Train and serve large AI models at scale with a single command. Point to your S3 bucket and go. We handle the rest — orchestration, efficiency, node failures, infrastructure. Simple and scalable.
Stay on the cutting edge with our latest recipes, techniques, and foundation models. Developed and rigorously tested by our research team.
Train advanced AI models in any cloud environment with complete data privacy, enterprise-grade security and full model ownership. Start in one cloud, continue on another — without skipping a beat.
Own the model that's trained on your own data. Introspect and better explain the model decisions. Filter the content and data based on your business needs.
Seamlessly integrate with your existing data pipelines, experiment trackers, and other tools. We are fully interoperable, cloud agnostic, and enterprise proven.
Run more experiments in less time with our world-leading efficiency optimizations. We’ve solved the hard engineering, systems, and research problems for you. Train and deploy with confidence that no performance was left behind.
Choose just the pieces you need from our modular training stack. Modify our starter code however you want. Our unopinionated tools make it easier, not harder, to implement your ideas.
With the release of PyTorch 2.0 and ROCm 5.4, we are excited to announce that LLM training works out of the box on AMD MI250 accelerators with zero code changes and at high performance!
Interview with Sharon Zhang, the CTO & co-founder of Personal AI, on why she was inspired to develop personalized AI models for individual users.
Together with Databricks, we can bring our customers and community to the forefront of AI faster than ever before.
Introducing MPT-30B, a new, more powerful member of our Foundation Series of open-source models, trained with an 8k context length on NVIDIA H100 Tensor Core GPUs.
Replit shares how they trained their code generation LLMs on the MosaicML platform.
Introducing MPT-7B, the first entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k.
They achieved astonishing results in their first MLPerf publication, beating NVIDIA’s optimized model by 17%, and the unoptimized model by 4.5x.
Packaging many algorithmic speedups in an easy-to-use API is quite a nice product.
"Using the MosaicML platform, we were able to train and deploy our Ghostwriter 2.7B LLM for code generation with our own data within a week and achieve leading results."
MosaicML researchers train large-scale vision and language models across multiple GPUs and nodes every single day. They understand how scalable research pipelines should be constructed.