# Prime Intellect — Own Your Intelligence

Train and deploy custom AI models on an integrated compute, training, and inference stack.

Install the CLI: `uv tool install prime`

## Deploy your own model factory.

Build and deploy your own custom models on one platform across post-training, inference, and compute.

## train

Train specialist models for your tasks.

Fine-tune with SFT, then reinforce with RL against rewards you define from real outcomes, on open models of any size.

### Hosted Training

Train models with reinforcement learning on managed Prime-RL, our open-source trainer, using hosted compute and sandboxes.

[Start Training](https://app.primeintellect.ai/)

### Evaluations

Create hosted evaluations for you to benchmark the performance of your models.

[Run an Evaluation](https://app.primeintellect.ai/dashboard/evaluations)

### Observability

Capture, view, and annotate every trace from your evals and training runs.

[Explore Traces](https://docs.primeintellect.ai/traces/overview)

### Sandboxes

Scale thousands of microVM sandboxes with native Docker and full RL support.

## Deploy

Deploy the specialist model you trained with Model Factory.

Flexible, production-grade inference in seconds. Optimize your deployment and promote checkpoints to production.

### Dedicated deployments

Production-grade capacity optimized for your usage, private routing, latency, reliability, and custom model requirements.

[Deploy a Model](https://app.primeintellect.ai/dashboard/inference/dedicated)

### Serverless API

Call frontier open models per token through an OpenAI-compatible API, with nothing to deploy or manage.

[Explore Models](https://app.primeintellect.ai/dashboard/inference/serverless)

## Improve

Turn real-world usage into better models.

Every model you ship makes the next one better.

## Prime Compute.

Your own dedicated cluster, reserved for you and operated by our team.

The latest GPUs, on demand or as a dedicated cluster: spin it up over API, no cluster plumbing.

Run training and inference on on-demand GPUs or dedicated clusters, with managed orchestration and support.

### Scale on the latest hardware

Choose GPU capacity for your training and inference workloads.

### SLURM + K8s orchestration

Dynamic workloads with enterprise-grade scheduling and container automation

### InfiniBand networking

High-bandwidth interconnects across nodes for distributed training.

### Self-healing clusters

Failing nodes are detected, drained, and replaced automatically.

### Grafana monitoring dashboards

Visualize metrics in real time with customizable dashboards for full system observability.

### 24/7 support and observability

Engineers on call around the clock, who spot problems on your cluster before you do.

### Liquid compute

Redeploy unused capacity as on-demand or spot compute across the Prime network, and reclaim it when you need it.

[Discover](https://app.primeintellect.ai/dashboard/quotes)

### On-Demand

Not ready to scale yet? Spin up capacity by the hour for experiments, evals, and short runs, with no contract.

[Launch a GPU](https://app.primeintellect.ai/dashboard/on-demand-gpus)

## Prime Inference.

Access leading open-source models.

Serve GLM, Qwen, INTELLECT and more through one OpenAI-compatible API. Deploy your fine-tuned checkpoints next to them.

Serve open-source and custom models through the same stack that trains them.

## Get started

1. Create a [Prime Intellect account](https://app.primeintellect.ai) and choose your personal or team workspace.
2. Install [uv](https://docs.astral.sh/uv/getting-started/installation/), then install and authenticate the CLI:

```bash
uv tool install prime
prime login
prime config view
```

Update an existing installation with `uv tool upgrade prime`. Use `prime --help` and each subcommand's `--help` for your installed version. See the [CLI reference](https://docs.primeintellect.ai/cli-reference/introduction.md) and [configuration guide](https://docs.primeintellect.ai/cli-reference/config-cli.md).

3. Create a scoped, expiring [API key](https://docs.primeintellect.ai/api-reference/api-keys.md) in [Settings → API Keys](https://app.primeintellect.ai/dashboard/tokens). Grant only the product permissions you need. Store credentials in server-side environment variables or a secret manager, never browser code, public repositories or trace content.
4. Confirm the workspace and billing account before creating resources. [Team configuration](https://docs.primeintellect.ai/cli-reference/teams.md) and [billing dashboard](https://app.primeintellect.ai/dashboard/billing) help you check ownership and credit balance.

CLI team configuration does not automatically select a team for direct inference HTTP/SDK requests: include `X-Prime-Team-ID` for team inference billing. Traces use their own documented workspace selection, including `PRIME_TEAM_ID`. Do not assume all products use the same team header or environment variable.

