Last summer, the ousted leadership team at Subnautica 2 developer Unknown Worlds launched a lawsuit against its parent company Krafton alleging wrongful termination and an attempt to bypass a previously negotiated multi-million dollar bonus. Now, a ruling has come down in favor of the plaintiffs that orders Krafton to reinstate CEO Ted Gill to his former position and return control of Subnautica 2's release plans. The judgment also calls out Krafton's decision to use a ChatGPT-inspired legal strategy to avoid paying Unknown Worlds co-founders Charlie Cleveland and Max McGuire, as well as Gill. The judge's decision has been released online , and it features a detailed timeline of Krafton's alleged actions, particularly Krafton CEO Changhan Kim, who consulted with ChatGPT to find a way out of paying the performance bonus that the company had internally calculated would be between $191.8 million and $242.2 million. Krafton's internal legal team cautioned Kim against following the strategy laid out by ChatGPT, but those words of caution were apparently ignored. According to the ruling, Krafton followed some of the steps laid out by ChatGPT, including removing Unknown Worlds' ability to release Subnautica 2 on Steam. It also notes that Krafton's actions had the desired effect of bringing Unknown Worlds' leadership team to negotiations over a potentially lesser amount for their bonus. But when those negotiations stalled, Krafton fired Cleveland, McGuire, and Gill. Continue Reading at GameSpot

• GameSpot
• 2 gün, 10 saat, 33 dakika

State attorneys general won't get climate chapter removed from a legal manual.

• Ars Technica
• 2 gün, 10 saat, 34 dakika

This Android gaming handheld looks like the Motorola Flipout brought back to life.

• Android Authority
• 2 gün, 10 saat, 37 dakika

Unboxing videos, pricing leaks, and internal hardware reveals have made the Galaxy A57 the most thoroughly leaked mid-ranger of 2026 — and Samsung hasn't said a word yet. The post Samsung’s upcoming Galaxy A57 mid-ranger just won’t stop appealing in leaks appeared first on Digital Trends .

• Digital Trends
• 2 gün, 10 saat, 37 dakika

The life-size, walking, talking robot will debut in parks later this month, representing huge leaps for Disney.

• Gizmodo
• 2 gün, 10 saat, 38 dakika

Tobias Mann / The Register : Nvidia unveils a server rack with 256 Vera CPUs, with each CPU featuring 88 custom Olympus cores and LPDDR5X memory for up to 1.2 TB/s of bandwidth —  GTC Intel and AMD take notice.  At GTC on Monday, Nvidia unveiled its latest liquid-cooled rack systems.

• Techmeme
• 2 gün, 10 saat, 38 dakika

Memories.ai is building a large visual memory model that can index and retrieve video-recorded memories for physical AI.

• TechCrunch
• 2 gün, 10 saat, 38 dakika

How to watch the Champions League for free. Watch Arsenal vs. Leverkusen in the Champions League for free from anywhere in the world.

