MosaicML enables you to easily train and deploy large AI models on your data, in your secure environment.
Build your next model.
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.
With a few simple steps, deploy inside your private cloud. Your data and models never leave your firewalls. 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.
Introducing MPT-7B, the latest 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.
We’re releasing a fully managed inference service to make deploying machine learning models as easy as possible. You can query off-the-shelf models with our Starter tier or securely deploy in-house models in your own environment with our Enterprise tier. By using MosaicML for both training and deployment, you can easily turn your data into production-grade AI services — often at a fraction of the cost of alternatives — without compromising data privacy.
In our previous blog post, we showed how we used the MosaicML platform, Streaming datasets, and the Composer library to train a Stable Diffusion model from scratch for less than $50,000. Now, we do a deep dive into the technical details behind this speedup, demonstrating how we were able to replicate the Stable Diffusion 2 base model in just 6.8 days.
We've replicated Stable Diffusion 2 for less than $50k, and we've open-sourced the training code so you can too! This is a 3x cost reduction from our last blog post and an 8x reduction from the original Stable Diffusion 2, making training large-scale diffusion models from scratch more accessible than ever before.
In this blog, we discuss how the architecture of the MosaicML platform enables you to easily train large-scale AI models on any cloud provider, while data remains secure on your own private network. Now, both startups and large enterprises can maintain maximum autonomy when training ML workloads.
The MosaicML platform is designed to tackle the challenges of training large models such as ChatGPT, LaMDA, and Stable Diffusion. Our blog post breaks down the difficulties of training such models, and shows how our platform makes training large AI models easier.
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.
Talk to our ML training experts and discover how MosaicML can help you on your ML journey.
Join us if you want to build world class ML training systems.
Open-source PyTorch library to plug and play speed-ups with just a few lines of code.
20+ speed-up methods for neural network training, rooted in our rigorous research.
Develop the best solutions to the most challenging problems in ML today.