News linked to both this project and an event.
Odaily Report: NVIDIA and Oracle have announced an expanded partnership, scaling the deployment of the NVIDIA Vera Rubin architecture to the multi-gigawatt level and bringing the NVIDIA Vera CPU into Oracle Cloud Infrastructure (OCI). Additionally, Oracle also announced in the statement a partnership with Cerebras, under which Oracle will use Cerebras' equipment in its AI data centers.
Odaily reports: AI compute startup General Compute has completed a $100 million Series A funding round, led by Blackbird Ventures and Abstract Ventures, with a post-money valuation of $800 million, a significant increase from the $60 million valuation after its seed round in May this year. The funding was driven by an order from a large customer with $100 million in annual recurring revenue.General Compute focuses on AI inference compute, using specialized chips from NVIDIA competitors, and has reached a $400 million chip procurement agreement with SambaNova, with suppliers also including Positron and Cerebras. Unlike the traditional approach of building large data centers, the company deploys AI compute by leasing data center space, utilizing smaller-scale power resources, and retrofitting Bitcoin mining farms, with plans to deploy 75 megawatts of compute capacity by 2027. Amid CoreWeave's stock falling to near a 12-month low and Elon Musk's prediction that more than 200 new cloud service providers will undergo elimination and consolidation, General Compute co-founder and CEO Finn Puklowski believes that market fears of a chip-induced industry collapse are severely exaggerated, and customer demand for various chips and compute remains strong.PANews, October 27: According to reports, AI compute startup General Compute has completed a $100 million Series A funding round, led by Blackbird Ventures and Abstract Ventures, with a post-money valuation of $800 million, a significant increase from the $60 million valuation after its seed round in May this year. Currently, General Compute focuses on AI inference compute, using specialized chips from NVIDIA competitors, and has reached a $400 million chip procurement agreement with SambaNova, with suppliers also including Positron and Cerebras. (TechCrunch)
According to TechCrunch, NVIDIA participated in the $14 million Series B financing round for Indian startup Simplismart, which was led by Dallas Venture Capital with participation from Accel, Shastra VC, and Micromax. The transaction values the four-year-old company at approximately $100 million, including the new investment. Simplismart states that its software sits between AI models and compute infrastructure, automatically adjusting model deployment and management to optimize response time, request throughput, and compute utilization, enabling customers to better leverage their existing GPU resources. The company noted that its software helped AI video generation platform InVideo reduce GPU service costs by 56%. Simplismart CEO Amritanshu Jain told TechCrunch that the company’s annualized revenue run rate grew 14 times over the past six months to reach an eight-figure amount. This figure is based on recent customer usage rather than contractual recurring revenue, and excludes revenue passed through to infrastructure providers. Although the company serves more customers in India, the United States accounts for more than half of its total revenue.
NVIDIA stated that it will expand its collaboration with Meta on Muse and the Muse series models, focusing on optimizing AI Agent training and inference performance. The partnership will also explore using NVIDIA OpenShell to enhance the secure deployment of AI Agents and leverage NVIDIA Sentry to monitor Agent behavior. On the infrastructure front, Meta plans to expand deployments of NVIDIA Vera Rubin and Vera CPUs, and adopt Spectrum-X Ethernet networking and BlueField-4 to improve network efficiency and throughput. Additionally, NVIDIA plans to invest $3 billion in Meta's equity offering.
According to an official announcement from Bitget, the platform has partnered with structured products liquidity provider VETA Finance to upgrade its stock dual-currency investment products. This upgrade expands the product pool beyond existing popular underlying assets such as NVIDIA (rNVDA), Google (rGOOGL), and AMD (rAMD), while enriching investment term options; supported by deeper liquidity, it provides more competitive annualized yields, and optimizes the product architecture and settlement mechanisms to deliver a better trading experience and more diversified return opportunities for users.