• Mashable
• 2 gün, 10 saat, 38 dakika

Nvidia on Monday took the wraps off Vera Rubin , a sweeping new computing platform built from seven chips now in full production — and backed by an extraordinary lineup of customers that includes Anthropic, OpenAI, Meta and Mistral AI, along with every major cloud provider. The message to the AI industry, and to investors, was unmistakable: Nvidia is not slowing down. The Vera Rubin platform claims up to 10x more inference throughput per watt and one-tenth the cost per token compared with the Blackwell systems that only recently began shipping. CEO Jensen Huang, speaking at the company's annual GTC conference , called it "a generational leap" that would kick off "the greatest infrastructure buildout in history." Amazon Web Services , Google Cloud , Microsoft Azure and Oracle Cloud Infrastructure will all offer the platform, and more than 80 manufacturing partners are building systems around it. "Vera Rubin is a generational leap — seven breakthrough chips, five racks, one giant supercomputer — built to power every phase of AI," Huang declared. "The agentic AI inflection point has arrived with Vera Rubin kicking off the greatest infrastructure buildout in history." In any other industry, such rhetoric might be dismissed as keynote theater. But Nvidia occupies a singular position in the global economy — a company whose products have become so essential to the AI boom that its market capitalization now rivals the GDP of mid-sized nations. When Huang says the infrastructure buildout is historic, the CEOs of the companies actually writing the checks are standing behind him, nodding. Dario Amodei, the chief executive of Anthropic, said Nvidia's platform "gives us the compute, networking and system design to keep delivering while advancing the safety and reliability our customers depend on." Sam Altman, the chief executive of OpenAI, said that "with Nvidia Vera Rubin, we'll run more powerful models and agents at massive scale and deliver faster, more reliable systems to hundreds of millions of people." Inside the seven-chip architecture designed to power the age of AI agents The Vera Rubin platform brings together the Nvidia Vera CPU, Rubin GPU, NVLink 6 Switch, ConnectX-9 SuperNIC, BlueField-4 DPU, Spectrum-6 Ethernet switch and the newly integrated Groq 3 LPU — a purpose-built inference accelerator. Nvidia organized these into five interlocking rack-scale systems that function as a unified supercomputer. The flagship NVL72 rack integrates 72 Rubin GPUs and 36 Vera CPUs connected by NVLink 6. Nvidia says it can train large mixture-of-experts models using one-quarter the GPUs required on Blackwell, a claim that, if validated in production, would fundamentally alter the economics of building frontier AI systems. The Vera CPU rack packs 256 liquid-cooled processors into a single rack, sustaining more than 22,500 concurrent CPU environments — the sandboxes where AI agents execute code, validate results and iterate. Nvidia describes the Vera CPU as the first processor purpose-built for agentic AI and reinforcement learning, featuring 88 custom-designed Olympus cores and LPDDR5X memory delivering 1.2 terabytes per second of bandwidth at half the power of conventional server CPUs. The Groq 3 LPX rack, housing 256 inference processors with 128 gigabytes of on-chip SRAM, targets the low-latency demands of trillion-parameter models with million-token contexts. The BlueField-4 STX storage rack provides what Nvidia calls "context memory" — high-speed storage for the massive key-value caches that agentic systems generate as they reason across long, multi-step tasks. And the Spectrum-6 SPX Ethernet rack ties it all together with co-packaged optics delivering 5x greater optical power efficiency than traditional transceivers. Why Nvidia is betting the future on autonomous AI agents — and rebuilding its stack around them The strategic logic binding every announcement Monday into a single narrative is Nvidia’s conviction that the AI industry is crossing a threshold. The era of chatbots — AI that responds to a prompt and stops — is giving way to what Huang calls " agentic AI ": systems that reason autonomously for hours or days, write and execute software, call external tools, and continuously improve. This isn't just a branding exercise. It represents a genuine architectural shift in how computing infrastructure must be designed. A chatbot query might consume milliseconds of GPU time. An agentic system orchestrating a drug discovery pipeline or debugging a complex codebase might run continuously, consuming CPU cycles to execute code, GPU cycles to reason, and massive storage to maintain context across thousands of intermediate steps. That demands not just faster chips, but a fundamentally different balance of compute, memory, storage and networking. Nvidia addressed this with the launch of its Agent Toolkit , which includes OpenShell, a new open-source runtime that enforces security and privacy guardrails for autonomous agents. The enterprise adoption list is remarkable: Adobe, Atlassian, Box, Cadence, Cisco, CrowdStrike, Dassault