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Responsible AI
Generative AI built with enterprise grade security & flexibility eliminating bias

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At SambaNova, we believe that generative AI must be applied fairly and transparently, while ensuring security and data privacy. Samba-1, the first trillion parameter model built for the enterprise and government organizations, was designed to ensure that these standards of Responsible AI are always applied.

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Samba-1

As a Composition of Experts, Samba-1 is an aggregation of multiple different models that delivers greater performance and accuracy than other models at a fraction of the inference costs.

Fairness

If one of the models in Samba-1 is found to have been trained on biased data, it can be easily replaced with one that has not.

Security

Deployed as an on-premises solution that is fully contained within your data center, including air gapped environments or other location of your choosing, the SambaNova platform is the most secure generative AI solution available. 

Model Ownership

With SambaNova, the customer owns the model, so they always have control over their models and data, eliminating vendor lock in.

Open Source

Stay up to date with the latest open source models and have insight into the model weights and training data which closed models do not offer.

Data Privacy

Samba-1 is the only generative AI platform that enables role based access controls, so you are always in control of your data and how results are delivered.

Sovereignty

SambaNova delivers a true full stack platform, so your generative AI infrastructure is completely in your control and in your environment.

Complete Solution

The first full stack generative AI solution combined with a revolutionary Composition of Experts model, the SambaNova platform uniquely enables customers to understand how their solution is built, reduce bias, and maintain complete control of their environment while enjoying absolute security and data privacy.

Ready to see more?

Schedule a meeting to see how SambaNova can help advance your Responsible AI initiatives.