Chips

Nvidia's predicting it will pull in $108 billion in revenue within just a few months. It wouldn't be the first company to rake in over $100 billion in quarterly revenue - Amazon, Apple, and Alphabet have repeatedly reached the milestone. Nvidia said in its latest earnings report that it brought in a record $96.2 billion in overall revenue in the past quarter, a jump of over $10 billion from the previous quarter. Its data center revenue alone more than doubled year-over-year to a record $89 billion, and the company's profits more than doubled to $59.7 billion. Nvidia's "edge computing" category, which includes its consumer gam … Read the full story at The Verge.

Z.ai has released GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series — a 320B-total / 18B-active MoE with a 1,048,576-token context window, MIT-licensed weights on Hugging Face, and API pricing at $0.15/M input and $0.50/M output. It scores 84.3 on Terminal-Bench 2.1 and 63.4 on DeepSWE v1.1, using hybrid KDA linear plus NoPE sparse MLA attention to cut attention compute ~3× and KV cache 4.4× versus GLM-5.3. The post Z.ai Releases GLM-5.3-Flash: A 320B-A18B Natively Multimodal MoE With a 1M-Token Context appeared first on MarkTechPost.

Meta's new MTIA 400 chip has a split personality: Training AI and serving ads Faster than Blackwell, but still no replacement for AMD or Nvidia ... yet Tobias Mann Tobias Mann SYSTEMS EDITOR Published wed 26 Aug 2026 //…

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 […]

https://p.dw.com/p/5JOAz AI is bringing significant challenges for the gaming industry, but it could also help significantly reduce costsImage: Political-Moments/IMAGO Earlier this month, gaming giant Electronic Arts wa…

Is a foldable iPhone finally in our future? We'll be on the ground on Sept. 9 to find out.

TL;DR: AgentPad13 is an open-source take on the $230 Codex Micro and a test of whether an autonomous scientist can teach itself PCB routing. We wanted a more wallet-friendly, open-source Codex Micro, so we asked Marvin…

Despite rapid advances in artificial intelligence, the enterprise world is still dealing with a data pipeline problem. More than 80% of enterprise data is unstructured, and 99% of this data is dark to AI because there is no easy solution to query it, according to industry experts. Yet organizations are still trying to build AI […] The post AMD, Supermicro and MinIO target the enterprise data pipeline bottleneck appeared first on SiliconANGLE.

The SageMaker Python SDK v3 redesigns script mode with unified ModelTrainer and ModelBuilder classes. This post walks through two end-to-end examples, a scikit-learn Random Forest and a multi-GPU Stable Diffusion 3.5 LoRA fine-tune, showing how SourceCode syncs your local code into any container at runtime so you can iterate without rebuilding Docker images.

Ox-alpha, the stealth model that quickly became the most popular model on OpenRouter in the last few days, is actually The post Z.ai’s GLM-5.3 Flash is cheap, good, and served on Chinese chips appeared first on The New Stack.

The Importance of Reading (and Teaching) Cyberpunk in the Age of AI - Reactor 0 Share Featured Essays Cyberpunk The Importance of Reading (and Teaching) Cyberpunk in the Age of AI Looking for answers — and finding hope…

Build, deploy, and monitor production-grade AI agents at scale with LangChain's enterprise agentic AI platform integrated with NVIDIA.

In a year of iterative upgrades, Google is introducing smart features that make the base Pixel still the one to buy for most people.

