The AI Cloud Built for Abundance

Radiant offers a complete AI Cloud with GPU-as-a-Service and MLOps features like Inference, Fine-Tuning, Model Registry, Serverless Kubernetes, and high-performance Storage. Aligned with NVIDIA’s tech, it delivers full-stack performance from silicon to service. Built in-house with no dependencies, it emphasizes engineering principles for scalable performance.

Proven in production, it includes intelligent scheduling, automated node management, secure multi-tenancy, and a distributed control panel. Its lightweight design ensures consistent performance from 10K to 100K+ GPUs with full operational control.

Launch GPU Virtual Machines in Seconds

GPU Instances deliver top NVIDIA performance and cost-efficiency for developers, AI researchers, and startups. Launch pre-configured GPUs in seconds, pay by the minute, and choose from 40+ setups—from fractional to multi-GPU. Preloaded with OS, ML tools, and drivers, they cut infrastructure hassle. Suspend and resume idle workloads to save up to 80% versus traditional hyperscalers.

Abundance Isn’t a Vision. It’s Our Operating Model.

Our economic advantage is your economic advantage. Radiant’s advantages cover the cost of capital, the land, the cost of power and the scale of our relationships. We pass those advantages onto our clients and can be even more aggressive when scale and contract length are significant.

Deliver Real-Time Inference at Scale

At its core is the Inference Delivery Network (IDN) — an intelligent global routing and caching layer that minimizes latency and enforces governance. The IDN automatically distributes models to the optimal region, delivering cold starts in under five seconds and ensuring data stays within national borders when required.

The network powers two endpoint types: Serverless Endpoints, an “Inference-as-a-Service” model that abstracts hardware, scales from zero to peak, and bills per token; and Dedicated Endpoints, offering exclusive GPU infrastructure with strict isolation and per-minute billing for performance-sensitive workloads.

Fine-Tune Models with One Click

Fine-Tuning Studio turns the complex, resource-intensive process of model fine-tuning into a simple, integrated single-click workflow. It removes the need for manual orchestration or custom scripts - users choose a foundation model, upload data, set hyperparameters and launch. The automation engine manages provisioning, scheduling and retries using Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA to cut training time and cost. Advanced users retain full control over parameters and real-time monitoring. Tuned models are automatically versioned in the Model Registry and ready for one-click deployment to Inference Endpoints or Kubernetes.

Manage and Deploy Models Seamlessly

For AI teams, the Model Registry is the essential connective tissue that streamlines workflows from training to production. It serves as the central hub and single source of truth for the entire AI/ML lifecycle, resolving scattered model files and versioning challenges. It stores, versions, and deploys all custom AI models from one place with unique IDs and version tags, simplifying development, staging, and lineage tracking. Integrated with the broader platform, new models are automatically added from the Fine-Tuning Studio or uploaded via CLI. The Registry also enables global distribution—assigning hardware and region preferences (e.g., NVIDIA H200, London) and caching for low-latency inference. From there, any model version can be deployed directly to Serverless or Dedicated Endpoints with a single click.

Run Containers Effortlessly with Serverless Kubernetes

Serverless Kubernetes is the ideal solution for AI-native companies that want to free ML teams from infrastructure management while retaining seamless scalability. It’s a fully managed, auto-scaling platform that delivers the power of Kubernetes with the simplicity of serverless - completely abstracting away the underlying infrastructure. Developers no longer manage clusters, node pools, load balancers, or GPU scheduling. Existing container workflows run natively (including Helm charts), while the platform handles everything else - from sub-second cold starts to auto-scaling from zero to thousands of GPUs in real time.

Store and Serve Data Without Limits

For organizations building AI, reliable, high-performance storage is essential to keep compute clusters and inference endpoints fed. Cloud Storage provides a powerful, scalable, globally available solution for all object storage needs. Built on a familiar S3-compatible interface, it lets teams integrate existing tools, SDKs, and scripts with zero friction—offering seamless management of datasets, model weights, and experiment logs, with transparent, industry-leading pricing and no hidden ingress or egress fees.

Designed for AI, the platform supports object versioning to protect data from accidental changes and preserve dataset lineage. With a global footprint, Ori Cloud Storage keeps data close to where it’s processed, reducing latency for distributed training and maintaining compliance with data sovereignty requirements.

Simplicity is power

NEWCO’s AI Cloud is remarkably simple to use. Simple, however, is not easy and NEWCO has spent thousands of engineering and design hours ensuring that spinning up, expanding and managing a cluster of 10,000 GPUs is as easy as 10.