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.

FAQs

How does Zerve simplify GPU usage for large language models?

Zerve allows data scientists to run GPU workloads on serverless infrastructure without managing hardware. GPUs are used only when needed, reducing both costs and operational overhead.

Can Zerve host open-source LLMs securely?

Yes. Once imported, open-source models are fully hosted within your own environment, keeping prompts, variables, and data secure without relying on third-party services.

How does Zerve support fine-tuning and customization?

Zerve enables fine-tuning with your own data and supports retrieval-augmented generation, giving teams more control over model accuracy and output quality.

What deployment features are available in Zerve?

Zerve includes built-in authentication, access control, versioned Git-based deployments, and HTTPS hosting under custom domains for seamless deployment and management.

Why is Zerve a safer option for working with sensitive prompts?

Because all models, prompts, and outputs remain in your environment, Zerve reduces the risk of leaking sensitive IP compared to third-party hosted services.

How easy is it to get started with Zerve for generative AI?

Zerve provides template code, preloaded access to open-source models, and integration with datasets, making it quick to launch generative AI projects.