Gate has released the Singapore special edition of its documentary series "Behind Gate: Dr. Han," which follows a three-day itinerary as its main thread, authentically chronicling the daily work and key moments of platform founder and CEO Dr. Han during TOKEN2049 Singapore, covering multiple scenarios including the launch of Gate Money, keynote speeches, media interviews, client meetings, and an awards dinner.The documentary focuses on the release process of Gate Money and reveals a real clip of Dr. Han using NVIDIA stock to buy coffee before his speech. He subsequently shared this experience in his keynote address to demonstrate the connection between asset management and everyday payments. In the film, Dr. Han states that Gate Money is not only aimed at traders but also hopes to serve a broad global user base, and that the platform needs to maintain innovation while continuously refining its products to meet users' increasingly diverse financial needs.The film also captures heartwarming moments of Dr. Han communicating with team members, discussing customer needs, and preparing a birthday cake for colleagues, revealing an everyday side beyond the tight schedule. Looking ahead, Dr. Han regards Gate Money as a new mission and starting point, and sets out a long-term vision of serving approximately 500 million users over the next five years, demonstrating Gate's strategic direction of continued development toward a comprehensive financial services platform.
Goldman Sachs assesses that AI deployment remains on track, with physical AI revenue diversifying. Supply chain capacity expansion points to a CPU ramp exceeding prior expectations. The firm maintains a Buy rating with a $300 target price, representing 38.6% upside from the Oct 20 closing price of $216.47; roughly $235 billion has been authorized for share buybacks. We recommend focusing on the scaling of physical AI revenue and supply chain delivery progress.
Odaily reports: AI cloud service provider CoreWeave has announced the launch of Physical AI Launchables, unveiled at the NVIDIA GTC Berlin event. The product offers physical AI workload templates built and tested by CoreWeave engineers, aimed at robotics, autonomous vehicle, and simulation teams, enabling task launch via a single command and reducing upfront preparation work such as environment configuration, storage setup, and task scheduling.The first batch of templates covers five tools—NVIDIA Isaac Sim, CARLA, MuJoCo, NVIDIA AlpaSim, and NVIDIA Cosmos 3—supporting workflows such as data generation, model evaluation, and policy training, and integrating Cosmos Data Factory-related agent skills usable for video, image, and text data augmentation, auto-labeling, and data curation. The platform also provides specially optimized skills for coding agents such as Claude Code and Codex, supporting automatic task launch, run-status monitoring, and result retrieval.CoreWeave is also collaborating with physical AI software vendors including Cadence, Encord, and Voxel51 to bring related products onto its cloud infrastructure.
NVIDIA announced an upgrade to the Isaac general-purpose robotics development platform, launching a new humanoid robot reference framework, simulation workflow, and data tools designed to accelerate general-purpose robotics development. The updates cover robot training, simulation validation, and deployment processes, helping developers use synthetic data and simulation environments to train robots and validate their behavior before actual deployment.
DeepSeek announced that it will fully open-source its internal inference framework DeepInfer, while simultaneously releasing the production-grade token encoder-decoder component deepseek-recipe, to help developers and enterprises optimize the throughput, latency, and deployment costs of large-scale model inference. DeepInfer integrates high-performance operators and a deeply optimized inference engine, supporting capabilities such as distributed inference, speculative decoding, SWA Bounded Replay, and structured output, while maintaining compatibility with NVIDIA and Huawei Ascend platforms. DeepSeek noted that it has already realized high-throughput, low-latency large-scale inference services for DeepSeek V4.1 Flash based on DeepInfer, and future inference code for new models along with subsequent optimizations will continue to be updated within this framework.
According to Reuters, Nvidia has secured a CPU order from New York-based AI cloud computing startup Modal. Previously relying solely on Intel and AMD chips for its AI agent workloads, Modal plans to transition to Nvidia's Vera central processing units. The company stated that early tests show Vera can double its AI agent workload capacity compared to existing Intel and AMD hardware. Modal CEO Erik Bernhardsson said the firm will maintain a hybrid chip deployment strategy, noting that next-generation chips from Intel and AMD may narrow the performance gap. Nvidia stated that Vera is the next-generation data center CPU designed for AI factories, marking another step in expanding its business beyond GPUs. Nvidia also added that OpenAI, Anthropic, and Oracle plan to deploy Vera.
According to TechCrunch, Nvidia has launched a batch of new open-weight models targeting agents and edge computing. Among them, Nemotron 3.5 Super VL is a 120-billion parameter model primarily aimed at enterprise-grade Agents. Nvidia states that the model's accuracy on instruction following and tool-use tasks can approach that of Nemotron 3 Ultra, and after post-training for specific Agent tasks, it can complete tasks faster than certain models from Anthropic and OpenAI.
