标签：#nvidia

Eager to find more public AI-related companies that may do as well as Nvidia, Wall Street investors think they've found a winner with Micron.

Public pushback against data centers has emphasized their water and energy consumption, and now Nvidia is highlighting its claim that the Rubin generation reference design for a fully liquid-cooled data center has "eliminated massive amounts of power usage and pretty much all water usage." Still, it doesn't address all of the concerns around AI data […]

Nvidia announced a new cooling system that cuts water use inside the data center. But it does nothing to address AI's biggest water use — fossil fuel power plants.

Nvidia's Nader Khalil — Director of Developer Technologies and co-founder of Brev.dev, acquired by Nvidia two years ago — sat down with The New Stack to talk agents, OpenClaw, and where enterprise AI is heading. His opening line is worth keeping: "An agent is an LLM and a harness. And if you think about that, it involves two things. It involves the loop and the LLM… Each loop should take us closer to our goal." That's not a complicated definition. It's also exactly right — and the fact that Nvidia's internal framing lands here matters more than the quote itself. What actually happened Nvidia has full-time OpenClaw contributors. Khalil: "We have a couple of developers at the company that contribute to OpenClaw full time." That's a real commitment, not a press-release mention. NemoClaw is their enterprise blueprint — a reference architecture for running OpenClaw (and Hermes) in production, with GPU routing, security policies, and a runtime called OpenShell. Khalil traces the harness evolution directly: from ChatGPT's system prompts → memory → file context → Cursor → Claude Code. All of it is harness, not model. The model is constant; the harness is where the product lives. On OpenClaw's PR backlog: "It got more stars than Linux in months… so I think you're gonna see a mountain of PRs." Their response — roll up their sleeves and start merging. Why this framing matters Nvidia makes money when AI compute scales. For that to happen, agents need to work reliably in enterprise environments — and the harness is the reliability layer. Their NemoClaw blueprints aren't a product play; they're an enablement play. Enterprise teams get a reference architecture that works on Nvidia silicon. Nvidia gets demand for the GPUs underneath. It's the CUDA X model applied to agentic AI. The microwave analogy Khalil uses is useful: "when it's your microwave at home, you just go 'Boop, boop. Done.'" Every enterprise will build specialized agents tuned to their domain — CrowdStrike, Cadence, P

nvidia-peermem "Invalid argument" on Ubuntu — Fix GPUDirect RDMA with DMA-BUF TL;DR: If modprobe nvidia-peermem fails with Invalid argument ( -EINVAL ) on a system using the inbox Ubuntu InfiniBand stack ( rdma-core ), the module is not broken and you do not need it. nvidia-peermem requires an API that only exists in MLNX_OFED. On Hopper/Blackwell GPUs with the NVIDIA open driver, use DMA-BUF instead — it does GPUDirect RDMA natively. The one gotcha: you must enable nvidia-drm modeset=1 . Applies to: Ubuntu 22.04 / 24.04, inbox rdma-core stack, NVIDIA open kernel driver, H100 / H200 / B200, ConnectX-6/7 (or any HCA with ODP support). The symptom $ sudo modprobe nvidia-peermem modprobe: ERROR: could not insert 'nvidia_peermem' : Invalid argument dmesg shows nvidia-peermem loaded but registered nothing, or the load returns -EINVAL . GPUDirect RDMA appears to be unavailable. Why this happens (and why it is not a bug) nvidia-peermem is the legacy path for GPUDirect RDMA. It registers GPU memory with the InfiniBand subsystem through a Mellanox-proprietary kernel API: ib_register_peer_memory_client () That symbol only exists in MLNX_OFED's build of ib_core . It is not in the mainline kernel, and it is not in rdma-core , which is the inbox InfiniBand stack on Ubuntu. If you are on the inbox stack, nvidia-peermem was compiled without that API present, so it can never bind and always returns Invalid argument . No module parameter or config change will fix it, because the thing it needs was never there. Do not install MLNX_OFED just to make nvidia-peermem load. That works, but it is the wrong fix — you would be adding a heavy proprietary stack to revive an obsolete module. There is a native path already in your kernel. The fix: use DMA-BUF On Hopper and newer with the open driver, GPUDirect RDMA works through DMA-BUF , a mainline Linux framework. No external module, no MLNX_OFED. Requirements (check these first) NVIDIA open kernel driver (not the proprietary build) nvidia-drm

Nvidia's self-improvement program for robots enlists teams of AI coding agents.

Debt sale set to test investor appetite for further exposure to AI sector amid a deluge of borrowing.

