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ctricity shortage. There are already signs investors are questioning how much money AI infrastructure companies are asking for. Firmus, an Australian data center company backed by Nvidia, withdrew its planned IPO on Oct. 9 after weak investor demand for its marketed share prices, according to Bloomberg. That doesn’t necessarily mean demand for AI computing is weakening. Intel CEO Lip-Bu Tan, who shared the panel with Tan at the conference, said hardware could remain in short supply for another t

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s deal takes $500 off its usual going rate to mark the return of one of its lowest prices since the hikes. It’s one of the more powerful RTX 5070 configs of this notebook, pairing NVIDIA’s graphics with Intel’s Core Ultra 7 356H, 32GB of RAM, and a 1TB SSD. That combination gives it enough headroom for heavier creative work, demanding multitasking, and gaming without turning this into a bulky desktop-replacement. The 16-inch 2880 x 1800 Dynamic AMOLED 2X touchscreen is still one of the biggest d

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omponent to the GPU only while it runs (we didn't benchmark this) Speed End-to-end timings for the distilled checkpoint (16 steps), averaged over three runs after a warmup: Single NVIDIA H100 80GB, no offloading, default attention backend. A reference image roughly doubles the time. Reference tokens join the same sequence as the text and the image being generated, so a 1024 × 1024 reference adds 4,096 tokens to every transformer call The architecture Let's open the model up, one piece at a time.

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taset and GitHub repo. Thanks to the Datalab team for the resources behind this article. Datalab supported and sponsored this content. Asif RazzaqWebsite | + postsBio ⮌ Asif RazzaqNVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model Asif RazzaqTypeSafe AI Releases Jev 1.14 With Images, Box Type and 262k Context RELATED ARTICLESMORE FROM AUTHOR NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model openKylin Outlines Its Vision for Reimagining Linux in the

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foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights. Michal SutterZ.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB RELATED ARTICLESMORE FROM AUTHOR NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model openKylin Outlines Its Vision for Reimagining Linux in the AI Era at Open Source Summit Europe 2026 TypeSafe AI Releases Jev 1.14 With Images, Box Type and 262k Context

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ong-context mode lifts limits to 262k tokens, at a reliability cost. Price holds at $0.042 per million input tokens, with free output. Asif RazzaqWebsite | + postsBio ⮌ Asif RazzaqNVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model RELATED ARTICLESMORE FROM AUTHOR NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model openKylin Outlines Its Vision for Reimagining Linux in the AI Era at Open Source Summit Europe 2026 Meet Grok Voice 3.0: SpaceXAI Voice Age

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ring, Michal excels at transforming complex datasets into actionable insights. Michal SutterMeet Grok Voice 3.0: SpaceXAI Voice Agent Model Ranks 1 on Artificial Analysis Michal SutterZ.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB RELATED ARTICLESMORE FROM AUTHOR NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model TypeSafe AI Releases Jev 1.14 With Images, Box Type and 262k Context Meet Grok Voice 3.0: SpaceXAI Voice Agent Model Ranks 1 on Artificial Analysis

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n learn more, book a demo, or request early access on the CLiF page. References CLiF product page What Is Agent Learning? (CopilotKit) Asif RazzaqWebsite | + postsBio ⮌ Asif RazzaqNVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model Asif RazzaqTypeSafe AI Releases Jev 1.14 With Images, Box Type and 262k Context RELATED ARTICLESMORE FROM AUTHOR NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model openKylin Outlines Its Vision for Reimagining Linux in the

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ing on memory much of the time. Mercury 2 gives them the dense parallel work they were designed for, producing more output per GPU cycle and per unit of energy on widely available NVIDIA GPUs. What's come since: Mercury 2.5, Mercury Voice, Mercury Router, Mercury Decide Mercury 2 launched in February. In September, we followed it with Mercury 2.5, our most capable production model yet. It is a significant step up in quality over Mercury 2, with the same low-latency, low-cost serving profile. Mer

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pted at NeurIPS (White Circle). Results: Human-confirmed nudity bypass rates were 13.33% for DALL-E 3, 9.47% for Nano Banana 2 and 3.27% for GPT-Image-2 (details). Safety funding: NVIDIA committed $250M to UK academic labs and the AI Security Institute for technical safety work (@ rockt). Inference Infrastructure and Systems Research AA-AgentPerf: Artificial Analysis replays agentic coding sessions on DeepSeek V4.1 Flash (AA). NVIDIA results: At 100 tok/s per user, GB300 serves 261M tokens/s per

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ntering its AI era. ( MIT Technology Review ) 5 Smartphone shipments plunged 22% in their worst quarter ever Analysts blamed rising memory costs. ( Bloomberg $) 6 Apple has joined Nvidia in the $5 trillion club The landmark valuation caps Tim Cook’s 15-year run as CEO. ( WSJ $) 7 Microsoft has dismantled a network behind a billion scam messages Japan’s National Police Agency helped investigate the operation. ( Verge ) 8 NASA has new launch targets for its Venus missions DAVINCI is set to take of