Launch price: $49/month, yours until you cancel. It rises after the first 100 customers. ReachFastSign in Your next customers are already asking on ReachFast finds people asking for your products across X, Reddit, Linke…

We look at Qwen3.8-Flash-Next, Alibaba's open-weight multimodal Mixture-of-Experts model and an early preview of the Qwen4 architecture. We break down where the 180B parameters actually sit: a 125B backbone, a 51B N-gram embedding table, and a 4B multi-token prediction module, with only 6B active per token. We walk through the four architectural changes — the Gated DeltaNet and Qwen Sparse Attention hybrid, Gated Residual, N-gram Embedding, and the Muon optimizer. We also cover the benchmark results, the reported 1/9 training cost against Qwen3.7-Plus, and what self-hosting a 172.78 GiB FP8 checkpoint really demands. The post Alibaba’s Qwen Team Releases Qwen3.8-Flash-Next: A 125B Multimodal MoE With 6B Active Parameters Previewing the Qwen4 Architecture appeared first on MarkTechPost.

Intel Crescent Island GPU Render - Image: Intel What kind of hardware do you need for AI processing? Well, every kind, because "AI processing" is a very broad term. Unlike a lot of specialized AI chips (e.g. d-Matrix Ra…

Photo: Steve A Johnson / Pexels Nvidia’s Vera CPU outpaces AMD EPYC 9655P in Linux kernel compilation at Hot Chips 2026 The chipmaker's new Vera CPU, Rubin GPU, and networking stack represent a coordinated bet that agen…

Piketon datacenter promises to generate thousands of jobs, but environmental groups voice concern over project On a winding road tucked away behind forests in the Appalachian foothills of southern Ohio is where OpenAI, Nvidia and Japanese investors are set to spend $500bn on one of the largest artificial intelligence datacenters on the planet. Last March, the energy secretary, Chris Wright, the commerce secretary, Howard Lutnick and a host of Japanese and other dignitaries briefly descended on Piketon to enthusiastically break ground on a project to build 8GW worth of AI computing power. Continue reading...

Prompt Claude, ChatGPT, Gemini, or any other popular large language model (LLM) with a question like “What is the best film ever made?” and the response will vary, and you (and most worryingly, the people who built the LLM) have little idea exactly how it came up with that specific answer. This mysterious behavior can be useful in some situations. But—as a recent incident where OpenAI could not explain why its advanced pre-release model hacked AI company Hugging Face highlighted—it can have negative and alarming consequences too. And when frontier AI models are writing code, generating results humans could not achieve alone, and performing other important tasks across society, the need to interpret AI ‘thinking’ and outputs has never been greater. Goodfire, an AI lab focused solely on this very problem, recently made its cutting-edge Silico platform, filled with tools to interpret the behavior of AI, generally available to the public. As part of this, the company recently announced a new grant program offering $1 million in free Silico usage for academic and nonprofit interpretability researchers. These efforts aim to democratize AI interpretability, placing techniques previously available to a clutch of elite labs into the hands of ambitious research teams and startups that want to build and understand their own models or adapt open-source models for different purposes. Mechanistic interpretability Founded in 2024 and based in San Francisco, Goodfire aims to provide the tools that build the next generation of safe and powerful AI by understanding the structures inside them instead of treating AI models as black boxes. “Treating models like black boxes isn’t inevitable, it’s a choice,” says Eric Ho, Goodfire co-founder and CEO. “With the right interpretability tools, we can see how models actually work.” The tools Ho refers to are built around a concept called mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by interpreting the model’s weights, activations, and attention patterns, and mapping its neurons and the pathways between them. Mechanistic interpretability tools span the gamut. One approach is mapping a model’s activations in response to controlled prompts, and matching those activation patterns to a set of human-understandable concepts. Another tack is tracking changes in model weights before and after a specific training run in order to spot and understand what changed. Yet another option is changing specific model weights or activations and observing how that affects the model’s output. With Silico, uSilico combines a broad range of these tools, and provides a layer of AI agents to help users understand their model. Users describe what they want to investigate about their AI model in plain language, asking things like ‘Find out when and why my model is hallucinating.’ The platform then autonomously builds an experimental plan involving a host of tasks that can be performed using the various interpretability tools and techniques at its disposal. It then sends out agents to perform these tasks in parallel. Completion of these subtasks should add up to an answer to the original prompt, or at least insights that can be inspected and built upon. Ho says: “In a sense, Silico is like a microscope to peer inside an AI model to understand which parts are responsible for what behavior, and even edit those parts directly.” Understanding Alzheimer’s and AI These tools have already been used to make some impressive advances in a host of fields. In medicine, for instance, Prima Mente, a UK-based AI company, worked with Goodfire to understand its Pleiades epigenetic foundation model. The model performed well at its task of detecting Alzheimer’s disease from blood samples, but the company didn’t know why. “We reverse-engineered Pleiades and found it was using DNA fragment-length patterns to make its predictions—a signal humans hadn’t used to detect Alzheimer’s before,” recalls Ho. In other words, the team had discovered that Pleiades was using a completely new biomarker for the disease. “As far as we know, it’s the first significant finding in the natural sciences discovered purely by reverse-engineering a foundation model,” Ho adds. Elsewhere, Silico is being used to explore deep questions surrounding AI. Cameron Berg, Founder and Director of Reciprocal Research (a New York nonprofit research organization he founded to explore methods of gauging AI cognition), says that Silico almost fell out of the sky at the right time for him and his research. “Silico has been really helpful for operationalizing my research agenda and executing on it way faster than I would have expected,” he says. “ I feel like I have basically become the PI and my research scientists and research engineers are AI systems.” Berg sees general access to Silico and tools like it leading to greater trust in AI’s ability to conduct research tasks, which will accelerate the scientific process across the board. But beyond scientific research, the widespread release of Silico could signal a shift in how AI innovators build, debug, and deploy their models. “I think it’s a mistake to not understand the most consequential technology of our time, particularly given the emergent behavior we’re seeing from increasingly capable AI agents,” says Ho. “If we truly understand how AI models think, instead of discovering and trying to correct their behavior retroactively, we can design them intentionally and shape how models behave to be safer and more reliable.”