Systèmes, IQVIA, Red Hat, Salesforce, SAP, ServiceNow, Siemens and Synopsys are all integrating the toolkit into their platforms. Nvidia also launched NemoClaw , an open-source stack that lets users install its Nemotron models and OpenShell runtime in a single command to run secure, always-on AI assistants on everything from RTX laptops to DGX Station supercomputers. The company separately announced Dynamo 1.0 , open-source software it describes as the first "operating system" for AI inference at factory scale. Dynamo orchestrates GPU and memory resources across clusters and has already been adopted by AWS, Azure, Google Cloud, Oracle, Cursor, Perplexity, PayPal and Pinterest. Nvidia says it boosted Blackwell inference performance by up to 7x in recent benchmarks. The Nemotron coalition and Nvidia’s play to shape the open-source AI landscape If Vera Rubin represents Nvidia's hardware ambition, the Nemotron Coalition represents its software ambition. Announced Monday, the coalition is a global collaboration of AI labs that will jointly develop open frontier models trained on Nvidia's DGX Cloud . The inaugural members — Black Forest Labs , Cursor , LangChain , Mistral AI , Perplexity , Reflection AI , Sarvam and Thinking Machines Lab , the startup led by former OpenAI executive Mira Murati — will contribute data, evaluation frameworks and domain expertise. The first model will be co-developed by Mistral AI and Nvidia and will underpin the upcoming Nemotron 4 family. "Open models are the lifeblood of innovation and the engine of global participation in the AI revolution," Huang said. Nvidia also expanded its own open model portfolio significantly. Nemotron 3 Ultra delivers what the company calls frontier-level intelligence with 5x throughput efficiency on Blackwell. Nemotron 3 Omni integrates audio, vision and language understanding. Nemotron 3 VoiceChat supports real-time, simultaneous conversations. And the company previewed GR00T N2, a next-generation robot foundation model that it says helps robots succeed at new tasks in new environments more than twice as often as leading alternatives, currently ranking first on the MolmoSpaces and RoboArena benchmarks. The open-model push serves a dual purpose. It cultivates the developer ecosystem that drives demand for Nvidia hardware, and it positions Nvidia as a neutral platform provider rather than a competitor to the AI labs building on its chips — a delicate balancing act that grows more complex as Nvidia's own models grow more capable. From operating rooms to orbit: how Vera Rubin's reach extends far beyond the data center The vertical breadth of Monday's announcements was almost disorienting. Roche revealed it is deploying more than 3,500 Blackwell GPUs across hybrid cloud and on-premises environments in the U.S. and Europe — the largest announced GPU footprint in the pharmaceutical industry. The company is using the infrastructure for biological foundation models, drug discovery and digital twins of manufacturing facilities, including its new GLP-1 facility in North Carolina. Nearly 90 percent of Genentech's eligible small-molecule programs now integrate AI, Roche said, with one oncology molecule designed 25 percent faster and a backup candidate delivered in seven months instead of more than two years. In autonomous vehicles, BYD , Geely , Isuzu and Nissan are building Level 4-ready vehicles on Nvidia’s Drive Hyperion platform. Nvidia and Uber expanded their partnership to launch autonomous vehicles across 28 cities on four continents by 2028, starting with Los Angeles and San Francisco in the first half of 2027. The company introduced Alpamayo 1.5 , a reasoning model for autonomous driving already downloaded by more than 100,000 automotive developers, and Nvidia Halos OS , a safety architecture built on ASIL D-certified foundations for production-grade autonomy. Nvidia also released the first domain-specific physical AI platform for healthcare robotics, anchored by Open-H — the world's largest healthcare robotics dataset, with over 700 hours of surgical video. CMR Surgical, Johnson & Johnson MedTech and Medtronic are among the adopters. And then there was space. The Vera Rubin Space Module delivers up to 25x more AI compute for orbital inferencing compared with the H100 GPU. Aetherflux, Axiom Space, Kepler Communications, Planet Labs and Starcloud are building on it. "Space computing, the final frontier, has arrived," Huang said, deploying the kind of line that, from another executive, might draw eye-rolls — but from the CEO of a company whose chips already power the majority of the world's AI workloads, lands differently. The deskside supercomputer and Nvidia’s quiet push into enterprise hardware Amid the spectacle of trillion-parameter models and orbital data centers, Nvidia made a quieter but potentially consequential move: it launched the DGX Station , a deskside system powered by the GB300 Grace Blackwell Ultra Desktop Superchip that delivers 748 gigabytes of coherent memory and up to 20 petaflops of AI compute performance. The system can run open models of up to one trillion