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t. Salling Group, Denmark’s largest retailer, is training humanoids to restock supermarket shelves overnight, targeting initial autonomous work in early 2027. Genesis AI confirmed Nvidia as a strategic investor, expanding the chipmaker’s robotics portfolio with its general-purpose robots and underlying AI models. Austin-based ICON raised $200M to scale its robotic 3D-printing construction systems and expand into new U.S. markets. DeepSeek has reportedly agreed to lead a seed investment of tens o

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cosystem and Actuation Modules Hesai is developing Kosmo as an open spatial intelligence ecosystem, offering open access to raw capture data and 3D assets with integrations across NVIDIA Isaac Sim, Isaac Lab, Unreal Engine, RenderMan, and Houdini. The company described two business models: integrated hardware and software solutions for customers in robotics, media, and other industries, and data services that produce real-world spatial data at scale for world model and Physical AI developers. He

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LiLiCorr 投机解码方法在 DGX Spark 上可将词元生成速度最高提升至 2.3 倍。模型还支持 NVFP4 量化，在英伟达 Blackwell 架构上使用 4 位权重和 4 位激活值；在非 Blackwell 平台上，同一检查点使用 4 位权重和 16 位激活值。 英伟达还公布了面向分布式推理的部署优化方案。根据其披露的数据，在使用 16 张 NVIDIA B200 GPU、每名用户每秒生成 50 个词元的测试条件下，NVIDIA Dynamo 配合缓存感知路由、会话亲和性和 KV 缓存卸载，最多可服务 8 倍数量的用户，并将首词元延迟降低 85%。 此次发布还包括基础模型、后训练模型和 NVFP4 量化模型权重，以及训练配方、数据集和强化学习环境。 英伟达表示，Nemotron 3.5 Super VL 在预训练和持续预训练阶段共使用约 26 万亿词元，后训练阶段使用约 380 万个样本，并在数十种环境中进行强化学习。新开放的数据集涵盖智能体工具调用、金融推理、指令遵循、长上下文、公平性和视觉语言理解等领域。 除 Super VL 外，英伟达还推出了两款 Nemotro

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ad released Gemini 4 Argon globally just days earlier. For robotics, the company had already introduced Gemini Robotics-ER 1.6 , a model for spatial perception and task planning. Training in virtual environments isn't new either: the startup World Labs showed how real scenes can be turned into virtual training worlds, while Nvidia's DreamDojo simulates robot actions as videos. What sets Google's approach apart is the ambition to handle simulation, control, and evaluation all with the same model.

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but serving still requires enough GPU memory to hold all 816 billion parameters, so the instance choice comes down to memory capacity. Second, the NVFP4 variant stores weights in NVIDIA’s 4-bit floating point format, which needs roughly a quarter of the memory of the BF16 weights. On SageMaker AI, that difference decides the instance: the NVFP4 variant runs on ml.p5e.48xlarge, and the BF16 variant runs on ml.p6-b300.48xlarge. That makes NVFP4 the variant to try first when you size for cost. Eva

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more information and can publish without approval, while the top tier adds METR’s employee-level access. Thursday’s announcement makes good on a promise Anthropic CEO Dario Amodei made in a September essay , where he proposed embedding outside evaluators inside AI labs as a first step toward slowing the technology’s development. OpenAI quickly pledged the same , and both companies, along with Google, Meta, Nvidia, and xAI, have since signed a White House accord committing to independent review.

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be successful. All this is to say: $\pi 0$ may or may not work for you, but you are welcome to try it and see! Requirements To run the models in this repository, you will need an NVIDIA GPU with at least the following specifications. These estimations assume a single GPU, but you can also use multiple GPUs with model parallelism to reduce per-GPU memory requirements by configuring fsdp devices in the training config. Please also note that the current training script does not yet support multi-n

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el checkpoint, and a fine-tuned checkpoint are now available. [2026-4] Fine-tuning code released! Directory Structure Installation & Setup System Requirements Hardware : Tested on NVIDIA A100 80GB GPU. Software : Python 3.10+. Installation Options Option 1: uv (recommended) For inference only: For training: Option 2: pip For inference only: For training: Note : flash-attn requires matching CUDA and PyTorch versions. If installation fails, refer to the flash-attn installation guide. Dependency Fi

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approach is to do all your video decoding and compositing on a cloud GPU and stream the final result down to the user’s computer. This is the same technique used for services like NVIDIA's to stream games to your computer and requires a dedicated computer in the cloud for every active user of your product. One noteworthy limitation of this approach is that the file needs to be uploaded before the user can see it. No instant gratification! Enter WebCodecs As part of Interop 2023, web browsers sta

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