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NVIDIA released Isaac GR00T N1.5, an updated foundation model for robot reasoning that can be fine-tuned for specific robot arms and tasks through post-training. The fine-tuning process requires approximately 25 gigabytes of VRAM and involves four steps: dataset preparation, model fine-tuning with 10,000 maximum training steps, open-loop evaluation, and deployment to physical hardware. Developers can now customize the model for diverse robot embodiments like the LeRobot SO-101 arm using the EmbodimentTag system instead of being limited to pre-trained configurations.
Mistral AI announced Mistral Compute, a new AI infrastructure service offering customers access to GPUs, orchestration tools, and managed platforms ranging from bare-metal servers to fully managed services. The offering will provide access to tens of thousands of NVIDIA GPUs with rapid expansion planned, and is being positioned as an alternative to US and China-based cloud providers for organizations seeking data sovereignty. This allows enterprises, nations, and research institutions to build and own their complete AI infrastructure stack rather than relying on third-party cloud providers.
Together AI launched a Batch API that processes large volumes of LLM requests asynchronously at 50% lower cost than real-time inference. The service supports up to 50,000 requests per batch file with a best-effort 24-hour completion window across 15 models including DeepSeek, Llama, and Mistral. Users can now handle non-urgent workloads like data classification and synthetic generation without consuming real-time API rate limits.
Hugging Face and NVIDIA announced Training Cluster as a Service, a collaboration enabling research organizations to access large GPU clusters on-demand for training AI models. The service allows any of Hugging Face's 250,000 organizations to request GPU capacity from NVIDIA Cloud Partners and pay only for the duration of their training runs. This reduces barriers for universities, labs, and companies that lack access to expensive supercomputer infrastructure, allowing them to develop domain-specific AI models.
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