Search: “geforce-now”

30 stories

DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll

SeverityAll critical 0 high 0 medium 0 low 0 info 0

Smart search ranks by meaning as well as keywords (one row per story, last 45 days).

Class Is in Session: GeForce NOW Levels Up Linux, Chromebooks and More

NVIDIA's GeForce NOW cloud gaming service ships its native Linux app out of beta and adds streaming optimizations for Frame Generation responsiveness.

NVIDIA announced that the native Linux application for its GeForce NOW cloud gaming service is officially out of beta. The update also delivers cloud optimizations that make Frame Generation feel more responsive while streaming, and Performance members will see higher frame rates. The announcement is timed for the back-to-school season and also mentions improvements for Chromebooks.

NVIDIA BlogAug 13, 13:00 UTC · Aug 13, 2026AI industry

GeForce NOW Gives Gamers More Ways to Play at Gamescom 2026

NVIDIA announced Gamescom 2026 GeForce NOW updates: DLSS 4.5 controls, new Steam device support, GOG single sign-on, Firefox access, and more cloud games.

At Gamescom 2026, NVIDIA announced expanded support for its GeForce NOW cloud gaming service, including new Steam devices, GOG single sign-on, Firefox browser access, and more big PC games coming to the cloud. The company also introduced new NVIDIA DLSS 4.5 technology controls that let members fine-tune gameplay. The announcements are consumer gaming product news rather than security or research developments.

NVIDIA BlogAug 27, 13:00 UTC · 20d agoAI industry

Leading Publishers Bring Blockbuster PC Games and Technology to NVIDIA RTX Spark

NVIDIA announces EA, Embark, and Ubisoft titles plus anti-cheat and visual upgrades for RTX Spark ahead of launch at Gamescom.

NVIDIA is promoting RTX Spark at Gamescom in Cologne, Germany, with Electronic Arts, Embark, and Ubisoft bringing blockbuster PC titles to the platform ahead of its launch. The announcement covers new game support, anti-cheat technologies, and increased visual quality. The piece is a product-marketing update rather than security research or AI safety news.

NVIDIA BlogAug 25, 15:30 UTC · 22d agoAI industry

Building a Linux GPU Driver for the M4 Mac Mini in One Month

Two developers built a fully OpenGL ES 3.0 compliant Linux GPU driver for the M4 Mac Mini in one month via clean-room reverse engineering.

Niklas and the author reverse engineered Apple's AGX GPU firmware ABI and user-space components in about a month, a process that normally takes years, producing an OpenGL ES 3.0 conformant driver fast enough to run Minecraft at 200fps on an M4 Mac Mini. The work was done transparently using hypervisor traces without examining Apple binaries, following clean-room practices, and included a custom shader compiler, command stream builder, and a full Linux kernel driver for the firmware ABI. The A18 Pro firmware ABI proved significantly more complex than the M1's, with 1.5x as many structs and twice as many pointers. All experiments and provenance evidence were published in public agx-re repositories.

Hacker News · AISep 15, 19:30 UTC · 1d agoResearch HN 40↑ · 5 comments

Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026

NVIDIA announces local AI push at IFA 2026 with faster llama.cpp/vLLM inference, PAIR routing tool, and October RTX Spark PCs.

At IFA 2026, NVIDIA announced simplified local AI support for agents in Hermes Agent, OpenClaw, and Perplexity Portable Computer, plus new llama.cpp and vLLM optimizations delivering up to 1.9x faster local inference. NVIDIA also unveiled PAIR, a Personal AI Router for distributing inference across a local network's PCs, and compact RTX Spark Windows PCs from Lenovo and Acer arriving in October. The post recaps recent local-capable model releases including Nemotron 3.5 Lightning (30B), Qwen3.8-Flash-Next and Qwen3.8-27B, DeepSeek v4 Flash (284B MoE, 13B active), Meta Muse Glimmer (30B), Z.ai GLM-5.3-Flash, LTX 2.5, and MiniMax-H3 with the FastH3 distilled variant.

