Nvidia has held acquisition talks in recent weeks with Hugging Face that could value the open-source AI platform at more than $13 billion, according to a person familiar with the matter. No agreement has been reached, and the discussions could still collapse. Nvidia and Hugging Face did not respond to requests for comment. Microsoft also met with Hugging Face, but two people familiar with the situation said those talks are no longer ongoing. Hugging Face hosts millions of AI models and datasets used by developers building with open-source technology. Nvidia already participated in Hugging Face’s $235 million funding round in 2023, when the company was valued at $4.5 billion. The Financial Times previously reported that Hugging Face rejected a $500 million Nvidia investment offer late last year that would have valued it at $7 billion, saying it did not want a dominant investor able to influence its decisions. Nvidia has expanded its investments and acquisitions as it builds on its cash reserves. The company said it had committed $18 billion to equity investments for the rest of its fiscal year and held $47.9 billion in private companies. Nvidia ownership could expand its relationship with developers using Hugging Face, but it could also raise questions about the platform’s neutrality because Hugging Face supports models and hardware from Nvidia competitors including AMD and Intel.

Nvidia is extending CUDA support toward RISC-V CPUs, potentially allowing server-class RISC-V systems to host Nvidia GPU compute workloads. CUDA currently supports x86-64 and AArch64 CPUs. Nvidia requires RISC-V systems to use the RVA23 profile and comply with RISC-V server SoC and platform specifications, which cover baseline reliability, availability, serviceability and security features. Nvidia also requires hardware features beyond those specifications, including vector extensions, PCIe cache coherency and peer-to-peer PCIe communication between devices. PCIe coherency prevents GPU DMA transfers from missing modified CPU-cache data or leaving CPUs with stale cached data after GPU writes. Without it, CUDA software would need explicit cache invalidation handling. Peer-to-peer PCIe support allows device-to-device buffer transfers without routing data through CPU memory. ACPI support was another initial obstacle, but the UEFI Forum added RISC-V ACPI support in 2025, and the ratified RISC-V Boot and Runtime Services specification includes ACPI. Nvidia said it is partnering with SiFive, which plans to demonstrate a CUDA-capable system at Hot Chips; the cited example specifications indicate a high-core-count server processor. Nvidia also described NVLink Fusion, which lets partners integrate Nvidia NVLink IP with custom CPUs or accelerators, including potential RISC-V designs, subject to CUDA requirements and support for frameworks such as DOCA and NCCL.

NVIDIA said its general-purpose coding agent, NVIDIA AVO, scored 100% on the ARC-AGI-3 interactive reasoning benchmark. The company said AVO completed all 183 levels across 25 public environments. NVIDIA said the agent determined what actions to take without instructions, explicit rules, or stated goals. The result concerns ARC-AGI-3’s interactive reasoning benchmark and NVIDIA’s reported performance on its public environments.

Writer Ed Zitron argues that OpenAI faces a severe financing risk because its compute commitments, operating losses and capital requirements greatly exceed its current revenue and available funding sources. He cites reported figures that OpenAI lost $20.9 billion on $13.07 billion of revenue in 2025, generated $5.7 billion in revenue in the first quarter of 2026 while spending $12.1 billion on cost of revenue and training, and is approaching a $40 billion annualized revenue run rate in August 2026. Zitron criticizes annualized revenue as a promotional metric based on a recent four-week period rather than full-year results. Zitron estimates that OpenAI has more than $800 billion in compute-related obligations through 2030, including at least $147 billion expected through the end of 2027 across major cloud and infrastructure providers. He says OpenAI may need to raise $100 billion to $200 billion annually before reaching projected profitability, while its $122 billion March funding round valued it at $852 billion and relied primarily on Amazon, Nvidia and SoftBank. He argues that an Anthropic IPO could make an OpenAI listing more difficult by exposing both companies’ losses and economics to public-market scrutiny. Zitron speculates that a cash shortfall could lead to a rescue fundraising round, a Microsoft acquisition, a merger with Anthropic, or a collapse that disrupts cloud providers and infrastructure companies dependent on OpenAI spending.

Nvidia is scaling back its potential $250 billion guarantee for OpenAI's data center financing. This reduction reflects a shift in Nvidia's commitment to supporting OpenAI's infrastructure needs.

Researchers from NVIDIA and Flower Labs have developed a method for recursive self-improving AI agents that allows them to enhance their own code without reaching an evaluation ceiling. This advancement addresses a significant challenge in the development of self-improving AI.

NVIDIA has launched the Nemotron 3.5 Lightning model, designed for high efficiency in long-running agentic AI workloads. This release enhances the Nemotron 3 family, following the introduction of Nemotron 3 Nano, and aims to improve accuracy and speed in AI applications.

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NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 is available for use with libraries such as Transformers for tasks like text generation. Users can implement this model by importing the pipeline from the Transformers library and specifying the model for text generation tasks.

Nvidia is navigating significant risks as it expands its business in the AI sector. The company's investments and market strategies are critical to its future growth amidst competitive pressures and technological advancements.