6Hugging Face Blog·
NVIDIA introduces Cosmos 3 Edge via Hugging Face blog
NVIDIA announced Cosmos 3 Edge, a new model in the Cosmos series, published via the Hugging Face blog. The post appears to introduce an edge-optimized variant of the Cosmos world-model or video-generation family. Details are sparse from the body, but the announcement signals continued development of NVIDIA's Cosmos platform targeting edge deployment scenarios.
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Related events (8)
Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action
NVIDIA has released Cosmos 3, described as the first open omni-model targeting physical AI reasoning and action. The model is hosted and announced via Hugging Face, positioning it as an open-weights offering for robotics and embodied AI applications. The announcement highlights multimodal capabilities oriented toward physical world understanding and agent-level action.
NVIDIA Cosmos Reason 2 Brings Advanced Reasoning To Physical AI
NVIDIA has released Cosmos Reason 2, a model designed to bring advanced reasoning capabilities to physical AI applications. The announcement appears on the Hugging Face blog, indicating the model is likely available or accessible through the platform. This represents a continuation of NVIDIA's Cosmos model family targeting robotics and physical world understanding.
NVIDIA Cosmos 3, Nemotron 3 Ultra, and RTX Spark
A Latent Space AI news digest covers three NVIDIA announcements: Cosmos 3 (a world model/simulation platform), Nemotron 3 Ultra (a large language model), and RTX Spark (likely a new hardware or inference product). The piece frames these as a significant win for Jensen Huang and NVIDIA's AI portfolio. Coverage is commentary-tier aggregation rather than primary technical reporting.
Gemma 3n Fully Available in the Open-Source Ecosystem
Google's Gemma 3n model has been integrated into the open-source ecosystem via Hugging Face, making it broadly accessible for developers and researchers. The announcement covers availability of the model weights and tooling support within the Hugging Face platform. Gemma 3n is designed for efficient on-device inference, targeting mobile and edge deployment scenarios. This release extends the open-weights frontier model landscape with a multimodal-capable, efficiency-focused architecture.
Fine-Tuning NVIDIA Cosmos Predict 2.5 with LoRA/DoRA for Robot Video Generation
This Hugging Face blog post details a workflow for fine-tuning NVIDIA's Cosmos Predict 2.5 world model using LoRA and DoRA parameter-efficient techniques for robot video generation tasks. The post covers practical implementation steps for adapting the foundation video model to robotics-specific domains. This represents a concrete application of world models to embodied AI, where synthetic video generation can support robot training data pipelines.
NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets
NVIDIA announced new open models and datasets for physical AI development at GTC 2025, covered via the Hugging Face blog. The release targets robotics and embodied AI developers with open-weights resources. This represents NVIDIA's continued push into the physical AI ecosystem alongside its hardware dominance.
Hugging Face on AMD Instinct MI300 GPU
Hugging Face announces support and optimization for AMD Instinct MI300 GPUs, expanding the ecosystem of hardware that can run Hugging Face models and tools. The post covers integration work enabling inference and training workloads on AMD's high-memory GPU accelerator. This represents a meaningful step in diversifying AI infrastructure beyond NVIDIA dominance.
Bringing Serverless GPU Inference to Hugging Face Users via Cloudflare Workers AI
Hugging Face and Cloudflare have partnered to bring serverless GPU inference to Hugging Face users through Cloudflare Workers AI. The integration allows developers to run Hugging Face models on Cloudflare's global edge network without managing GPU infrastructure. This represents an expansion of serverless inference options for the Hugging Face ecosystem, lowering the barrier to deploying ML models at scale.