parameters from a desk. Snowflake , Microsoft Research , Cornell , EPRI and Sungkyunkwan University are among the early users. DGX Station supports air-gapped configurations for regulated industries, and applications built on it move seamlessly to Nvidia's data center systems without rearchitecting — a design choice that creates a natural on-ramp from local experimentation to large-scale deployment. Nvidia also updated DGX Spark , its more compact system, with support for clustering up to four units into a "desktop data center" with linear performance scaling. Both systems ship preconfigured with NemoClaw and the Nvidia AI software stack, and support models including Nemotron 3, Google Gemma 3, Qwen3, DeepSeek V3.2, Mistral Large 3 and others. Adobe and Nvidia separately announced a strategic partnership to develop the next generation of Firefly models using Nvidia’s computing technology and libraries. Adobe will also build a cloud-native 3D digital twin solution for marketing on Nvidia Omniverse and integrate Nemotron capabilities into Adobe Acrobat. The partnership spans creative tools including Photoshop, Premiere Pro, Frame.io and Adobe Experience Platform. Building the factories that build intelligence: Nvidia’s AI infrastructure blueprint Perhaps the most telling indicator of where Nvidia sees the industry heading is the Vera Rubin DSX AI Factory reference design — essentially a blueprint for constructing entire buildings optimized to produce AI. The reference design outlines how to integrate compute, networking, storage, power and cooling into a system that maximizes what Nvidia calls "tokens per watt," along with an Omniverse DSX Blueprint for creating digital twins of these facilities before they are built. The software stack includes DSX Max-Q for dynamic power provisioning — which Nvidia says enables 30 percent more AI infrastructure within a fixed-power data center — and DSX Flex, which connects AI factories to power-grid services to unlock what the company estimates is 100 gigawatts of stranded grid capacity. Energy leaders Emerald AI, GE Vernova, Hitachi and Siemens Energy are using the architecture. Nscale and Caterpillar are building one of the world's largest AI factories in West Virginia using the Vera Rubin reference design. Industry partners Cadence , Dassault Systèmes , Eaton , Jacobs , Schneider Electric , Siemens , PTC , Switch , Trane Technologies and Vertiv are contributing simulation-ready assets and integrating their platforms. CoreWeave is using Nvidia's DSX Air to run operational rehearsals of AI factories in the cloud before physical delivery. "In the age of AI, intelligence tokens are the new currency, and AI factories are the infrastructure that generates them," Huang said. It is the kind of formulation — tokens as currency, factories as mints — that reveals how Nvidia thinks about its place in the emerging economic order. What Nvidia's grand vision gets right — and what remains unproven The scale and coherence of Monday's announcements are genuinely impressive. No other company in the semiconductor industry — and arguably no other technology company, period — can present an integrated stack spanning custom silicon, systems architecture, networking, storage, inference software, open models, agent frameworks, safety runtimes, simulation platforms, digital twin infrastructure and vertical applications from drug discovery to autonomous driving to orbital computing. But scale and coherence are not the same as inevitability. The performance claims for Vera Rubin , while dramatic, remain largely unverified by independent benchmarks. The agentic AI thesis that underpins the entire platform — the idea that autonomous, long-running AI agents will become the dominant computing workload — is a bet on a future that has not yet fully materialized. And Nvidia's expanding role as a provider of models, software, and reference architectures raises questions about how long its hardware customers will remain comfortable depending so heavily on a single supplier for so many layers of their stack. Competitors are not standing still. AMD continues to close the gap on data center GPU performance. Google's TPUs power some of the world's largest AI training runs. Amazon's Trainium chips are gaining traction inside AWS. And a growing cohort of startups is attacking various pieces of the AI infrastructure puzzle. Yet none of them showed up at GTC on Monday with endorsements from the CEOs of Anthropic and OpenAI. None of them announced seven new chips in full production simultaneously. And none of them presented a vision this comprehensive for what comes next. There is a scene that repeats at every GTC: Huang, in his trademark leather jacket, holds up a chip the way a jeweler holds up a diamond, rotating it slowly under the stage lights. It is part showmanship, part sermon. But the congregation keeps growing, the chips keep getting faster, and the checks keep getting larger. Whether Nvidia is building the greatest infrastructure in history or simply the most profitable one may, in the end, be a distinction without a difference.

