Natural-language deployment planning
Describe the model, budget, latency target, and risk tolerance. Crucible turns that into a concrete GPU deployment plan with a provider, accelerator, price estimate, and uncertainty notes.
Switching pages
crucible compute
A private GPU deployment backend for personal agents. Plan it, approve it, ship it.
Crucible workflow
what it does
Crucible Compute is a private GPU deployment backend for personal agents. It lets an agent safely plan, price, approve, launch, monitor, and stop model-serving workloads without handing the agent unchecked cloud-spend authority.
The workflow centers on deploying a model through the cheapest viable GPU route, showing the recommendation, the approval checkpoint, provider status, endpoint health, logs, and the context the agent used to make the decision.
Provider routing across GPU clouds
The control plane compares Modal, SkyPilot, Lambda Cloud, CoreWeave, Prime Intellect, Vast.ai, and Vultr so an agent can pick the cheapest viable path without pretending every provider is live.
Human approval before paid launch
Personal agents can plan and prepare deployments, but paid GPU provisioning stops at an approval gate. Nothing expensive launches just because a prompt sounded confident.
OpenAI-compatible model endpoints
Approved deployments expose familiar /v1/models and /v1/chat/completions routes, plus logs, health checks, benchmark data, and stop controls.
Deployment path
From prompt to endpoint, with the risky step gated.
01
Ask for a deployment in plain English.
02
Crucible profiles the model and pulls relevant Nia context.
03
The broker recommends a GPU provider and accelerator.
04
A human approves the paid launch.
05
The system provisions, verifies health, and exposes an endpoint.
What operators can use
- Dashboard
- Active deployments, endpoint console, provider status, and context snippets.
- Planner
- Prompt, model ID, objective, stop policy, recommended GPU, price estimate, and approval request.
- Providers
- Honest live, configured, dry-run, failed, and unsupported capability states.
- Agent API
- Token-based access so outside agents can request plans and inspect deployments.
Why it matters
agent-safe gpu ops
Private by default
Sessions, approvals, deployment records, logs, health checks, and benchmark traces are persisted locally for the prototype instead of depending on a hosted account system.
Built for controlled autonomy
The agent can gather context and make a recommendation, but the approval boundary keeps cloud credentials, spend, and stop controls visible to the operator.