Search “NVIDIA”

Mifeng jointly launches the embodied evaluation platform RoboColiseum, with GPT-6, Opus 5.5, and Gemini 4 competing head-to-head

Mifeng Technology, together with nearly 20 institutions including Hugging Face, NVIDIA, and Tsinghua University, jointly launched the embodied evaluation platform RoboColiseum, and announced this round's results on October 20: Claude Opus 5.5 ranked first overall among the four flagship models, followed closely by GPT-6 Astra. GPT-6 Astra scored 99.35 on instruction following, while Opus 5.5 performed better on spatial understanding and general manipulation and consumed the fewest tokens. The platform contains 78 simulated evaluation tasks, with a simulation-to-real-world correlation of 89.5%, and since launching in August, more than 100 teams have participated.

MiniMax M3.1 Flash Gets Day-One Support from SGLang and Miles

MiniMax announced that M3.1 Flash is ready from inference to RL, with SGLang and Miles providing support on the very first day of release. According to SGLang, the model achieves over 1K tok/s for bs1 decode on SGLang, supports NVIDIA Blackwell and Hopper (GB300/B300/B200/H200) as well as AMD Instinct MI350X and MI355X, and supports MiniMax's DSpark draft model along with HiCache and multimodal input.

KT Cloud Breaks Ground on 50MW AI Data Center in Ansan

On October 22, KT Cloud broke ground on its own 50MW AI data center in Ansan, Gyeonggi Province. The facility has a total floor area of approximately 36,878 square meters, with 9 floors above ground and 2 below, and is scheduled for completion in 2029. All server rooms in the facility will support liquid cooling, and it will use a high-load structural design to accommodate Nvidia's next-generation Vera Rubin GPUs, while also introducing a free-cooling system to reduce cooling energy consumption. The project is part of its long-term strategy to secure 1GW of AI data center capacity by 2031.

How Mixx Technologies Paves the Way for All-Optical Scale-up Networks

Photonics startup Mixx Technologies is betting on all-optical scale-up networks. Its first product, the SxC connector, can accommodate 64 fibers within a single connector, and the company claims it can arrange 1,000 fibers within 800mm2. The company has laid out a three-step roadmap of NPO optical engines, CPO, and optical circuit switches. Its first NPO module is targeted for launch in 2028 or 2029, and CPO products are expected to be released by the end of this decade. If Nvidia's Vera Rubin NVL576 is estimated at one pair of fibers per SerDes, it would require approximately 20,736 pairs of fiber connections to connect 8 NVL72 racks.

Cohere Model Vault Launches Confidential Computing Public Beta

Cohere announced that the confidential computing feature of Model Vault has entered public beta, providing hardware-level isolation for hosted model inference based on the NVIDIA H100 GPU confidential computing capability on Microsoft Azure. This feature extends the scope of encryption from data at rest and in transit to prompts, documents, responses, and model execution during inference; after end-to-end encryption on the application workload side, decryption can only occur in a confidential environment that has passed remote attestation. Customers can cryptographically verify the hardware, firmware, and software configuration before sending sensitive data; if it does not comply with the security policy, the client will refuse to send the request. The existing Model Vault API and request format remain unchanged.

Cohere Model Vault Launches Verifiable Confidential AI Inference

Cohere has launched confidential computing capabilities on Model Vault, currently in public beta, running on NVIDIA GPUs on Microsoft Azure. The feature combines confidential CPU and GPU execution, OHTTP end-to-end encryption of request content, and remote attestation, allowing customers to verify the target environment before releasing sensitive data. Cohere plans to open-source the confidential inference stack and advance support for NVIDIA Blackwell and B200 GPUs.

Jensen Huang on neocloud financing and high cost of capital

Jensen Huang said that Nvidia invests in neoclouds because they are good investments, which is different from an endorsement. Neoclouds have ample customers and are sold out; the only problem is a lack of working capital to buy land, power, buildings, and chips to fulfill commitments. He believes that rising borrowing costs are a side effect of enormous AI demand, not a flaw in the business model, and that when demand is high, other problems will be resolved.

xAI launches the SpaceX Super Intelligence partner network to build enterprise products on Grok

xAI launches the SpaceX Super Intelligence partner network for companies building products, solutions, and services on Grok, divided into two categories: Transformation Partners and Marketplace Partners. More than 130 companies have already participated in the program's early access phase, and the Forward Deployed Engineer certification program has trained engineers at more than 30 organizations. Bain & Company, Databricks, NVIDIA, Snowflake, and others are among the first Launch Partners.

RhymeQ Emerges from Stealth with $2.2M to Simplify Quantum Programming

Quantum computing startup RhymeQ has emerged from stealth, closing a $2.2 million pre-seed funding round led by Ground State Ventures, with participation from Vanagon Ventures, Plug and Play, and Freiraum Ventures. Its platform Rhyme provides an abstraction layer that lets developers with basic Python skills write and debug code for quantum computers; the compiler translates the code for quantum hardware and supports multiple quantum processors. RhymeQ is an NVIDIA Inception member, and its technology is compatible with NVIDIA's CUDA-Q platform.