NVIDIA BlogSep 3, 16:00 UTC · 13d agoAI industry

How XPUs Meet a World-Class AI Factory

NVIDIA argues AI factories with custom XPUs and NVLink Fusion connectivity must optimize tokens-per-second, tokens-per-watt, cost and uptime.

NVIDIA published a blog explaining that AI factories running continuously are economically defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. It argues hyperscalers and AI-native companies building custom XPUs need infrastructure designed as a complete factory rather than collections of individual accelerators. The piece promotes NVIDIA's NVLink Fusion and full-stack XPU connectivity as the foundation for such world-class AI factory builds.

NVIDIA BlogAug 24, 15:00 UTC · 23d agoAI industry

Arm Mali G2-Ultra NX GPU: desktop-class mobile gameplay with AI-native graphics

Arm unveiled Mali G2-Ultra NX, its first AI-native mobile GPU with in-shader neural acceleration, third-gen ray tracing, and up to 24% higher benchmark performance.

Arm announced the Mali G2-Ultra NX, the first AI-native Mali GPU, integrating neural accelerators directly into shader cores alongside a new execution engine and third-generation hardware ray tracing. It introduces Neural Super Sampling (NSS), Neural Frame Rate Upscaling (NFRU), and Neural Super Sampling and Denoising (NSSD); the Neural Dawn demo with Sumo Digital showed up to 4x performance efficiency and 70% lower external memory traffic versus native rendering. Arm claims up to 24% higher benchmark performance, 13% lower DRAM traffic on ray tracing benchmarks, and up to 120 FPS with NFRU. Over 14 billion Mali GPUs have shipped to date.

Hacker News · AISep 8, 04:06 UTC · 8d agoAI industry HN 71↑ · 53 comments1

nvidia/Qwen3.8-Flash-Next-NVFP4 — new model trending #28 on Hugging Face

NVIDIA released an NVFP4 4-bit quantized build of Alibaba's Qwen3.8-Flash-Next, a 125B-parameter MoE vision-language model, via Model Optimizer.

The checkpoint quantizes Qwen3.8-Flash-Next — a hybrid-attention (Gated DeltaNet and Qwen Sparse Attention) Mixture-of-Experts model with 125B total and 6B activated parameters, plus 51B n-gram embeddings and 4B MTP — using NVIDIA Model Optimizer v0.46.0. NVFP4 benchmarks stay close to FP8: GPQA Diamond 91.5 vs 92.0, MMMU Pro 78.3 vs 77.1, Terminal-Bench 2.1 82.9 vs 83.3. It targets Blackwell B200/B300 GPUs, runs on vLLM, supports 262K context extendable to 1M tokens, and is licensed under the NVIDIA Open Model License with Qwen Community License 1.0.

Hugging Face trending modelsSep 2, 04:03 UTC · 14d agoModel release

Chipmaker Patch Tuesday: Nvidia, AMD, Arm Issue Security Advisories

AMD, Arm, and Nvidia issued Patch Tuesday advisories fixing a Linux GPU driver DoS flaw, nine Mali GPU vulnerabilities, and two high-severity Triton defects.

AMD fixed CVE-2026-43603, a NULL pointer dereference in its Linux GPU kernel driver that can crash systems and cause denial-of-service, credited to SecMate researchers, with patches for EPYC, Ryzen, Radeon, and Instinct shipped in July and embedded variants due in October. Arm published an advisory covering nine Mali GPU vulnerabilities allowing use-after-free access, kernel information leaks, or DoS, releasing fixes for Valhall and 5th Gen GPU Architecture drivers, with Bifrost also affected. Nvidia updated Triton Inference Server for Linux to resolve two high-severity flaws, one causing DoS and one enabling information disclosure, data tampering, and DoS. Intel had issued no new advisories since the previous Patch Tuesday.

SecurityWeekSep 9, 16:22 UTC · 7d agoAdvisory CVE-2026-43603

Towards Standardized Evaluation of GPU Memory Safety with GMSBench

GMSBench provides 149 CUDA tests covering spatial, temporal, and concurrency GPU memory errors, exposing detection gaps in Compute Sanitizer.

