui-hire deals? Groq raised money, is leaning into its neocloud business, and is hiring new execs.

Mach's approach differs sharply from some of its peers.

Siri’s AI overhaul may have grabbed the headlines at WWDC, but some of Apple’s most useful AI features are arriving elsewhere in iOS 27.

I run a homelab with four RTX 3090s — 96 GB of VRAM, 44 CPU cores. For two weeks I tried to make it my daily driver for local LLM inference instead of paying for cloud APIs. I got it working. Then I looked at the numbers and subscribed to a paid API anyway. Here's the uncomfortable part, and the optimizations that still made it worth doing. ## The setup 4× RTX 3090 (Ampere — no native BF16), 96 GB VRAM total, 44 cores Models: Qwen3.6-35B-A3B (Q8_0, MoE) and Qwen3-Coder-Next (Q6_K, hybrid) llama.cpp in router mode + OpenWebUI Ceiling I hit: ~105 tokens/second ## The 6% problem The wall wasn't compute. GPU utilization sat at 6%. The bottleneck was CPU orchestration — llama.cpp dispatches across multiple GPUs sequentially, so the cards spent 94% of the time idle waiting on each other. Throwing more VRAM at it does nothing for this. ## What actually moved the needle | Change | Effect | |---|---| | --ubatch-size 512 | +40% throughput | | KV cache quantization (Q4_0) | 4× VRAM savings | | Speculative decoding (n-gram) | 2.5× speedup on repetitive tasks | | YaRN rope scaling | context extended to 1M tokens | Two things surprised me: MoE models tolerate aggressive quantization far better than dense ones — inactive experts don't eat bandwidth, so the quant hit lands softer. The 3B active -parameter model was great at local decisions but fell apart on coherence past ~300–400 lines of code — fine for a function, not for cross-file consistency. ## The conclusion I didn't want At ~11 kWh/day, plus hardware depreciation, against current API pricing, the math doesn't favor local for interactive work. The single biggest improvement to my daily AI workflow was paying for an API. Local still wins for privacy, high-volume batch jobs, or uncensored experimentation — but not as a general cloud replacement. It's an economics problem, not a capability one. I wrote up the full cost breakdown and the exact llama.cpp router configs on aipster.com . If you're weighing a local rig, I also benc

While it's not flashy like Apple’s new Siri AI and Apple Intelligence upgrades, there are still a number of additions to iOS 27 worth looking at.

Every developer who has ever muttered "there is a bug in this" is repeating a word with a surprisingly literal origin. On September 9, 1947, the operators of the Harvard Mark II, an early electromechanical computer, traced a malfunction to its source and found something they did not expect: a moth wedged inside Relay #70. They removed the insect, taped it into the operations logbook, and wrote a now-famous line beside it: "First actual case of bug being found." That page, moth and all, survives today in the collection of the Smithsonian's National Museum of American History. It is one of the best-loved stories in computing, and like most good stories it is a little more complicated than the popular version. Worth getting right, because the discipline it gave us is the same one behind every connected device we build. What actually happened in 1947 The Mark II was a room-sized machine built from relays, switches, and thousands of moving parts. When a moth flew into one of those relays, it physically interfered with the contacts and caused a fault. The technicians who found it had a sense of humor: calling it the "first actual case of bug being found" was a joke precisely because engineers had already been using "bug" for years to describe mysterious faults in machinery. Thomas Edison used the term in his notebooks back in the 1870s. So the 1947 moth did not invent the word "bug." What it did was give the term a perfect, photographable origin story, and it cemented the companion word that really matters: debugging. The act of removing that moth was, quite literally, de-bugging the computer. The Grace Hopper connection The story is almost always told with Grace Hopper at its center, and that deserves a small correction. Hopper, a pioneering computer scientist who later helped develop COBOL, was part of the Mark II team in 1947, but the evidence suggests she did not personally find the moth or write the logbook entry. What she did do was tell the story, brilliantly and o

