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Mostly to boost research and commercial usage around JAX/Gemini is my read. Any internal research using Gemma is now more easily externally reproducible, exter

• gaogao 3y ago
  There's also less of a special sauce for text models itself these days with the propietary being more on the pre-training data and training stack (e.g. how to get 10k GPUs/TPUs running together smoothly). Multi-modal models (or adjacent like Sora) are less likely to be open sourced in the immediate term.
  • smarterclayton 3y ago
    There is a lot of work to make the actual infrastructure and lower level management of lots and lots of GPUs/TPUs open as well - my team focuses on making the infrastructure bit at least a bit more approachable on GKE and Kubernetes. https://github.com/GoogleCloudPlatform/ai-on-gke/tree/main https://github.com/GoogleCloudPlatform/ai-on-gke/tree/main and https://github.com/google/xpk https://github.com/google/xpk (a bit more focused on HPC, but includes AI) and https://github.com/stas00/ml-engineering https://github.com/stas00/ml-engineering (not associated with GKE, but describes training with SLURM) The actual training is still a bit of a small pool of very experienced people, but it's getting better. And every day serving models gets that much faster - you can often simply draft on Triton and TensorRT-LLM or vLLM and see significant wins month to month.