• VentureBeat
• 2 gün, 10 saat, 38 dakika

When an AI agent loses context mid-task because traditional storage can't keep pace with inference, it is not a model problem — it is a storage problem. At GTC 2026, Nvidia announced BlueField-4 STX, a modular reference architecture that inserts a dedicated context memory layer between GPUs and traditional storage, claiming 5x the token throughput, 4x the energy efficiency and 2x the data ingestion speed of conventional CPU-based storage. The bottleneck STX targets is key-value cache data. KV cache is the stored record of what a model has already processed — the intermediate calculations an LLM saves so it does not have to recompute attention across the entire context on every inference step. It is what allows an agent to maintain coherent working memory across sessions, tool calls and reasoning steps. As context windows grow and agents take more steps, that cache grows with them. When it has to traverse a traditional storage path to get back to the GPU, inference slows and GPU utilization drops. STX is not a product Nvidia sells directly. It is a reference architecture the company is distributing to its storage partner ecosystem so vendors can build AI-native infrastructure around it. STX puts a context memory layer between GPU and disk The architecture is built around a new storage-optimized BlueField-4 processor that combines Nvidia's Vera CPU with the ConnectX-9 SuperNIC. It runs on Spectrum-X Ethernet networking and is programmable through Nvidia's DOCA software platform. The first rack-scale implementation is the Nvidia CMX context memory storage platform. CMX extends GPU memory with a high-performance context layer designed specifically for storing and retrieving KV cache data generated by large language models during inference. Keeping that cache accessible without forcing a round trip through general-purpose storage is what CMX is designed to do. "Traditional data centers provide high-capacity, general-purpose storage, but generally lack the responsiveness required for interaction with AI agents that need to work across many steps, tools and different sessions," Ian Buck, Nvidia's vice president of hyperscale and high-performance computing said in a briefing with press and analysts. In response to a question from VentureBeat, Buck confirmed that STX also ships with a software reference platform alongside the hardware architecture. Nvidia is expanding DOCA to include a new component referred to in the briefing as DOCA Memo. "Our storage providers can leverage the programmability of the BlueField-4 processor to optimize storage for the agentic AI factory," Buck said. "In addition to having a reference rack architecture, we're also providing a reference software platform for them to deliver those innovations and optimizations for their customers." Storage partners building on STX get both a hardware reference design and a software reference platform — a programmable foundation for context-optimized storage. Nvidia's partner list spans storage incumbents and AI-native cloud providers Storage providers co-designing STX-based infrastructure include Cloudian, DDN, Dell Technologies, Everpure, Hitachi Vantara, HPE, IBM, MinIO, NetApp, Nutanix, VAST Data and WEKA. Manufacturing partners building STX-based systems include AIC, Supermicro and Quanta Cloud Technology. On the cloud and AI side, CoreWeave, Crusoe, IREN, Lambda, Mistral AI, Nebius, Oracle Cloud Infrastructure and Vultr have all committed to STX for context memory storage. That combination of enterprise storage incumbents and AI-native cloud providers is the signal worth watching. Nvidia is not positioning STX as a specialty product for hyperscalers. It is positioning it as the reference standard for anyone building storage infrastructure that has to serve agentic AI workloads — which, within the next two to three years, is likely to include most enterprise AI deployments running multi-step inference at scale. STX-based platforms will be available from partners in the second half of 2026. IBM shows what the data layer problem looks like in production IBM sits on both sides of the STX announcement. It is listed as a storage provider co-designing STX-based infrastructure, and Nvidia separately confirmed that it has selected IBM Storage Scale System 6000 — certified and validated on Nvidia DGX platforms — as the high-performance storage foundation for its own GPU-native analytics infrastructure. IBM also announced a broader expanded collaboration with Nvidia at GTC, including GPU-accelerated integration between IBM's watsonx.data Presto SQL engine and Nvidia's cuDF library. A production proof of concept with Nestlé put numbers on what that acceleration looks like: a data refresh cycle across the company's Order-to-Cash data mart, covering 186 countries and 44 tables, dropped from 15 minutes to three minutes. IBM reported 83% cost savings and a 30x price-performance improvement. The Nestlé result is a structured analytics workload. It does not directly demonstrate agentic inference performance. But it makes IBM and Nvidia's shared argument concrete: the data layer is where enterprise AI performance is currently constrained, and GPU-accelerating it produces material results in production. Why the storage layer is becoming a first-class infrastructure decision STX is a signal that the storage layer is becoming a first-class concern in enterprise AI infrastructure planning, not an afterthought to GPU procurement. General-purpose NAS and object storage were not designed to serve KV cache data at inference latency requirements. STX-based systems from partners including Dell, HPE, NetApp and VAST Data are what Nvidia is putting forward as the practical alternative, with the DOCA software platform providing the programmability layer to tune storage behavior for specific agentic workloads. The performance claims — 5x token throughput, 4x energy efficiency, 2x data ingestion — are measured against traditional CPU-based storage architectures. Nvidia has not specified the exact baseline configuration for those comparisons. Before those numbers drive infrastructure decisions, the baseline is worth pinning down. Platforms are expected from partners in the second half of 2026. Given that most major storage vendors are already co-designing on STX, enterprises evaluating storage refreshes for AI infrastructure in the next 12 months should expect STX-based options to be available from their existing vendor relationships.