## Choose your first workload

| Goal | Start here | What you need |
| --- | --- | --- |
| Call a model | [Inference quickstart](https://docs.primeintellect.ai/inference/overview.md) | Inference permission, funded account, a current model ID |
| Train a specialist model | [Hosted Training quickstart](https://docs.primeintellect.ai/hosted-training/getting-started.md) | Beta access, Python 3.10+, funded wallet, environment/config and checkpoint storage |
| Test a model or agent | [Hosted evaluations](https://docs.primeintellect.ai/tutorials-environments/hosted-evaluations.md) | An environment and model endpoint |
| Build an RL environment | [Environment quickstart](https://docs.primeintellect.ai/tutorials-environments/getting-started.md) | CLI login and a profile username |
| Run isolated code | [Sandbox quickstart](https://docs.primeintellect.ai/sandboxes/overview.md) | Sandbox permissions and a container image |
| Rent GPUs | [GPU provisioning guide](https://docs.primeintellect.ai/cli-reference/provision-gpu.md) | Current availability, billing and SSH configuration |
| Run your own training stack | [prime-rl](https://docs.primeintellect.ai/prime-rl/overview.md) | Compatible GPU hardware and framework configuration |
| Inspect training or agent traces | [Prime Traces](https://docs.primeintellect.ai/traces/overview.md) | Traces permissions, correct workspace and supported records |

## Documentation for agents

Read the [platform overview](https://docs.primeintellect.ai/platform-overview.md) for how the products fit together. Fetch the [complete documentation index](https://docs.primeintellect.ai/llms.txt) to discover current guides and API endpoints. Documentation pages are available as Markdown by appending `.md`; the [full documentation export](https://docs.primeintellect.ai/llms-full.txt) is available when you need the whole reference, but the index and task-specific pages are usually more efficient.

These website pages summarize products and route you to implementation guides. Use the live docs, model catalog and availability APIs for current schemas, supported models, access requirements, quotas and prices. Do not infer API endpoints from marketing-page URLs.


## Open-source tools

- [Prime CLI and Python SDKs](https://github.com/PrimeIntellect-ai/prime): platform clients and runnable examples.
- [Verifiers](https://github.com/PrimeIntellect-ai/verifiers): environments and evaluations; start with the [overview](https://docs.primeintellect.ai/verifiers/overview.md) and [v1 quickstart](https://docs.primeintellect.ai/verifiers/v1/getting_started.md).
- [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl): self-hosted asynchronous RL; read [configuration](https://docs.primeintellect.ai/prime-rl/configuration.md), [training](https://docs.primeintellect.ai/prime-rl/training.md) and [scaling](https://docs.primeintellect.ai/prime-rl/scaling.md).
- [Renderers](https://github.com/PrimeIntellect-ai/renderers): model-specific message/token rendering for training workflows.

Use the documentation matching your installed version. Hosted services and self-hosted libraries have different access, deployment and billing requirements.

## Before an agent takes action

Confirm the user's workspace, region/data constraints, budget and intended resource lifetime before launching paid jobs. Never silently create resources, upload private datasets/traces, publish an endpoint or delete retained storage. Keep credentials out of logs and traces. Verify job status and saved outputs; an accepted API request is not proof that a job finished. On failure, inspect the documented error and current state before retrying a write. Clean up compute explicitly, and confirm separately whether persistent storage should remain.

## Updates, support and company information

- [Product changelog in Markdown](/changelog.md) or [on the website](/changelog): dated releases and product updates. Use live documentation for current behavior and pricing.
- [Prime CLI/SDK releases](https://github.com/PrimeIntellect-ai/prime/releases)
- [Verifiers releases](https://github.com/PrimeIntellect-ai/verifiers/releases)
- [prime-rl releases](https://github.com/PrimeIntellect-ai/prime-rl/releases)
- [Blog and research](/blog) and [case studies](/case-study)
- [Contact sales/support](/contact) or [email contact@primeintellect.ai](mailto:contact@primeintellect.ai) for beta access, dedicated deployments and higher limits
- [Security](/security), [Privacy Policy](/privacy-policy) and [Platform Terms](/terms-of-service)
- [Careers](/careers)


## Explore Prime Intellect

- [Model Factory](/model-factory.md)
- [Compute](/compute.md)
- [Inference](/inference.md)
- [Documentation](https://docs.primeintellect.ai)
- [Changelog](/changelog.md)
- [Blog and research](/blog)
- [Case studies](/case-study)
- [Careers](/careers)
- [Contact](/contact)
- [Security](/security)
- [Get started with Prime Intellect](/llms.txt)