What’s the most cliche possible gift you can give a relative? A digital photo frame, displaying a rotating slideshow of family photos. Now Aura has completely refreshed this product space with its gorgeous Aura Ink frame, which uses e-ink to create a display that doesn’t even look digital. Digital frames have always been so popular […]

Every connected device on your desk, from a smart plug to a fitness band to a hobbyist ESP32 board, runs on a descendant of one tiny chip that was never meant to change the world. In 1971, Intel released the 4004, the first commercially available microprocessor. It was not built for computers, robots, or the internet. It was built to run a desk calculator. The story of how a calculator chip became the foundation of modern IoT is one of the most instructive in all of electronics. A calculator contract that got out of hand The 4004 began as a job for hire. A Japanese calculator company called Busicom approached Intel in 1969 wanting a set of custom chips for a new line of printing calculators. The original plan called for around a dozen separate, purpose-built integrated circuits, each wired to do one fixed task. It was the standard approach of the era: if you wanted a device to do something, you designed silicon that did exactly that and nothing else. Intel engineer Ted Hoff looked at the sprawling design and proposed something radical. Instead of a pile of single-purpose chips, why not build one general-purpose processor that could be told what to do through software? A program stored in memory could make the same chip behave like a calculator today and something else entirely tomorrow. Stanley Mazor helped shape the architecture, and a newly arrived engineer named Federico Faggin turned the concept into a working device, inventing the silicon-gate design techniques that made it physically possible. Masatoshi Shima, Busicom's representative, worked alongside them on the logic. 2,300 transistors that started everything When the 4004 was announced on November 15, 1971, it packed about 2,300 transistors onto a single sliver of silicon. By modern standards that is almost nothing; a current smartphone chip holds tens of billions. But the leap was not about raw count. It was about the idea. For the first time, a complete central processing unit existed on one chip that an

“People just really want to take back control of their time, their lives, their attention... They’re down for whatever helps them do that.”

CEO Tim Cook said in a recent interview that the situation is "unsustainable."

Snap's long-awaited smart glasses debut hasn't exactly done wonders for the company's stock.

“People just really want to take back control of their time, their lives, their attention... They’re down for whatever helps them do that.”

Qualcomm's CEO said today that the company is working on over 40 new AI hardware designs

For over a decade now, Snap has been working on this device. Now the glasses are finally here. So what stands out on first impression?

Plaud is trying to make a mark in a crowded market full of AI-powered meeting notetakers.

You scan one to pay at a sari-sari store, pull up a restaurant menu, or board a flight. The QR code has quietly become one of the most universal pieces of interface design on the planet. But it was never meant for any of that. The QR code was invented in 1994 to solve a very specific problem on a Japanese car factory floor, and the engineering decisions made under that constraint are exactly why it later conquered the world. A barcode problem on the assembly line In the early 1990s, Toyota's manufacturing arm had a data problem. Tracking thousands of distinct components through production meant scanning barcodes, and barcodes are stingy: a standard one-dimensional barcode holds roughly 20 characters. Workers were ending up with parts plastered in ten or more barcodes just to encode enough information, and each one had to be scanned separately. It was slow, and on an assembly line, slow is expensive. Masahiro Hara, an engineer at Denso Wave, a Toyota subsidiary, took on the challenge of designing something better. He wanted a code that could hold far more data, be read much faster, and tolerate the dirt, smudges, and odd angles of a real factory rather than a clean lab. Designing for speed and any angle The breakthrough was going two-dimensional. By encoding data in a grid of black and white squares rather than a single row of lines, Hara's team could pack in thousands of characters instead of a few dozen. The name they chose, QR for "Quick Response," was a direct promise about scanning speed. The most recognizable feature of a QR code, the three large squares in its corners, solves the hardest part of the problem: letting a scanner instantly find the code and work out its orientation no matter how the part is turned. Hara's team analyzed printed material to find a black-and-white sequence that almost never occurs naturally in text and images, and settled on a ratio of 1:1:3:1:1 for those corner markers. Because that pattern is so rare in everyday print, a scanner ca