China’s Moonshot AI is in early talks with Microsoft, Amazon and Google to host Kimi K3 Credit: Bangla press via Shutterstock.com Moonshot AI is in early discussions with Microsoft, Amazon, and Google about hosting Kimi…

Apple Updates Mini and Studio, AI Computers, OpenAI Jalapeño Wednesday, August 26, 2026 Listen to Podcast Apple and OpenAI have two completely different hardware announcements; both represent pressure on Nvidia. Subscri…

Even the best AI models can suck at chess The launch of ChatGPT had an interesting effect on the online chess discourse. Chess has already long been conquered by machines. As early as 1996 a computer (IBM’s Deep Blue) was able to beat the human world champion, grandmaster Garry Kasparov, in a game watched by […]

...\n **config_kwargs,\n )\n"," File \"/usr/local/lib/python3.14/site-packages/datasets/inspect.py\", line 291, in get_dataset_config_info\n raise SplitsNotFoundError(\"The split names could not be parsed from the datas…

The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.

Drive-By Agent Hijacking: One Website Visit, Persistent Model Poisoning CustomersPricing Back Back Back Back Get a demo Elad Luz Ofek Itach Nemoclaw CVE-2026-65105: One Website Visit to Hijack Your AI Agent A vulnerabil…

5 Join the conversation Follow us Add us as a preferred source on Google A Russian Molniya drone carrying an Nvidia Jetson Orin module crashed and killed three civilians at a gas station in Zaporizhzhia last month after…

Make every site work for you. Describe the outcome. Retriever AI works across the open web and the sites you’re signed into, then brings back the finished result. Add to Chrome⭐⭐⭐⭐4⭐Run in cloud 7M+ tasks automated#1 on…

IBM has released Granite 4.2, a family of open reasoning language models in 3B, 8B, and 30B sizes, all under Apache 2.0. Every model exposes a thinking / low-effort / non-thinking switch and native tool calling. The 8B and 30B additionally go through an agentic RL block that trains them to edit code, drive a terminal, and run web searches inside real sandboxed environments. The 30B reports 57.00 on SWE-Bench Verified and 29.24 on Terminal-Bench 2.1. The post IBM Releases Granite 4.2: Bringing Native Reasoning and Agentic RL to Open Enterprise Models appeared first on MarkTechPost.

