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Nvidia's GeForce Now cloud gaming service will officially support Valve's Steam Controller and Steam Machine starting later this year. With Steam Controller support, you'll be able to play games with Valve's great new gamepad when using the GeForce Now app on your Steam Deck or Steam Machine. If you're using GeForce Now on Windows or […]
The Nvidia Cloverleaf data center partnership announced on Friday tells you where the real constraint in AI has moved, and it is not the chip. TechCrunch reported that Nvidia has taken a minority stake in Cloverleaf , a company founded in 2024 that raised $300 million that year and sits between utility companies and data centers, arranging power and site infrastructure. Nvidia did not buy a chip designer. It bought a piece of the electricity supply chain. That reframing is worth thinking about if you build software from anywhere outside a well-supplied grid. 🔌 The bottleneck moved from silicon to substations Read the deal literally. Cloverleaf's product is not compute. It is power sourcing and site infrastructure — the interconnect agreements, the substations, the land next to a utility that can actually deliver load. Nvidia buying into that layer is an admission that shipping more GPUs does not help if nobody can plug them in. This was not a one-off either. The same week, per the reporting: Deal Announced Reported size What Nvidia bought into SB Energy (OpenAI-linked, Ohio) 17 Aug 2026 $1.5 billion Data center project with an energy parent Cloverleaf 21 Aug 2026 Several hundred million (WSJ), minority stake (Reuters) Utility-to-data-center power intermediary Terms were not disclosed by either company, so treat the dollar figures as press reporting rather than filings. Key takeaway: When the company that sells the shovels starts buying the mines, the scarce input is no longer shovels. For AI in 2026, the scarce input is grid capacity. 💰 The circular money problem you should price in Here is the part I would not skip over. Nvidia sells GPUs to data centers. Nvidia is now also investing in the companies that build and power those data centers . Some of that capital flows back as GPU orders. That is not illegal or even unusual in capital-intensive industries. Telecom vendors financed carriers for decades. But it has a specific consequence for you as a buyer of compute:
Nvidia continues to pour money into data center development — just as AI data centers bring lots of money into Nvidia.
The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or for firms to hedge their exposure when the price changes. Silicon Data […]
April - 1805 Napoleon is master of Europe Only the British fleet stands before him Compute is now an asset class I see it is once again time to talk financial innovation. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR are all working with Nvidia to put together $500 billion in financing to turn compute […]
Nvidia's investment in SoftBank's data center developer will guarantee its chips power an OpenAI data center.
Filing comes after Elon Musk announced exclusive arrangement to kit out its data centers.
Qwen3.8-2.4T-A95B is a 2.4-trillion-parameter Mixture-of-Experts model with roughly 95B parameters active for each token. If you're planning to self-host it, the first thing to know is that this is a genuinely large distributed model: even the low-precision checkpoints are measured in terabytes. The official open checkpoint is: Qwen/Qwen3.8-2.4T-A95B The model has 512 routed experts and selects 10 of them per token alongside one shared expert. Its 92-layer backbone mixes 69 Gated DeltaNet linear-attention layers with 23 full-attention layers, with full attention appearing every fourth layer. Native context is 262,144 tokens , with an extended configuration available up to roughly 1.01 million tokens . The open checkpoint is text-only and always uses reasoning. This is different from Qwen's hosted Qwen3.8-Max service, which adds features such as vision input and non-thinking mode. For GPU deployment, the main decision is not whether 2.4T parameters will somehow fit. It is which precision format gives you a documented configuration on the hardware you actually have . Start with the checkpoint that matches your GPUs The practical options today are: Your GPUs Checkpoint Documented setup 8× B300 Inferact/Qwen3.8-2.4T-A95B-NVFP4 TP8 8× GB300 Inferact/Qwen3.8-2.4T-A95B-NVFP4 TP8 across two NVL4 trays 16× B300 Qwen/Qwen3.8-2.4T-A95B-FP8 TP16 16× GB300 Qwen/Qwen3.8-2.4T-A95B-FP8 TP16 12× GB300 Qwen/Qwen3.8-2.4T-A95B-FP8 TP4 × PP3 8× MI355X Inferact/Qwen3.8-2.4T-A95B-MXFP4 TP8 The full BF16 checkpoint is roughly 4.45 TiB . The official FP8 version is around 2.27 TiB , while the NVFP4 checkpoint used in the NVIDIA eight-GPU recipe is around 1.32 TiB . That is why NVFP4 is the most approachable NVIDIA deployment if your goal is simply to get Qwen3.8 running without moving immediately to a 16-GPU cluster. H100, H200, A100, B200 and smaller GPU configurations are not included here. Current vLLM material contains sizing information for some of those GPUs, but not equivalent end-to
In mid-2026, FP8 and FP4 have become essential tools for making large-scale AI training and inference more efficient . FP8 uses two main formats-E4M3 for better precision on activations and weights, and E5M2 for wider dynamic range on gradientswhile NVIDIA’s NVFP4 takes things further with 4-bit values and micro-block scaling (shared FP8 scales per 16 elements plus a tensor-level scale). These formats dramatically cut memory use and increase throughput on modern GPUs compared with traditional BF16 or FP16, making it possible to train and serve bigger models on the same hardware. The benefits are clear: roughly 2× memory savings with FP8 and up to 3.5× with NVFP4, higher Tensor Core performance, and better energy efficiency. The trade-offs come from reduced numerical range and precision, which can lead to accuracy loss or instability unless carefully managed with techniques such as delayed scaling, stochastic rounding, Hadamard transforms, and selective quantization that skips sensitive layers. When these methods are applied properly, accuracy often stays within 1–2 % of higher-precision baselines on real workloads. Research has moved quickly from the foundational 2022 FP8 paper to 2025 studies showing stable FP4 pre-training of multi-billion-parameter models. Hardware support is mature for FP8 on Hopper GPUs and reaches its peak on Blackwell with native NVFP4 and MXFP8 acceleration. Among frameworks, PyTorch currently leads with native float8 dtypes, Transformer Engine for production training, and TorchAO for optimized inference. JAX offers solid support through Transformer Engine, TensorFlow/Keras provides simpler quantize-to-FP8 options but relies more on TensorRT for high performance, and libraries such as bitsandbytes remain useful for complementary 4-bit memory savings. Practical adoption is already strong for both training and inference, especially when teams start with proven recipes, monitor scaling factors, and prototype on smaller models. Workarounds for r
Nvidia has a plan to make sure its GPUs won't lose value. It wants to convince a new crop of financiers to keep lending for AI buildouts.