GMSBench is a GPU memory safety benchmark comprising 149 self-contained CUDA tests spanning spatial, temporal, and concurrency errors across different GPU memory spaces and execution scenarios. The authors evaluate NVIDIA's Compute Sanitizer across multiple GPU architectures using the suite, exposing gaps in its detection coverage. The benchmark offers a standardized foundation for comparative evaluation of GPU memory safety mechanisms.

arXiv cs.CRSep 8, 15:11 UTC · 8d agoResearch

Salesforce and Nvidia’s new reasoning model is everything the AI labs should fear

Salesforce and Nvidia launch Koa, Salesforce's first reasoning model, built on Nvidia's open-weight Nemotron and post-trained on synthetic sales and support data.

Salesforce announced Koa at Dreamforce, its first reasoning model, built on Nvidia's open-weight Nemotron and post-trained with synthetic data mimicking sales and customer-support scenarios rather than real customer data. Koa will be offered through the Agentforce platform's AI gateway as a cheaper, token-efficient alternative to closed frontier models like Claude and ChatGPT for enterprise tasks. Salesforce simultaneously announced a ClaudeForce partnership with Anthropic keeping customer data inside Salesforce's infrastructure.

TechCrunch · AISep 15, 12:00 UTC · 1d agoModel release1

GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay

GPU-CFR compiles counterfactual regret minimization into static dataflow with CUDA Graph Replay, achieving 29.8-80.4x speedups over prior GPU solvers.

The paper presents a compiler and runtime that turns any fixed game's CFR iteration into a static dataflow graph of flat arrays and precomputed indices, cutting framework operations by up to 18.1x. Because shapes and buffer addresses never change, CUDA Graph Replay records the iteration once and replays it with a single launch. On one A100 across an eight-game suite, GPU-CFR runs 29.8-80.4x faster than the fastest prior GPU CFR and 14-258x faster than the CPU implementation LiteEFG on the four largest games, while reproducing reference iterates bitwise on CPU.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:58 UTC · 6d agoAI research3· 1 read

Apple’s Ternus era begins as Nvidia bets on the whole AI stack

Apple's John Ternus becomes CEO as Tim Cook steps down; Nvidia expands across the AI stack while a16z launches a $1.1B Machine Age fund.

Tim Cook stepped down as Apple CEO, handing the company to former hardware chief John Ternus, with Cook staying as Executive Chairman focused on policy relationships. TechCrunch's Equity podcast also unpacks Nvidia's moves to own the entire AI stack, including its Hugging Face acquisition, a MediaTek investment, and deeper compute deals. Other items include robotaxi competition (Tesla Cybercab, Waymo expansion, Zoox paid rides), Andreessen Horowitz's new $1.1 billion 'Machine Age' fund, and the $285 million GoPro acquisition.

TechCrunch · AISep 4, 16:04 UTC · 12d agoAI industry

[AINews] NVIDIA buys HuggingFace for $13B, as OpenAI publishes their HF incident retro

Z.ai released open-weight GLM-5.3-Flash (320B/18B active, 1M context, MIT) while Nvidia confirmed buying Hugging Face for $13B.

Z.ai formally launched GLM-5.3-Flash, the model previously previewed as Ox Alpha: 320B total parameters with 18B active, a 1M-token context window, natively multimodal, MIT-licensed, and claimed on par with Claude Opus 4.8 on coding. Artificial Analysis scored it 57 on its Intelligence Index at $0.09 per task, roughly 7.5x cheaper than GLM-5.3, and it scored 84.3% on Terminal-Bench 2.1. Nvidia's $13B acquisition of Hugging Face (~80x its $150M ARR) was confirmed, nearly double its initial $7B January offer. The roundup also notes Qwen shipping an impressive Flash model on Chinese chips as part of a broader open-model narrative.

Latent SpaceAug 27, 01:50 UTC · 20d agoModel release1

Teaching Everyone to Fish for Tokens

Analysis argues open-source AI now depends heavily on Nvidia's financing, with a reported $26 billion bet shaping the open-weights ecosystem's future.

