Train and fine-tune.
Build a model with your data or adapt an existing one to the task.
GPU compute for training and inference.
Bare-metal capacity for training, fine-tuning and inference. Your team runs the models and software; Origon provides the infrastructure.
Workloads
Train a model. Put it to work.
Your team runs the training and serving software that fits the application.
Serve your models.
Serve model responses to your application through the software your team chooses.
Reservation
GPU capacity matched to your workload.
Model size, memory needs and expected load guide the hardware configuration.
The reservation sets the allocation, commercial terms and delivery timing. Availability is confirmed for the configuration and capacity you need.
- Infrastructure
- Bare-metal NVIDIA GPUs on Origon infrastructure
- Workloads
- Training, fine-tuning, inference, and model serving
- Models and serving software
- Run your own
- Allocation
- Reserved per customer
- Billing
- Hourly
AI Datastore
Give your application knowledge and memory.
Add AI Datastore when your application needs to retrieve knowledge or remember earlier work. Connect through S3-compatible APIs and a native MCP endpoint.
Explore AI DatastoreEvaluation
See how your workload performs.
Test the proposed configuration with your models and software, including the memory, storage and networking the application needs.
Use the same workload and conditions when comparing results.
Explore AI Cloud.
Bring your next workload.
Talk through what you plan to build and the compute it needs.