Nebius AI monthly digest, March 2024

The latest release introduces managed databases and container registries to enhance machine learning (ML) workloads by offering secure, fault-tolerant, and ready-to-use environments, with cloud providers handling setup, maintenance, and scaling. This update frees users to focus on optimizing models while storing metadata for ML tools. A webinar by cloud solutions architect Panu Koskela explores Slurm and Kubernetes for ML, discussing their architectures and considerations for platform choice. Users can now monitor GPU usage on virtual machines via dashboards to manage resources and detect anomalies, and a new guide details setting up GlusterFS distributed storage for scalable model training. Strategies for efficient large model checkpointing are also shared. Nebius AI is at the forefront of adopting NVIDIA B200 Tensor Core GPUs, emphasizing energy efficiency. Additionally, a new blog category on AI research kicks off with discussions on alternatives to transformers in large language models.

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