An Interconnects essay examines whether the open-source model recipe, exemplified by Ai2's Olmo and Nvidia's Nemotron releases, can become economically self-sustaining. It reports Nvidia is spending roughly $26 billion on near-open-source models to drive demand for its chips, and argues the open ecosystem faces an existential financing window over the next few years. The author predicts open models may fork toward efficiency, specialization, and on-prem enterprise agents rather than competing head-on with closed frontier labs.

InterconnectsAug 17, 15:07 UTC · Aug 17, 2026AI industry

Miles v0.1: Production-Level Post-Training

Radix Ark open-sources Miles v0.1, a full-stack RL post-training framework demonstrated with asynchronous agentic RL on GLM-5.2 744B-A40B across 64 GB300 GPUs.

Miles v0.1 is a full-stack, open-source system for frontier-scale reinforcement-learning post-training, built on slime with rollout engines on SGLang and trainers supporting NVIDIA Megatron-LM and PyTorch FSDP backends plus three weight-synchronization transports. It supports full-parameter RL, LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends to diffusion models. The end-to-end case study ran fully asynchronous agentic RL on GLM-5.2 744B-A40B for terminal-use coding tasks on 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps. The code is open-sourced on GitHub.

Hugging Face daily papersSep 7, 20:00 UTC · 9d agoAI tools & infra

Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI

Hugging Face released @huggingface/kernels, a library offering 200+ WebGPU compute kernels to accelerate AI inference locally in browsers.

Hugging Face introduced the @huggingface/kernels package, bundling more than 200 optimized WebGPU compute kernels for running AI workloads locally. The release targets browser-based and on-device inference, reducing reliance on server-side compute. No article body was available beyond the title, so benchmark results and supported models are not specified.

Hugging Face BlogSep 1, 00:00 UTC · 15d agoAI tools & infra

Securing the Infrastructure of Intelligence

NVIDIA positions AI factories combining chips, networking, power and data as the defining infrastructure of the AI economy needing full-stack security.

NVIDIA's blog argues that AI factories are the defining infrastructure of the AI era, transforming energy and data into intelligence that powers businesses and countries. It frames compute as revenue and lists the full stack of critical resources required: advanced chips, packaging, memory, networking, land and power. The piece is a corporate positioning article about securing this infrastructure, with no specific incident or product announcement detailed in the excerpt.

NVIDIA BlogAug 17, 12:34 UTC · Aug 17, 2026AI industry

Jensen Huang took a call from Trump, and showed off something else, too

At the All-In conference, Nvidia's Jensen Huang took a live Trump call dismissing AI safety fears while flaunting Apple's unreleased $1,999 foldable phone.

At the All-In conference in L.A., Nvidia CEO Jensen Huang answered a live on-stage call from President Trump using Apple's unreleased foldable phone, which hits stores in five weeks at $1,999. During the call, Trump and Huang agreed that fears about AI safety amount to what Trump called a 'hoax,' with Trump claiming the AI panic benefits China and 'political people' and arguing the technology is 'bigger than the internet.' Nvidia stock is up 33% over the past year but fell a few percentage points on the day. The article notes Apple now depends on Nvidia hardware to run part of Apple Intelligence, a decade after dropping Nvidia chips from its devices.

TechCrunch · AISep 15, 01:50 UTC · 1d agoAI industry

NVIDIA AI Factory Compute Is Becoming an Investable Asset Class

NVIDIA partners with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 billion of third-party capital for AI infrastructure financing.

NVIDIA announced partnerships with major financial firms including Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. The independent financing platforms are designed to mobilize more than $500 billion of third-party capital to support AI infrastructure buildout. NVIDIA frames the move as positioning AI factory compute as an investable asset class.

NVIDIA BlogAug 12, 00:38 UTC · Aug 12, 2026AI industry

Nvidia CEO Jensen Huang tells Trump ‘we’re not going to let [an AI slowdown] happen’

Nvidia CEO Jensen Huang told Trump 'we're not going to let an AI slowdown happen,' rejecting Amodei's call to slow AI capability gains.