Plug a sensor into a switch, wire up a building full of cameras, or rack a server, and you are using Ethernet. It is the most widely deployed wired networking standard on earth, the quiet backbone under offices, factories, and data centers. And it is named after a scientific idea that turned out to be completely wrong. The name was not an accident or a marketing afterthought. It was a deliberate engineering choice, and the reasoning behind it explains why Ethernet outlived nearly every rival and still underpins industrial IoT half a century later. A memo, a laser printer, and a dead theory The date Ethernet enthusiasts celebrate is May 22, 1973. On that day, a young engineer named Robert Metcalfe, working at Xerox's legendary Palo Alto Research Center (PARC), circulated a memo describing how to connect the Alto - one of the first personal computers - to a new device PARC had built: the laser printer. The problem was getting many machines to share one wire without their messages colliding into noise. Metcalfe needed a name for the shared medium that carried the signals. He reached back into nineteenth-century physics and borrowed the term luminiferous ether . For generations, physicists had assumed that light, being a wave, needed something to wave through - just as sound needs air. They called that invisible, all-pervading substance the ether, and they believed it filled the entire universe as a silent carrier of electromagnetic waves. The trouble is that the ether does not exist. The famous Michelson-Morley experiment of 1887 failed to detect it, and Einstein's special relativity in 1905 made it unnecessary altogether. By the time Metcalfe wrote his memo, the luminiferous ether had been a discredited idea for decades. He used it anyway, and on purpose. Why a debunked idea made for brilliant engineering Metcalfe later explained the choice plainly: "We called it Ethernet because the ether could be coax, twisted pair, radio, optical fibers, power line, whatever you wa

I’ve struggled with insomnia since I was very young. Like many chronic overthinkers, I tend to fall asleep best when my mind is occupied by something else, such as podcasts, YouTube compilations, or my personal favorite: rain sounds. But earbuds can be uncomfortable, and playing audio out loud isn’t exactly considerate when I’m staying at […]

The new round values the physical AI startup that aims to automate heavy engineering and drug design at $41 billion.

CUDA for AMD Lemonade, Intel Arc Pro Linux Gains, XPU Manager 2.0 Today's Highlights Today's top GPU news highlights include AMD's Lemonade SDK gaining NVIDIA CUDA support, significant performance improvements for Intel Arc Pro GPUs on Linux 7.1, and the major 2.0 overhaul of Intel's XPU Manager for better GPU management on both Windows and Linux. AMD's Lemonade SDK For Local AI Adds NVIDIA CUDA Support (Phoronix) Source: https://www.phoronix.com/news/AMD-Lemonade-10.7-Released AMD has released a new version of its Lemonade SDK, a powerful local AI server solution designed to leverage AMD's diverse hardware ecosystem, including their CPUs, GPUs, and NPUs. The most significant update in this release is the addition of NVIDIA CUDA support. This integration allows developers to utilize NVIDIA GPUs within their Lemonade-powered local AI deployments, bridging a critical gap in cross-platform AI development. The inclusion of CUDA support is a strategic move, enabling Lemonade to tap into NVIDIA's extensive CUDA ecosystem and a vast array of pre-optimized models and libraries. This means that applications built with Lemonade can now seamlessly target a wider range of hardware, offering unprecedented flexibility for developers working with local AI. For users, it provides the choice to deploy their AI models on either AMD or NVIDIA hardware using a single, unified SDK, expanding the potential reach and efficiency of their AI workloads. Comment: This is a massive step for cross-vendor AI development. Being able to use AMD's Lemonade SDK to deploy local AI models and then seamlessly target NVIDIA GPUs via CUDA truly unifies the AI backend landscape for diverse hardware setups, making it incredibly practical for hybrid environments. Intel Arc Pro B70 Showing Off Some Performance Wins With Linux 7.1 (Phoronix) Source: https://www.phoronix.com/review/linux-71-arc-pro-b70 Recent testing by Phoronix indicates that Intel's Arc Pro B70 discrete GPUs are demonstrating notable perform