Welcome to the Kalavai Developer Docs — your guide to building, training, and deploying AI workloads on Kalavai’s distributed compute platform.
Kalavai leverages spare data center capacity to deliver flexible, cost-effective compute for machine learning, AI inference, and large model hosting.
What is the Kalavai Platform
Kalavai Platform is a managed computing platform that simplifies access to GPU compute and LLM hosting. It builds on our open-source orchestration library, integrating directly with tools you already use — like Ray and JupyterHub — to provide on-demand distributed compute for AI workloads.
Efficient cost and low infrastructure overhead
Our platform abstracts the complexity of provisioning and managing GPU clusters, while optimizing performance and cost through dynamic utilization of spare capacity. When using the Kalavai Platform, users have direct access to a large fleet of data centre level GPUs at the lowest price in the market.
| Product | Description |
|---|---|
| Managed GPU Clusters | Spin up distributed Ray and LocalAI clusters for training, hyperparameter tuning, reinforcement learning and custom workloads. |
| Async Inference & LLM queue | Affordable LLM inference for large scale intelligence projects |
| Model fine tuning | Easily customise LLM to your data |
Beta Tester Program
We’re currently in Beta, and inviting developers and research teams to get early access to Kalavai. We're seeking developers who have hands-on experience with one or more of the following frameworks to participate in our beta testing program: Ray, Unsloth, Axolotl, LocalAI Unsloth Studio or GPUStack.
👉 Join the Beta Tester Program to get started.
Join our exclusive Discord community for beta testers.
What next?
Head over to the Getting started guide and get going with AI deployments!