Now Perplexity is trying to get into the local AI action Amid talk of an Nvidia deal, the AI search biz is looking beyond the cloud Thomas Claburn Thomas Claburn AI AND SOFTWARE REPORTER Published wed 26 Aug 2026 // 00:…

arXiv:2608.23972v1 Announce Type: new Abstract: Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-aware-stl-mppi, a computationally efficient sampling-based receding-horizon planning framework designed to promote satisfaction of constraints expressed in Signal Temporal Logic (STL). Our approach encodes discrete-time STL formulas into candidate time-varying control barrier functions (CBF), which are integrated into a model predictive path integral (MPPI) controller. Our method inherits the benefits of low computational cost from an efficiently parallelizable sampling based planner and utilizes CBF for constraints expressed in STL. We compare against several MPPI baselines using four artificial Mars Rover planning case studies with a diverse environment and cost setups, where we show our method consistently achieving high safety and efficiency. We show a quadcopter planning experiment with NVIDIA Isaac Lab.

arXiv:2608.23575v1 Announce Type: new Abstract: We convert drone-vision annotation streams into virtual swarm-game states without controlling physical drones. VisDrone and UAVSwarm metadata are compressed into a Bloom representation; deterministic probes produce bounded capability vectors, image-space formations, finite zero-sum payoffs, and human-readable visual overlays. The audit scales from $6\times 6$ to $32\times 32$ finite games and adds a repeated Markov layer with stock, fatigue, adaptation, exposure, stress, budget, data-growth, model-improvement, and entropy-budget state variables. Local screen tuning raises robust screen security from $0.526$ to $0.593$, and the $32\times 32$ tuned screen reaches value $0.616$. A field readout audit shows that fixed-pixel rasters do not improve monotonically: $128\times 128$ accuracy is $67.2\%$ and hotspot error is $0.136$. The diagnosed error is shrinking image-plane bandwidth. A finite empirical-risk encoder over scale-normalized Gaussian bandwidths selects a scale-normalized encoder with $\lambda=1.50$, reaching $77.6\%$ accuracy at $128\times 128$ and reducing joint loss by $0.185$. A server-side audit checks $16{,}777{,}216$ target-localization states, and a 32-round repeated-game audit over $16{,}777{,}216$ trajectories selects a budget-adaptive policy with value $0.461$.

Facebook X Pinterest Linkedin ReddIt Email Print Copy URL Microsoft-Maia200-Hero The fourth AI accelerator presentation of Hot Chips 2026 comes from Microsoft, who like so many other hyperscalers has gone into the busin…

Maria Sukhareva Aug 25, 2026 ∙ Paid Hype-free executive briefing on last week’s critical AI developments, complete with ready-to-present slides for your team. If you are a paid subscriber, you can listen to the radar in…

Model cards report quality under server-class, full-precision conditions. Those numbers rarely predict how the same model behaves on a phone. This week, Liquid AI released Pipette. It is an open-source platform for benchmarking foundation models on edge devices, built in partnership with Artificial Analysis as an independent methodology validator. Pipette treats on-device behavior as a […] The post Liquid AI Open-Sources Pipette: A Reproducible Benchmarking Suite That Measures On-Device Models, Quantization, Runtime and Hardware Together appeared first on MarkTechPost.

