32 stories tagged Hardware
Researchers developed TACS-RF, an AI system that uses the Claude Opus 4.8 large language model to drive industry-standard EDA tools and design a 240-251 GHz sub-THz transmitter in a fraction of the usual time.
AIPCs.store, a sister site of AInformed, benchmarks real computers on local AI models and publishes every result. It pairs the data with a calculator that estimates whether a model fits on your machine and how fast it will run.
Cerebras Systems CEO Andrew Feldman will discuss the challenges of scaling AI at TechCrunch Disrupt 2026, focusing on compute, energy, and infrastructure constraints and how Cerebras' wafer-scale architecture offers a different approach.
Mistral has introduced a new AI model with 24 billion parameters that uses 4-bit quantization to run efficiently on consumer-grade hardware, making advanced AI capabilities more accessible.
NVIDIA's H200 GPU delivers a 40% increase in memory bandwidth and trains large language models up to 30% faster than the H100, making it a significant upgrade for enterprise AI workloads.
Google is imposing new memory restrictions on Android apps due to AI-driven hardware shortages. This could impact lower-cost phones, forcing developers to optimize their apps for smaller memory budgets.
Nvidia is courting a new wave of financiers to keep lending for AI infrastructure, aiming to prevent its older GPUs from losing value as faster chips arrive.
Anthropic, the company behind Claude, is assembling a team to design its own AI chips. This move aims to make its AI models run faster and more efficiently by co-designing hardware and software together.
Researchers introduced Nova, an automated end-to-end JIT compiler for deep learning that maps high-level mathematical operations directly to hardware. It promises to improve AI model performance and energy efficiency by providing whole-graph visibility and granular control over hardware and memory.
AMD has unveiled Helios, a new rack-scale AI system designed to rival Nvidia's offerings. The system will start shipping to customers later this year, giving businesses and researchers a high-performance alternative for large-scale AI workloads.
Etched, an AI chip startup founded by three Harvard dropouts, has secured a $10.3 billion valuation from major investors. The company claims its new chips and memory components accelerate AI inference without requiring traditional GPUs, potentially disrupting Nvidia's dominance in the AI hardware market.
Lunar, an AI research lab, has released an open-source AI model that runs on standard consumer hardware such as gaming PCs, eliminating the need for expensive servers and making advanced AI accessible to hobbyists and small businesses.
Apple has sued OpenAI, potentially derailing the AI company's plans to enter the hardware market and go public. The legal battle centers on alleged intellectual property theft and could reshape the AI hardware landscape.
NVIDIA is creating AI models that are optimized to work seamlessly with their hardware. This approach could make AI systems faster and more efficient for everyone.
Meta is starting production of its own AI chips designed for flexibility as AI technology evolves. This move could help Meta reduce its reliance on third-party hardware providers.
AMD's new Ryzen AI Halo brings powerful AI capabilities to your home computer, but it comes with a hefty price tag of $4,000. This system is designed to run AI models locally, offering privacy and speed benefits.
Anthropic, a leading AI company, is in talks with Samsung to develop custom AI chips. This follows OpenAI's recent announcement of a similar partnership with Broadcom.
Etched, a rival to Nvidia in AI chips, has secured $1 billion in sales and a $5 billion valuation. The company says the sales are under contract for inference systems powered by its chip, marking a significant shift in the competitive landscape for AI hardware.
OpenAI is releasing a physical device for its AI coding tool, Codex, on July 15th. The square-shaped device with buttons is designed to provide quick shortcuts for Codex. This is separate from the mysterious AI hardware project OpenAI is working on with other partners.
OpenAI unveiled Jalapeño, a custom AI inference chip built with Broadcom, marking a major step toward reducing reliance on Nvidia. The move highlights a broader industry trend by Google, Apple, and SpaceX to avoid single-supplier risk and could lead to faster, cheaper AI services.
Researchers have identified key ways AI models fail when translating programming logic into hardware design. The findings highlight a 90.8% success rate ceiling for current models, with specific types of errors limiting their effectiveness.
Hugging Face has integrated AI models with robot hardware, allowing developers to deploy models directly to robots. This breakthrough makes advanced AI capabilities accessible to physical robots for the first time.
Researchers have released OpenRTLSet, the largest open-source dataset for hardware design, featuring 131,000 Verilog code samples. This resource enables AI models to learn and generate hardware designs, potentially speeding up innovation in electronics.
Researchers developed StepPRM-RTL, an AI framework that enhances the accuracy of automatically generating hardware code. This could make designing digital circuits faster and more reliable for engineers.