• VentureBeat
• 2 gün, 10 saat, 38 dakika

Nvidia on Monday unveiled a deskside supercomputer powerful enough to run AI models with up to one trillion parameters — roughly the scale of GPT-4 — without touching the cloud. The machine, called the DGX Station , packs 748 gigabytes of coherent memory and 20 petaflops of compute into a box that sits next to a monitor, and it may be the most significant personal computing product since the original Mac Pro convinced creative professionals to abandon workstations. The announcement, made at the company's annual GTC conference in San Jose, lands at a moment when the AI industry is grappling with a fundamental tension: the most powerful models in the world require enormous data center infrastructure, but the developers and enterprises building on those models increasingly want to keep their data, their agents, and their intellectual property local. The DGX Station is Nvidia's answer — a six-figure machine that collapses the distance between AI's frontier and a single engineer's desk. What 20 petaflops on your desktop actually means The DGX Station is built around the new GB300 Grace Blackwell Ultra Desktop Superchip , which fuses a 72-core Grace CPU and a Blackwell Ultra GPU through Nvidia's NVLink-C2C interconnect. That link provides 1.8 terabytes per second of coherent bandwidth between the two processors — seven times the speed of PCIe Gen 6 — which means the CPU and GPU share a single, seamless pool of memory without the bottlenecks that typically cripple desktop AI work. Twenty petaflops — 20 quadrillion operations per second — would have ranked this machine among the world's top supercomputers less than a decade ago. The Summit system at Oak Ridge National Laboratory , which held the global No. 1 spot in 2018, delivered roughly ten times that performance but occupied a room the size of two basketball courts. Nvidia is packaging a meaningful fraction of that capability into something that plugs into a wall outlet. The 748 GB of unified memory is arguably the more important number. Trillion-parameter models are enormous neural networks that must be loaded entirely into memory to run. Without sufficient memory, no amount of processing speed matters — the model simply won't fit. The DGX Station clears that bar, and it does so with a coherent architecture that eliminates the latency penalties of shuttling data between CPU and GPU memory pools. Always-on agents need always-on hardware Nvidia designed the DGX Station explicitly for what it sees as the next phase of AI: autonomous agents that reason, plan, write code, and execute tasks continuously — not just systems that respond to prompts. Every major announcement at GTC 2026 reinforced this "agentic AI" thesis, and the DGX Station is where those agents are meant to be built and run. The key pairing is NemoClaw , a new open-source stack that Nvidia also announced Monday. NemoClaw bundles Nvidia's Nemotron open models with OpenShell , a secure runtime that enforces policy-based security, network, and privacy guardrails for autonomous agents. A single command installs the entire stack. Jensen Huang, Nvidia's founder and CEO, framed the combination in unmistakable terms, calling OpenClaw — the broader agent platform NemoClaw supports — "the operating system for personal AI" and comparing it directly to Mac and Windows. The argument is straightforward: cloud instances spin up and down on demand, but always-on agents need persistent compute, persistent memory, and persistent state. A machine under your desk, running 24/7 with local data and local models inside a security sandbox, is architecturally better suited to that workload than a rented GPU in someone else's data center. The DGX Station can operate as a personal supercomputer for a solo developer or as a shared compute node for teams, and it supports air-gapped configurations for classified or regulated environments where data can never leave the building. From desk prototype to data center production in zero rewrites One of the cleverest aspects of the DGX Station's design is what Nvidia calls architectural continuity. Applications built on the machine migrate seamlessly to the company's GB300 NVL72 data center systems — 72-GPU racks designed for hyperscale AI factories — without rearchitecting a single line of code. Nvidia is selling a vertically integrated pipeline: prototype at your desk, then scale to the cloud when you're ready. This matters because the biggest hidden cost in AI development today isn't compute — it's the engineering time lost to rewriting code for different hardware configurations. A model fine-tuned on a local GPU cluster often requires substantial rework to deploy on cloud infrastructure with different memory architectures, networking stacks, and software dependencies. The DGX Station eliminates that friction by running the same NVIDIA AI software stack that powers every tier of Nvidia's infrastructure, from the DGX Spark to the Vera Rubin NVL72. Nvidia also expanded the DGX Spark, the Station's smaller sibling, with new clustering support. Up to four Spark units can now operate as a unified system with near-linear performance scaling — a "desktop data center" that fits on a conference table without rack infrastructure