Nebius announced the expansion of its AI Developer Program across Europe, partnering with Accel, NVIDIA, and European venture capital networks to invest $10 million in compute grants, capital access, and community projects. The initiative aims to assist European developers in building and deploying an open, independent AI ecosystem, covering open model R&D, coding agents, inference and infrastructure tools, reinforcement learning, and evaluation data tools.
According to Bloomberg, clinical-stage cancer drug development and technology company Iambic Therapeutics Inc., backed by Nvidia Corp., raised $150 million through a U.S. initial public offering (IPO). In a statement released on Wednesday, the company said it sold approximately 9.38 million shares at a price of $16 per share, priced at the midpoint of its previously indicated offering range of $15 to $17.
According to the UK's Financial Times, NVIDIA-backed AI cloud provider CoreWeave is seeking to tap into insurers' vast pools of capital to reduce its financing costs for purchasing AI chips and building infrastructure. Brandon McBride, CoreWeave's co-founder and head of financing, stated that if the company can secure investment-grade ratings for more of its chip-backed debt and ultimately improve its own credit rating, it will have the opportunity to enter the insurance capital market. He noted that the insurance industry's capital base is "almost infinite," because "all the money sitting in insurance capital pools represents one of the largest funding sources in the world."
According to CNBC, Microsoft officially released its third-generation in-house AI accelerator chip, the Maia 300, on October 12, announcing plans to use the Maia series as the primary architecture for running AI models to gradually reduce reliance on NVIDIA GPUs. The Maia 300 is slated for production in 2027, with each data center rack capable of hosting 96 chips (doubling over the previous generation), increasing AI token processing volume per rack by seven times and improving token processing efficiency per megawatt by 40% compared to GPUs. Microsoft aims to deploy multi-gigawatt-scale Maia chip clusters, with Microsoft 365 Copilot expected to be the first to benefit from the performance improvements. Analysts at Evercore ISI estimate that by Q1 2027, Maia chips will account for approximately 10% of Microsoft’s AI chip spending. Notably, Maia accelerators are currently reserved exclusively for internal use and are not available for external leasing; NVIDIA and AMD will continue to participate in Microsoft's model training operations.
Bitdeer AI has secured a 67MW data center campus in Malaysia, planned to host NVIDIA GB300 systems. Following this expansion, its secured AI cloud business capacity has increased to 273.5MW, and its AI cloud business pipeline is expected to have exceeded $10 billion.
According to CoinDesk, asset tokenization platform Securitize has announced the launch of tokenized US stock products on the Solana blockchain, with the initial batch covering shares of 12 companies including Apple, NVIDIA, and Tesla. Each token is pegged 1:1 to the underlying actual shares and retains corresponding shareholder rights. Trading supports USDC settlement, with liquidity provided by Jump Trading. Additionally, the product plans to expand in the future to the digital trading platform under the New York Stock Exchange (NYSE) and the OKXICE platform.
According to Reuters, NVIDIA-backed AI infrastructure startup Upscale AI has announced the launch of the Token Fabric platform, designed to help customers connect AI chips from different vendors within the same data center. Built on devices leveraging NVIDIA's Spectrum-X technology, the platform combines hardware and software to enable cross-vendor AI processor interconnectivity.
据 CrowdFundInsider 报道，ARK Invest 首席执行官 Cathie Wood 表示，投资者判断下一轮技术趋势时，应从关注开发者投入时间转向追踪 AI Agent 的消费行为，即观察自主软件将资金支付给哪些服务。随着 AI Agent 从回答问题发展到自主执行任务，市场需要关注这些智能体如何选择服务、完成交易以及资金流向，未来 AI Agent 产生的支付活动可能比产品发布本身更能反映技术真实采用情况。 Cathie Wood 预测 2027 年市场发展时表示，OpenAI、Anthropic 以及英伟达（NVIDIA）将继续成为 AI 基础设施竞争核心，算力供应、能源、资本投入和模型可靠性仍是行业扩张面临的关键限制因素，如果 AI Agent 已经开始自主选择服务并进行支付，那么追踪其资金流向可能成为衡量 AI 商业化落地的重要方式。