During the All-In Summit, Nvidia CEO Jensen Huang took a live speakerphone call from President Donald Trump and agreed 'we're not going to let' an AI slowdown happen, countering Anthropic CEO Dario Amodei's call to slow AI capability improvements, which Elon Musk and Sam Altman have endorsed. Trump called the slowdown sentiment 'a hoax' that could benefit political actors or China. Gallup polling shows seven in 10 Americans oppose local data center construction, with over 50% citing environmental resource concerns.

TechCrunch · AISep 14, 21:51 UTC · 2d agoAI industry

NVIDIA NVLink Fusion Expands With NVHBM Custom High-Bandwidth Memory

NVIDIA expands NVLink Fusion with NVHBM custom high-bandwidth memory for hyperscalers building trillion-parameter and agentic AI infrastructure.

NVIDIA announced that NVLink Fusion will expand to support NVHBM, a custom high-bandwidth memory option. The offering targets hyperscalers and AI innovators building next-generation systems where agentic AI and trillion-parameter workloads are mainstream. NVIDIA frames the announcement around co-designing compute, memory, storage, networking, and software as a unified system.

NVIDIA BlogAug 26, 21:05 UTC · 21d agoAI industry

d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment

d-Matrix will integrate its Raptor inference XPUs with NVIDIA NVLink Fusion, MGX racks and Spectrum-X networking for rack-scale AI factory deployment.

Inference chipmaker d-Matrix announced adoption of NVIDIA NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA's scale-up and scale-out networking, MGX rack architecture, and broader AI factory platform. NVIDIA claims 3x lower XPU-to-XPU latency than off-the-shelf Ethernet and 3 TB/s per-XPU all-to-all bandwidth via sixth-generation NVLink. d-Matrix plans to integrate Vera CPUs, ConnectX-9 SuperNICs, BlueField-4 DPUs and Spectrum-X Ethernet, with racks able to work alongside Vera Rubin NVL72 GPU systems. Other NVLink Fusion partners include AWS, Arm, Intel, Fujitsu, Marvell, MediaTek, Samsung and Cadence.

NVIDIA Blogupdated 15:00 UTC · 7h agofirst Sep 10, 13:00 UTC · 6d agoAI industry 2 sources

Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licenses

Interconnects surveys new open models—Motif-3, GLM-5.3, Hy4-preview—while analyzing a licensing split: Western labs opening up, Chinese frontier labs getting restrictive.

The roundup covers Motif-3 (MIT license, strong scores for its size), GLM-5.3 (switched from MIT to a custom license with a $10 billion revenue threshold and undefined 'affiliates' clause requiring Z.AI security review), and Tencent's Hy4-preview (competent but prone to overthinking). It also notes dots3-note-prev from RedNote/Xiaohongshu (won IMO 2026 with a perfect score), Qwen3.8-Flash-Next (125B-A6B with GDN and Qwen Sparse Attention), NVIDIA Nemotron-3.5-Lightning-30B-A3B-BF16, and Ling-3.0-flash. The core theme: Google and Meta adopted Apache 2.0 while Chinese frontier labs (Zhipu, Kimi K3, MiniMax M3) adopted restrictive commercial licenses.

InterconnectsSep 8, 14:15 UTC · 8d agoAI research

Why Scaling AI Compute Performance Requires a New Power Architecture

NVIDIA argues AI factories need 800 VDC power distribution as dense GPU racks outgrow traditional AC-based delivery.

NVIDIA's blog contends each generation of accelerated computing demands higher rack density and more efficient, scalable power distribution. It frames the bottleneck as how power moves from the grid to the GPU rather than raw wattage, and describes limitations of traditional AC power delivery. NVIDIA advocates a new 800 VDC power architecture for AI factories.

NVIDIA BlogAug 11, 15:00 UTC · Aug 11, 2026AI industry

NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100

NVIDIA released BioNeMo Inference Runtime (BioIR), an open-source PyTorch-compatible library delivering 2.90x higher Boltz-2 protein-folding throughput on 8xH100 GPUs.