or an IT ticket. For teams that need to fine-tune mid-size models or develop smaller-scale agents, clustered Sparks offer a credible departmental AI platform at a fraction of the Station's cost. The early buyers reveal where the market is heading The initial customer roster for DGX Station maps the industries where AI is transitioning fastest from experiment to daily operating tool. Snowflake is using the system to locally test its open-source Arctic training framework. EPRI , the Electric Power Research Institute, is advancing AI-powered weather forecasting to strengthen electrical grid reliability. Medivis is integrating vision language models into surgical workflows. Microsoft Research and Cornell have deployed the systems for hands-on AI training at scale. Systems are available to order now and will ship in the coming months from ASUS , Dell Technologies , GIGABYTE , MSI , and Supermicro , with HP joining later in the year. Nvidia hasn't disclosed pricing, but the GB300 components and the company's historical DGX pricing suggest a six-figure investment — expensive by workstation standards, but remarkably cheap compared to the cloud GPU costs of running trillion-parameter inference at scale. The list of supported models underscores how open the AI ecosystem has become: developers can run and fine-tune OpenAI's gpt-oss-120b , Google Gemma 3 , Qwen3 , Mistral Large 3 , DeepSeek V3.2 , and Nvidia's own Nemotron models, among others. The DGX Station is model-agnostic by design — a hardware Switzerland in an industry where model allegiances shift quarterly. Nvidia's real strategy: own every layer of the AI stack, from orbit to office The DGX Station didn't arrive in a vacuum. It was one piece of a sweeping set of GTC 2026 announcements that collectively map Nvidia's ambition to supply AI compute at literally every physical scale. At the top, Nvidia unveiled the Vera Rubin platform — seven new chips in full production — anchored by the Vera Rubin NVL72 rack, which integrates 72 next-generation Rubin GPUs and claims up to 10x higher inference throughput per watt compared to the current Blackwell generation. The Vera CPU , with 88 custom Olympus cores, targets the orchestration layer that agentic workloads increasingly demand. At the far frontier, Nvidia announced the Vera Rubin Space Module for orbital data centers, delivering 25x more AI compute for space-based inference than the H100. Between orbit and office, Nvidia revealed partnerships spanning Adobe for creative AI, automakers like BYD and Nissan for Level 4 autonomous vehicles, a coalition with Mistral AI and seven other labs to build open frontier models, and Dynamo 1.0, an open-source inference operating system already adopted by AWS, Azure, Google Cloud, and a roster of AI-native companies including Cursor and Perplexity. The pattern is unmistakable: Nvidia wants to be the computing platform — hardware, software, and models — for every AI workload, everywhere. The DGX Station is the piece that fills the gap between the cloud and the individual. The cloud isn't dead, but its monopoly on serious AI work is ending For the past several years, the default assumption in AI has been that serious work requires cloud GPU instances — renting Nvidia hardware from AWS , Azure , or Google Cloud . That model works, but it carries real costs: data egress fees, latency, security exposure from sending proprietary data to third-party infrastructure, and the fundamental loss of control inherent in renting someone else's computer. The DGX Station doesn't kill the cloud — Nvidia's data center business dwarfs its desktop revenue and is accelerating. But it creates a credible local alternative for an important and growing category of workloads. Training a frontier model from scratch still demands thousands of GPUs in a warehouse. Fine-tuning a trillion-parameter open model on proprietary data? Running inference for an internal agent that processes sensitive documents? Prototyping before committing to cloud spend? A machine under your desk starts to look like the rational choice. This is the strategic elegance of the product: it expands Nvidia's addressable market into personal AI infrastructure while reinforcing the cloud business, because everything built locally is designed to scale up to Nvidia's data center platforms. It's not cloud versus desk. It's cloud and desk, and Nvidia supplies both. A supercomputer on every desk — and an agent that never sleeps on top of it The PC revolution's defining slogan was "a computer on every desk and in every home." Four decades later, Nvidia is updating the premise with an uncomfortable escalation. The DGX Station puts genuine supercomputing power — the kind that ran national laboratories — beside a keyboard, and NemoClaw puts an autonomous AI agent on top of it that runs around the clock, writing code, calling tools, and completing tasks while its owner sleeps. Whether that future is exhilarating or unsettling depends on your vantage point. But one thing is no longer debatable: the infrastructure required to build, run, and own frontier AI just moved from the server room to the desk drawer. And the company that sells nearly every serious AI chip on the planet just made sure it sells the desk drawer, too.