FastVideo FastH3 Omni Runs Locally on DGX Spark, RTX GPUs, and Apple MLX

Hao AI Lab announced that FastVideo FastH3 Omni can run locally on NVIDIA DGX Spark, RTX GPUs, and Apple MLX. Generating a 5-second 480p video from a single photo and a prompt takes about 3 minutes, or about 2 minutes on two Sparks; FastH3 T2AV takes 52.6 seconds on a single Spark and 30.7 seconds on two Sparks. The clips in the demo video were all generated by DGX Spark using an 8-step NVFP4 checkpoint, and blog, Cookbook, and Hugging Face links are provided.

One-Year-Old AI Research Lab Flapping Airplanes Raises $1 Billion, Reportedly Valued at $6 Billion

AI research lab Flapping Airplanes announced it has closed a new $1 billion funding round, with Bloomberg reporting a valuation of $6 billion; the company is about a year old and has no commercial product yet. In its first year the company focused mainly on developing new GPU primitives, saying its megakernel framework can complete full training, inference and game running within a single kernel, allowing memory-bound inference and compute-bound training to overlap on the same chip. The Series A round was co-led by Index Ventures and Lux Capital, with Sequoia Capital and GV, which participated in the $180 million seed round in January, continuing to invest, and Nvidia also among the institutional investors.

Era4 ties financial sovereignty to UK AI infrastructure

Era4, through self-built energy, UK-based AI facilities and its own hardware, argues for giving the UK more control over the AI technology stack and keeping related revenue within the domestic economy. Its Chesterfield facility uses liquid-cooled Nvidia B300 systems supplied by HPE and runs HPE Private Cloud AI; the company also plans to build AI infrastructure on about 20% of a 230-acre former steelworks site in Newport, Wales.

Recursive Releases Open-Source Coding Model Flint, Says Most of Development Process Was Completed by AI

Recursive released Flint, its first open-weight coding model, with 40B parameters. CEO Richard Socher said that nearly every stage of the model pipeline was completed by its Eureka Machine system, including a new base training method, the model architecture, and code optimized for specific chips. A WIRED author tested it on a Mac with 48GB of memory, using it to build a command-line to-do app, a web research scraping program, and an RSI educational website, and found its speed and accuracy superior to models of the same size. Recursive has raised $650 million from Google Ventures, Greycroft, Nvidia, and AMD Ventures, at a valuation of $4.65 billion.

Inception Releases Diffusion Language Model Mercury 3, Claiming Speeds Four to Eight Times Faster Than GPT-6 Luna

Inception has released the diffusion language model Mercury 3, claiming it wins 8 of 12 benchmarks and runs four to eight times faster than GPT-6 Luna and Claude Haiku 5.5. In Inception's own testing, Mercury 3 scored 76.84 on IFBench, 15 points higher than GPT-6 Luna; it scored 51.08 on SWE-Bench Atlas, versus 33.33 and 32.26 for its competitors. Claude Haiku 5.5 led on the DeepSWE long-code test and long-text retrieval. The model generates 1,068 tokens per second on Nvidia GPUs, is priced at $0.20 per million input tokens and $0.70 per million output tokens, with a 50% discount during the launch period, and is now available on the Inception platform as well as OpenRouter, Baseten, and Vercel; there are currently no independent tests.

DeepSeek open-sources the Deepinfer inference framework, lowering the cost of self-hosted model deployment

DeepSeek has open-sourced its internal inference framework Deepinfer on GitHub, with the goal of reducing the compute and energy costs of deploying models on your own. During the inference stage, the framework distributes load more evenly across available processor cores, uses speculative decoding to speed up responses, and improves the structure of results before output; it currently supports only DeepSeek-V4.1-Flash, with more of its own models to be opened up later. On the hardware side, it requires Nvidia GPUs with the SM100 architecture or Huawei Ascend 950 NPUs, so it is not aimed at individual users.

English translation is being prepared.

English translation is being prepared.

Deploying the RFD4-Proteina model on Databricks

NVIDIA and Databricks plan to integrate NVIDIA BioNeMo's open-source protein design model RFD4-Proteina into Databricks' Genesis Workbench, running on the now generally available AI Runtime serverless NVIDIA GPU compute. The model can simultaneously generate protein sequences and all-atom structures, and supports optional reward-guided search to steer candidate designs toward target properties such as thermostability and binding affinity. Genesis Workbench includes two built-in RFD4-Proteina workflows, Antibody Design and Vaccine Design, and can be extended through Genie Code and NVIDIA BioNeMo Agent Toolkit skills.

English translation is being prepared.

English translation is being prepared.