CUDA for AMD Lemonade, Intel Arc Pro Linux Gains, XPU Manager 2.0 Today's Highlights Today's top GPU news highlights include AMD's Lemonade SDK gaining NVIDIA CUDA support, significant performance improvements for Intel Arc Pro GPUs on Linux 7.1, and the major 2.0 overhaul of Intel's XPU Manager for better GPU management on both Windows and Linux. AMD's Lemonade SDK For Local AI Adds NVIDIA CUDA Support (Phoronix) Source: https://www.phoronix.com/news/AMD-Lemonade-10.7-Released AMD has released a new version of its Lemonade SDK, a powerful local AI server solution designed to leverage AMD's diverse hardware ecosystem, including their CPUs, GPUs, and NPUs. The most significant update in this release is the addition of NVIDIA CUDA support. This integration allows developers to utilize NVIDIA GPUs within their Lemonade-powered local AI deployments, bridging a critical gap in cross-platform AI development. The inclusion of CUDA support is a strategic move, enabling Lemonade to tap into NVIDIA's extensive CUDA ecosystem and a vast array of pre-optimized models and libraries. This means that applications built with Lemonade can now seamlessly target a wider range of hardware, offering unprecedented flexibility for developers working with local AI. For users, it provides the choice to deploy their AI models on either AMD or NVIDIA hardware using a single, unified SDK, expanding the potential reach and efficiency of their AI workloads. Comment: This is a massive step for cross-vendor AI development. Being able to use AMD's Lemonade SDK to deploy local AI models and then seamlessly target NVIDIA GPUs via CUDA truly unifies the AI backend landscape for diverse hardware setups, making it incredibly practical for hybrid environments. Intel Arc Pro B70 Showing Off Some Performance Wins With Linux 7.1 (Phoronix) Source: https://www.phoronix.com/review/linux-71-arc-pro-b70 Recent testing by Phoronix indicates that Intel's Arc Pro B70 discrete GPUs are demonstrating notable perform

In 2025 Google Cloud added G4 , powered by NVIDIA's RTX PRO 6000 Blackwell Server Edition GPUs to their offering, allowing them to offer hardware not only for AI applications, but also for other applications, such as rendering, simulations or gaming. A single G4 instance with one accelerator ( g4-standard-48 ) comes equipped with 48 CPU cores, 180 gigabytes of RAM and 96 gigabytes of GPU memory. This is a lot of resources for a single cloud workstation, that only the most demanding workstreams would utilize. Most professionals who require a graphics accelerator to do their job, don't really need this much compute power for day to day tasks. It wasn't financially reasonable to pay for a G4 instance, when you weren't utilizing all the resources you paid for. If only there were smaller machine types… If only you could share that one very powerful GPU between multiple virtual machines… Introducing fractional VMs! During Google Cloud Next 2026, Google announced GA for fractional G4 VMs and was the first provider to bring vGPU functionality to RTX PRO 6000 accelerators. vGPU stands for virtual graphical processing unit . Just like VMs (virtual machines) are a way to split one physical computer into smaller, independent systems, vGPU allows for a single physical accelerator to be split into 2, 4 or 8 virtual accelerators! The new fractional machine types ( g4-standard-24 , g4-standard-12 , g4-standard-6 ) now allow you to perfectly match the compute capabilities to your needs! Who is it for? The existence of those new machine types makes it much more cost-efficient to move many GPU-dependent tasks to the cloud. Replacing physical workstations in offices with cloud infrastructure is not a new thing , but till now, Google Cloud didn't offer a good platform for those who needed workstations to process images, post-process videos, simulate physics or render 3D graphics. Those users now can get exactly the hardware they need, allowing their companies to move away from maintaini

Vortex 3.0 RISC-V GPGPU, Pragtical SDL GPU Backend, NVIDIA RTX Spark Launch Today's Highlights Today's top stories highlight significant advancements in open-source GPU hardware with Vortex 3.0 adding a 3D pipeline and a lightweight code editor, Pragtical, leveraging an SDL GPU backend for UI rendering. NVIDIA also unveiled RTX Spark, a new 'superchip' aimed at bringing personal AI agents to Windows PCs with accelerated on-device processing. Vortex 3.0 Released As Full-Stack, Open-Source RISC-V GPU Now With 3D Pipeline (Phoronix) Source: https://www.phoronix.com/news/Vortex-3.0-RISC-V-GPGPU Vortex, an open-source, OpenCL-compatible RISC-V GPGPU implementation developed by Georgia Tech, has released its next major version, 3.0. This significant update introduces a full 3D rendering pipeline, marking a crucial evolution from its previous focus solely on general-purpose GPU (GPGPU) compute. The expansion into 3D graphics capabilities makes Vortex a more comprehensive open-source GPU solution, enabling it to handle a wider range of visual and computational tasks. As an open-source hardware design, Vortex 3.0 provides developers, researchers, and hardware enthusiasts with unparalleled access to study, modify, and implement its architecture. Its OpenCL compatibility ensures that it can leverage existing GPGPU codebases, fostering experimentation with RISC-V-based GPU development, custom hardware accelerators, and exploring alternative GPU instruction sets and architectures. This release allows for deeper exploration into the integration of compute and graphics within an open framework. This development is pivotal for the open-source hardware and RISC-V ecosystems. It underscores the growing maturity of RISC-V for demanding compute and graphics workloads, offering a royalty-free alternative to proprietary GPU designs. The inclusion of a 3D pipeline extends its utility beyond just general-purpose compute to full graphics rendering, potentially impacting future embedded syst