• VentureBeat
• 2 gün, 10 saat, 38 dakika

TechRadar spoke with Kyle Laughlin, SVP of R&D, Technology and Engineering at Walt Disney Imagineering, about how Disney built a walking Olaf robot in just four months — and why it could lead to parks filled with roaming characters.

• TechRadar
• 2 gün, 10 saat, 38 dakika

Formerly known as the Computer Museum of America, Roswell’s Mimms Museum of Technology and Art will officially open “iNSPIRE: 50 Years of Innovation from Apple” to the public on April 1st. Here are the details. more…

• 9to5Mac
• 2 gün, 10 saat, 48 dakika

Apple's premium over-ears finally have a proper successor, but after five years of waiting, it's hard not to wonder: "Is that it?"

• WIRED
• 2 gün, 10 saat, 54 dakika

Sony's upgraded PlayStation Spectral Super Resolution (PSSR) tech is rolling out as part of the PlayStation 5 Pro system update that's available today. The company had teased last month that this update was in the works. These improvements should be a better reflection of why you might pay a premium price for the more powerful console if you value peak image quality in gaming. For a very surface-level definition, PSSR is Sony's upscaling tech. It uses an AI library for a pixel-by-pixel analysis to display a game with better visuals even while running at a lower resolution. Today's update revamped the algorithm and neural networked in use, which in practice means that "image reconstruction is more precise, motion stability is improved, and developers have greater flexibility to balance performance and fidelity on PS5 Pro," according to the latest blog post from the company. For those who want more technical definition, you'll likely be familiar with the folks at Digital Foundry , who have a more detailed analysis with comparisons between the old and new upscaling on four titles. The improved PSSR is only available for supported games, but several familiar Sony partners are already on board. PS5 Pro owners can enable the enhanced PSSR image quality for all supported titles via a toggle in the Screen and Video settings menu. The following games are joining Resident Evil Requiem in offering the better upscaling experience: Silent Hill 2 Silent Hill f Dragon Age: The Veilguard Control Alan Wake 2 Senua’s Saga: Hellblade II Final Fantasy VII Rebirth Nioh 3 Rise of the Ronin Monster Hunter Wilds Dragon’s Dogma 2 This article originally appeared on Engadget at https://www.engadget.com/gaming/playstation/sonys-enhanced-pssr-upscaling-arrives-on-ps5-pro-today-201020423.html?src=rss

• Engadget
• 2 gün, 10 saat, 58 dakika

Carl Franzen / VentureBeat : Z.ai launches GLM-5-Turbo, a closed-source, faster, and cheaper variant of GLM-5 optimized for agent-driven workflows and OpenClaw-style tasks —  Chinese AI startup Z.ai, known for its powerful, open source GLM family of large language models (LLMs), has introduced GLM-5-Turbo, a new …

• Techmeme
• 2 gün, 10 saat, 58 dakika