NVIDIA detailed BioIR, a Python library that accelerates Boltz-2, OpenFold2, and OpenFold3 structure-prediction inference on NVIDIA GPUs while preserving standard PyTorch workflows. On a matched benchmark of 1,000 human dimers on 8xH100 80GB GPUs, BioIR delivered 58.5K folded residues per GPU-hour versus 20.2K for a torch.compile baseline, a 2.90x throughput gain. BioIR already powered the AlphaFold Database expansion, generating about 31 million candidate complexes across 4,777 proteomes, with 1.81 million released as high-confidence predictions. Extrapolated to 1 million targets, estimated folding energy drops from 35 MWh to 11 MWh at 8-GPU TDP equivalents.

MarkTechPostSep 10, 21:57 UTC · 6d agoAI tools & infra1

Apple might make servers again to cash in on the AI rush

Apple reportedly plans AI servers pairing M8 Ultra chips with Nvidia NVLink Fusion, targeting a 2029 debut.

According to The Information, Apple is considering re-entering the server market it left in 2011 with the Xserve retirement, betting on its ARM-based M processors amid surging AI compute demand. The servers may run two or four M8 Ultra chips and could incorporate Nvidia's NVLink Fusion interconnect, and Apple recently turned to Nvidia chips for its revamped Siri servers. A product would likely debut around 2029, and plans could still change; Mac Mini and Mac Studio popularity with AI developers has already caused shortages.

The Verge · AIupdated 22:02 UTC · 21m agofirst 17:20 UTC · 5h agoAI industry 3 sources

NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing

NVIDIA open-sourced OSMO, a Kubernetes-native YAML orchestrator running physical-AI training, simulation, and robot testing across mixed GPU tiers.

OSMO (Apache-2.0, latest release 6.3.1) lets teams describe training, simulation, and hardware-in-the-loop pipelines in a single YAML and routes tasks across datacenter GPUs (GB200), workstation RTX hardware, and edge devices like Jetson AGX Thor. It ships Helm charts and containers on NGC, uses the KAI Scheduler with NVLink topology-aware placement, and includes RBAC, OAuth2, and TLS termination. NVIDIA says it is battle-tested on GR00T, Isaac Lab, Isaac Sim, and Isaac ROS, and integrates with Claude Code, OpenAI Codex, and Cursor agents.

MarkTechPostSep 14, 08:04 UTC · 2d agoAI tools & infra

NVIDIA security advisory (AV26-900)

Canada's Cyber Centre flags NVIDIA Triton Inference Server versions through 26.03 and 26.06 as vulnerable, urging review of the September 2026 bulletin.

The Canadian Centre for Cyber Security issued advisory AV26-900 on September 9, 2026, noting that as of September 8, NVIDIA Triton Inference Server versions 0.0 to 26.03 and 0.0 to 26.06 are affected by vulnerabilities. The advisory includes no CVE identifiers or severity details and directs users to review NVIDIA's September 2026 Triton security bulletin and apply available updates.

Canadian Centre for Cyber SecuritySep 9, 15:47 UTC · 7d agoAdvisory

From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production

NVIDIA detailed DSX power-management results: Lambda gained 24% token throughput at fixed power, and an AI factory auto-shed 1MW via Emerald AI's grid program.

NVIDIA says Lambda's first validation of DSX MaxLPS on HGX B200 servers ran 19 nodes within a 16-node power budget, lifting cluster token throughput 24% (roughly 4M to 5M tokens/second) and improving performance per watt by 23%. NVIDIA projects DSX MaxLPS can enable up to 40% more GPU capacity for Vera Rubin NVL72 factories within the same megawatt budget. Emerald AI's Conductor platform, running at NVIDIA's Eos factory with Silicon Valley Power, responded to over 200 utility demand signals, automatically dropping power from 4MW to 3MW without interrupting priority workloads. The first dedicated DSX Flex commercial deployment is planned at a 96-megawatt Manassas, Virginia facility.

NVIDIA BlogSep 15, 16:55 UTC · 1d agoAI industry