Working with large language models brings real challenges. Sensitive prompts can leak to third parties. Managing GPU infrastructure for training or fine-tuning is complex and resource-heavy.

Zerve addresses both.

It allows data scientists to run code on serverless GPUs without managing infrastructure. GPU workloads can be orchestrated alongside other compute types within the same project. Use GPUs only where needed. Skip them where they’re not. That reduces both compute costs and DevOps load.

You can import open-source LLMs and generative models into your own environment. Once imported, models are fully hosted and accessible. Prompts, variables, and data persist within Zerve’s canvas. There’s no need to send prompts to third-party services, which reduces the risk of leaking sensitive IP.

With models hosted privately in your cloud environment, you can fine-tune them with your own data and enhance prompts using retrieval-augmented generation. This gives your team more control over output quality.

 

Zerve also streamlines deployment. Authentication is built in, and access controls are handled automatically so only authorized users can access models and outputs. Versioned deployments are supported through Git, making it easy to iterate and improve without disrupting what’s already in production. Models can be served over HTTPS and hosted under your own custom domain.

Getting started is simple. Zerve includes template code for common tasks and provides access to a wide range of open-source models and datasets.

These capabilities help reduce time to generative AI while increasing control, privacy, and flexibility.