Clusters are the primary compute environment developers interact with — used for:
- Direct SSH access and interactive workloads
- Jupyter and IDE-based development
- Inference endpoint hosting
- Batch and long-running GPU workloads
- Experimentation and model fine-tuning
Allocated Machines section
Use this section to understand how much capacity is available before creating a new cluster.
Cluster list fields
Cluster lifecycle states
Sold Out state
When all GPU capacity on a machine has been allocated, the machine shows as Sold Out in the Allocated Machines section. No additional clusters can be created from that machine until capacity is freed. If your organization needs additional GPU capacity, contact Acasia.Creating a cluster
1
Review available capacity
Check the Allocated Machines section to confirm GPUs are available before proceeding.
2
Click Create Cluster
Select the machine to allocate from and configure the cluster resource allocation — GPU count, CPU, RAM, and storage.
3
Wait for Active status
New clusters enter Provisioning state. Refresh the page and wait for the status badge to show Active.
4
Attach an SSH key
Before connecting, ensure a public SSH key is attached to the cluster. Navigate to Settings → SSH Keys to add a reusable key.
Cluster actions
From the cluster list, you can:- Connect via SSH — Open the SSH connection panel for the cluster
- Deploy inference endpoint — Navigate directly to endpoint deployment for the cluster
- View details — Inspect full hardware specifications and connection information
- Terminate — Stop the cluster and release its resources
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