h2o-llmstudio

H2O LLM Studio is a no‑code graphical interface that lets you fine‑tune large language models on infrastructure that you own, ranging from a private cloud to a fully air‑gapped data center. The studio sits inside H2O.ai's Sovereign AI ecosystem, which is marketed as a platform where models, data, and results stay within the customer's walls. If you need a tool that keeps all AI workloads on‑prem or in a restricted environment, H2O LLM Studio offers the visual workflow you can use out of the box.

A typical use of the studio begins by selecting a base language model from a list of vendors. After uploading a training corpus, text files or CSV records that contain the examples you want the model to learn, the interface presents a set of train‑time options: number of epochs, learning rate, batch size, and token limit. The tool runs the finetuning job on the local or on‑premises compute node you specify. When training finishes, the studio exports a model file in the same format that H2O's inference engine understands. That model can then be plugged into the H2O Super Agent, which manages the whole system of predictive, generative, agentic, and observability services. The platform guarantees that no data leaves the environment, that every decision can be traced, and that the AI system is auditable end‑to‑end.

Who it is for: organizations that must maintain strict data sovereignty, such as banks, federal agencies, and healthcare providers, benefit because model training and inference never exit the organization's infrastructure. The tool is also for data scientists looking for a visual workflow that eliminates the need to write code for basic finetuning steps. It may not suit those who prefer to script training pipelines or who need a fully cloud‑native, multi‑tenant SaaS solution backed by a public cloud provider.

What the listing does not say: the site does not disclose the exact licensing cost or provide a public pricing table; it only mentions an annual license based on compute and memory. It also does not detail the supported hardware accelerators or the software dependencies required to run the studio on a local server.

Key features

• No‑code UI for selecting base models and training parameters
• Model‑agnostic finetuning that accepts plain text or CSV inputs