Radiant AI Cloud Model Registry
Radiant AI Cloud Model Registry is a central place to manage, version and prepare custom AI models for deployment.
What Radiant AI Cloud Model Registry is
Radiant AI Cloud Model Registry is a central place to manage, version and prepare custom AI models for deployment. Users upload model weights through the RAIC CLI, with supported files including .safetensors, .json, .md, .txt, .py, .gitattributes, .yml and LICENSE. Each organization starts with a default registry and can create more, choosing where model weights are stored. When adding a model, users can select multiple preferred GPU types for matching with compatible GPUs at the registry location. Versions progress through Uploaded and Synchronizing before becoming Available for deployment to Endpoints. Completed fine-tuning jobs automatically create registry models, add checkpoint versions and make them available for deployment. Fine-tuning supports public Hugging Face datasets and custom .jsonl or .csv datasets with prompt and completion columns; RAIC uses LoRA and enables FlashAttention-2 during training. The platform supports model versioning, deployment tracking and artifact storage. Documentation describes optional authenticator-app MFA. Radiant Infrastructure Ltd says it undergoes annual ISO 27001, SOC 2 and HIPAA audits. Support resources include documentation, community discussions, a status page and support case portal.
Good
- Supports model versioning and deployment tracking.
- Users can choose registry locations for model weights.
- Completed fine-tuning jobs add deployable checkpoints.
- Supports several model weight file types.
- Optional MFA is documented.
Watch out
- Model weights are uploaded through the RAIC CLI.
- Listed platforms include no mobile platform.
Who it suits
It suits teams managing custom model artifacts and versions before deployment, particularly those using RAIC fine-tuning workflows. It may also suit organizations that want GPU selection and registry storage choices.
Verdict
The registry connects model storage and version tracking with RAIC fine-tuning outputs and deployment readiness. Teams should check that its CLI-based upload process and supported workflow match their needs.
The facts
Each one read from Radiant AI Cloud Model Registry's own pages, with the date it was checked.
What it does
- Compliance
- Radiant Infrastructure Ltd says it undergoes annual ISO 27001, SOC 2, and HIPAA audits; its listed public-cloud data centers are marked SOC 2 or SOC 2/ISO 27001.checked 4 Oct 2026
- Dataset support
- The fine-tuning guide supports publicly available Hugging Face datasets and custom datasets in .jsonl or .csv format with prompt and completion columns.checked 4 Oct 2026
- Fine-tuning integration
- Completed fine-tuning jobs automatically create a registry model, populate it with checkpoint versions, and make those versions available for deployment.checked 4 Oct 2026
- Fine-tuning methods
- RAIC uses Low-Rank Adaptation (LoRA) for fine-tuning and enables FlashAttention-2 during training.checked 4 Oct 2026
- GPU selection
- When creating a model, users can select multiple preferred GPU types so the platform can match it to compatible GPUs available in the registry location.checked 4 Oct 2026
- Model uploads
- Users can upload model weights through the RAIC CLI.checked 4 Oct 2026
- Purpose
- Radiant AI Cloud’s Model Registry is a centralized repository for managing, versioning, and preparing custom AI models for deployment.checked 4 Oct 2026
- Registries
- Every new organization starts with a default registry, and users can create additional registries and choose where model weights are stored.checked 4 Oct 2026
- Security
- The maker’s documentation describes an optional MFA setting that prompts users for an authenticator-app code after login.checked 4 Oct 2026
- Support
- RAIC support resources include documentation, community discussions, a platform status page, and a support case portal.checked 4 Oct 2026
- Supported files
- Supported upload file types include .safetensors, .json, .md, .txt, .py, .gitattributes, .yml, and LICENSE.checked 4 Oct 2026
- Version status
- After upload, a model version progresses through Uploaded and Synchronizing to Available, at which point it is ready for deployment to Endpoints.checked 4 Oct 2026
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