0 Join the conversation Follow us Add us as a preferred source on Google Earlier this month, Samsung introduced the industry's first LPDDR5X-PIM memory, adding in-memory logic to the low-power memory standard, and at Ho…

AI Disruptors: How the Next Generation of Business is Being Built | DigitalOcean Community AI Disruptors: How the Next Generation of Business is Being Built Updated: May 29, 2026 8 min read See author profile Community…

Augmented Mind: Think with AI and Manolo Remiddi Aug 21, 2026 You can now run an almost frontier class model, locally, on your own hardware. I run one, and I can tell you: it is real, it is fast, and it changed how I wo…

Perplexity AI Inc. today introduced Portable Computer, an artificial intelligence agent designed to run on desktops equipped with Nvidia Corp. silicon. The launch follows a report that Nvidia is weighing an investment in the startup that could value it at over $30 billion. Furthermore, Nvidia has reportedly floated the idea of licensing Perplexity’s technology and […] The post Perplexity AI launches Portable Computer on-device AI agent appeared first on SiliconANGLE.

Learn how to fine-tune and evaluate LLMs with LangSmith for dataset management. Complete guide covers LLaMA2 and GPT-3.5 fine-tuning with practical examples.

Apple Inc. today debuted four custom processors that will power a new generation of Macs. The company is bringing to market a new Mac mini miniature desktop and Mac Studio workstation that are both available in two editions. Each edition features a different processor. All the chips include a central processing unit, a graphics processing […] The post Apple refreshes Mac mini, Mac Studio lineups with new chips appeared first on SiliconANGLE.

AI storage infrastructure is becoming a more consequential planning issue as organizations move from model training toward agentic AI. As agents reason, act and reassess, they build longer contexts and generate more data that they must access quickly during inference. Agentic AI is also changing the shape of the data problem. Interactions are growing longer […] The post AI inference gets a new tier as context windows grow appeared first on SiliconANGLE.

On Tuesday, IBM launched the latest family of its open-weight Granite large language models (LLMs). Weighing in at 3 billion, The post IBM’s new Granite 4.2 models add reasoning and stay dense appeared first on The New Stack.

Perplexity releases Portable Computer, packaging local models, harness, sandbox, and connectors into one system running on NVIDIA DGX Spark. The post Perplexity Ships Portable Computer on NVIDIA DGX Spark: Local Harness, OS-Enforced Sandbox, and Zero Per-Token Cost for Local Steps appeared first on MarkTechPost.

Among those charged are two Super Micro employees and one from Nvidia, marking another flashpoint in US-China AI rivalry Taiwanese prosecutors charged nine people Monday, including one from Nvidia and two from Super Micro, for illegally exporting “high-end AI servers” to mainland China, adding another wave of turbulence in the AI ​​rivalry between China and the United States. Prosecutors said the servers involved were graphics processing units known as “B300,” which have been banned from sale to China. Continue reading...

Training and serving frontier models is now a networking problem as much as a compute problem. Collective operations like all-reduce and all-to-all synchronize thousands of accelerators during training, and the slowest transfer sets the pace for the entire job. Even small amounts of network friction directly strand significant compute capacity. This week, Meta introduced MetaRoCE. […] The post Meta AI Introduces MetaRoCE: A Clean-Sheet RDMA Transport Built for AI-Scale Ethernet appeared first on MarkTechPost.

Chipmaker Nvidia Corp. says its dedicated artificial intelligence inference accelerator Groq 3 LPX has now entered full production as it strives to maintain its dominance in the world of AI compute. The new chip, announ…

The AI Hater's Manifesto Ed Zitron Aug 25, 2026 50 min read If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, $17 a quarter, or $7 a month, and in return you get a weekly newslette…

Perplexity, in partnership with Nvidia, has taken Computer, its agentic AI assistant, and brought it to the desktop in the The post Perplexity’s Computer agent can now run locally — if you can afford it appeared first on The New Stack.

NVIDIA is bringing the next wave of RTX gaming to the Gamescom conference running this week in Cologne, Germany, with support for new games, anti-cheat technologies and increased visual quality. Electronic Arts, Embark and Ubisoft are among the latest game publishers and developers bringing their blockbuster titles to NVIDIA RTX Spark ahead of its launch […]

The Mac Studio with the M5 Ultra will feature up to a whopping 512GB of unified memory, and drive up to eight external displays.