The week-old Open Secure AI Alliance, spearheaded by Nvidia and grown to over 120 companies, already has proposals out for defending against AI agents.
July 2026 is over. The month that opened with AUTONOMOUS 2026 and WAIC 2026 running simultaneously on opposite sides of the Pacific closed with the sector tallying what it built. The number that defines the period is $55.8 billion in robotics funding across H1 - nearly double the prior full-year record. But the more durable signal from this week is operational rather than financial: Neura Robotics has a confirmed deployment date at a Schaeffler facility in December, NVIDIA's simulation-to-real pipeline is now functional at production scale, and five simultaneous shifts are reshaping factory floors right now, not in 2027. The questions that drove the first half of 2026 - does Physical AI work, is the funding real, will the robots actually arrive - are no longer interesting. H2 starts with harder ones. Stats: Value Description $55.8B Robotics funding raised in H1 2026, nearly double the prior annual record $8.6B Humanoid startup funding in H1 2026 alone, 1.8x all of 2025 December 2026 Confirmed first deployment of Neura Robotics humanoids at Schaeffler's German facilities 5 Simultaneous operational shifts reshaping factory floors identified in the mid-2026 analysis Neura Robotics Has a Deployment Date: December 2026 in a Schaeffler Factory Most Physical AI deployment announcements are directional. "We are partnering with X to explore robotics in our facilities" is a press release. A confirmed month and a specific facility is a contract. Neura Robotics confirmed that Schaeffler - one of the key investors in its $1.4 billion Series C alongside Amazon, Nvidia, Qualcomm, and the European Investment Bank - plans to deploy Neura's humanoids in its German facilities in December 2026 . Schaeffler manufactures precision bearings and components for electric vehicles, operating in environments where dimensional tolerances are measured in micrometers. Deploying a humanoid robot in that context is a fundamentally different challenge than warehouse pick-and-place or automotive sequ
Best Buy has raised the price of the Asus ROG Astral RTX 5080 OC to $2,099 - the first time a major US retailer is directly selling an RTX 5080 for more than the $1,999 MSRP of the RTX 5090, VideoCardz reports. The ROG Astral RTX 5080 OC now costs 40 percent more than its […]
Introduction It was a perfectly normal day. I opened my laptop, ready to get some work done, and then... BAM. A black screen. Not a gentle fade to black, but more like my computer shouting, "I’ve had enough of your crap!" The same operating system that had been working perfectly just five hours earlier had suddenly decided it had had enough of life. I wasn't too worried though. After all, I had ChatGPT on my side. Three hours later... Yeah... my confidence crumbled faster than my phone battery at 2%. What followed was a three-hour rabbit hole involving NVIDIA drivers, multiple Linux kernels, Secure Boot, DKMS, Xorg, GDM, journalctl , systemd , and more terminal commands than I'd like to admit. Somehow, against all odds (and probably a little divine intervention), we managed to fix it. And honestly? I enjoyed every minute of the chaos. It was like a wild adventure—except with more curse words and less danger. So I decided to document the entire debugging journey—not just because it might help someone who runs into the same issue, but also because I deserve a little sympathy after spending three hours arguing with my laptop. (And if the solution seems painfully obvious to you... please let me enjoy my victory. Don't take this away from me.😤 The Problem After rebooting my laptop, I was greeted with just a black screen. No login screen, no desktop… just nothing.** At first, I tried to enter TTY using Ctrl + Alt + F3, but that wasn’t working either. Since I wasn’t able to reach TTY directly, I had to take a different route. By editing the GRUB boot entry and booting into multi-user.target , I forced Linux to start in text-only mode, giving me access to a terminal.** For this, I edited the GRUB boot entry and appended systemd.unit=multi-user.target to the end of the kernel command line (after quiet splash ). That was the first breakthrough, though. The operating system wasn’t completely dead… only the graphical interface was failing to wake up. First Clues and Initial Ass
After two years in stealth, Safe Superintelligence has announced a long-term partnership with Nvidia as it prepares to scale to its next phase.
Nvidia on Monday said it is joining forces with Microsoft, SpaceX, IBM, and other tech companies to build and share open-source AI security tools. The new Open Secure AI Alliance said open tools are required to effectively defend against attacks from frontier models. The initiative is a direct response to mounting concerns over the safety […]
If there's a place in the universe without GPUs, Nvidia is sending them there.
Nvidia’s Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company’s growing ambition to power every layer of AI infrastructure.
Alphabet, Google's parent company, is reportedly working on a new chip designed to make its Gemini models run much more efficiently.
AI infrastructure company Infinity announced Monday a $15 million raise at a $100 million valuation from investors including Touring Capital, Principal VC, and researchers from companies such as OpenAI and Anthropic.