标签：#nvidia

The new generation of data center systems is increasing efficiency with smarter traffic control instead of just more processor cycles.

The unexpected interruption came hours before the president wrote a congratulatory post on Truth Social about the company’s most recent earnings report.

There's a lot of capital pouring into the business of giving models away.

Modders are trying out an unofficial version of Nvidia's DLSS 5 on Skyrim, Cyberpunk 2077, GTA V, and a bunch of other games after code for the AI upscaling tech appeared in an early-access build of NBA 2K27. Members of the RenoDX modding channel on Discord reportedly found a way to extract the DLSS "Neural […]

Nvidia is nabbing critical infrastructure for open models as interest grows.

On Nvidia's earnings call Wednesday, CEO Jensen Huang casually announced the company had "achieved AGI," one of the tech industry's ultimate goals some of its biggest players have spent years chasing. Almost immediately, Huang dismissed the coveted milestone as "senseless." He's right. For the supposed finish line of the AI race, there is no consensus […]

Amazon is adding another 2 million Nvidia GPU chips to its data centers over the next two years. But this extended partnerships stretches beyond buying more chips.

Nvidia's predicting it will pull in $108 billion in revenue within just a few months. It wouldn't be the first company to rake in over $100 billion in quarterly revenue - Amazon, Apple, and Alphabet have repeatedly reached the milestone. Nvidia said in its latest earnings report that it brought in a record $96.2 billion […]

The new deal with the infrastructure provider is the latest example of Anthropic's white-hot compute-gobbling streak.

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.