Linux 7.1 Boosts Intel Arc, Flatpak Integrates ROCm, Vintage AMD Driver Refined Today's Highlights Recent developments enhance GPU performance and accessibility, with the Linux 7.1 kernel providing significant gains for Intel Arc Battlemage graphics. AMD's ROCm compute platform gains broader deployment potential through Flatpak 1.18 integration, while an older AMD GPU driver sees notable code cleanups. Linux 7.1 Helping Intel Arc Battlemage Graphics Achieve Better Performance (Phoronix) Source: https://www.phoronix.com/review/intel-b580-linux-71 Phoronix reports that the upcoming Linux 7.1 kernel release is delivering superior graphics performance for Intel's Arc B580 Battlemage desktop graphics card compared to the current stable Linux 7.0. This indicates ongoing, critical optimization work within the open-source Linux graphics stack, directly impacting the gaming and compute capabilities of Intel's latest GPU architecture. Such kernel-level improvements are vital for unlocking the full potential of new hardware on Linux platforms, ensuring users receive the best possible experience from their Intel Arc GPUs. The performance uplift suggests that deeper integration and fine-tuning of the kernel's display and compute drivers are progressing, addressing potential bottlenecks and enhancing throughput. For users and developers leveraging Intel Arc GPUs on Linux, this kernel update is a significant milestone, promising more stable and efficient operation for various workloads, from gaming to professional applications. It highlights the dynamic nature of Linux driver development, where continuous collaboration leads to tangible performance benefits even before major hardware refreshes. Comment: This shows how crucial kernel updates are for modern GPUs on Linux. Early adopters of Arc Battlemage should definitely keep an eye on Linux 7.1 for a noticeable performance bump. Flatpak 1.18 Released With Integration For AMD ROCm (Phoronix) Source: https://www.phoronix.com/news/Fl

Just in case you were wondering, Nvidia's RTX Spark isn't supposed to be a one-off. The company is not just flirting with becoming the fifth high-profile vendor of consumer laptop chips to see if people bite. At Computex 2026 in Taipei, Nvidia CEO Jensen Huang confirmed at least two additional generations of RTX Spark are […]

One hardware announcement and several software highlights from Microsoft Build.

Nvidia's new chips will power laptop workstations and mini desktop PCs at first.

Computex 2026 is kicking off in Taipei, Taiwan this week, where Nvidia, AMD, Qualcomm, Intel, and other tech brands are announcing new laptops, handhelds, chips, and more. Nvidia unveiled RTX Spark, its first family of consumer PC chips, arriving in laptops and mini PCs starting this fall. Intel is launching two new custom chips made […]

NVIDIA's CEO Jensen Huang is set to take the stage for his GTC Taipei keynote at 8PM PT / 11PM ET. You can watch all the announcements here and embedded below. Rumors have been flying about what to expect from today's presentation, but the big one is the possibility of a partnership with Microsoft and […]

Notes on Serving LLMs with TensorRT-LLM and Triton 2026-05-31 · LLM serving / NVIDIA stack These are working notes on taking an open-weights LLM from a Hugging Face checkpoint to a production-style serving endpoint on the NVIDIA stack — TensorRT-LLM for the engine, Triton Inference Server for the deployment surface — and benchmarking it honestly against vLLM on multi-GPU hardware. They follow the harness in trtllm-triton-serving (4× H100, NVLink). The goal is to move from "I use vLLM" to "I can stand up the NVIDIA inference stack on real multi-GPU hardware and reason about the trade-offs." 1. The serving pipeline The path from checkpoint to endpoint has four stages. Each one is a place where a decision affects latency, throughput, or accuracy: Checkpoint — a Hugging Face model. Engine build — compile to a TensorRT-LLM engine for a fixed tensor-parallel degree, precision, and batching policy. Model repository — wrap the engine in a Triton tensorrt_llm -backend model repo. Serving + load test — trtllm-serve (or Triton) exposes an OpenAI-compatible endpoint; a load generator drives it under controlled concurrency. The key mental shift from vLLM: TensorRT-LLM does ahead-of-time compilation . vLLM is a runtime that takes the model and serves it; TensorRT-LLM builds an engine specialized to your GPU, TP degree, and precision first. That build is where the performance comes from, and also where the rigidity comes from. 2. Tensor parallelism (TP) For a model that doesn't fit on one GPU — or to cut latency — TensorRT-LLM shards each layer across GPUs. On a 4× H100 NVLink box, TP=4 means every forward pass does an all-reduce across the four GPUs over NVLink. The all-reduce is not free. On this fabric it tops out around 77 % of the NVLink budget (see the separate NVLink-wall notes ). For prefill (large tensors) you're bandwidth-bound and TP helps. For decode (one token at a time) you're pinned against the small-message latency floor, and past a point more TP makes decode slowe

Nvidia says the Control Panel's features have been migrated to the Nvidia app.