> ## Documentation Index > Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt > Use this file to discover all available pages before exploring further. # Platform Overview > How environments, training, evaluation, and inference fit together on Prime Intellect Prime Intellect is an open research platform for post-training. It unifies the [Environments Hub](/tutorials-environments/environments), [Hosted Training](/hosted-training/getting-started), and [Hosted Evaluations](/tutorials-environments/hosted-evaluations) into a full-stack platform for reinforcement learning research and optimization. The platform enables the entire lifecycle of post-training research — from large-scale agentic RL, to inference and evaluation — without needing to worry about the costs of massive GPU clusters or the details of low-level algorithm implementation. ## Core Concepts ### Environments The platform is built around **environments**, which contain everything needed to run a model on a task: * A **dataset** of tasks (input prompts with optional ground-truth answers) * A **harness** for the model (tools, sandboxes, context management, multi-turn interactions) * A **rubric** to score the model's performance (reward functions, metrics) Environments are self-contained Python modules built with the [verifiers](https://github.com/PrimeIntellect-ai/verifiers) library. They can be used to train models with reinforcement learning, evaluate capabilities, generate synthetic data, optimize prompts, experiment with agent harnesses, and more. See [The Environment Model](/hosted-training/environment-model) for a detailed breakdown of environment types and the rollout loop. ### Environments Hub The [Environments Hub](https://app.primeintellect.ai/dashboard/environments) is a community registry where you can browse and deploy open-source environments, or create and share your own. Environments support running evaluations with any OpenAI-compatible model endpoint, or training any supported open-weights model via Hosted Training. The hub includes a range of pre-built environments spanning math, coding, games, search and tool use, multimodal tasks, and more — as well as validated implementations of popular benchmarks like AIME, MATH-500, and Humanity's Last Exam. ### Model Factory **Model Factory** is the training side of the platform, built on the open-source [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) training library. It covers two things: * **[Hosted Training](/hosted-training/getting-started)** — submit a config and we run it on GPUs we operate, on-demand and [billed per GPU-hour](/hosted-training/billing). Full-parameter RL and SFT, no cluster to reserve. * **[Model Factory Clusters](/model-factory/clusters)** — a dedicated cluster running training, inference, Slurm and Kubernetes side by side, for when you need control over the stack. The earlier shared multi-tenant LoRA service has been [retired](/hosted-training/shared-lora-retired). ### Hosted Evaluations Run evaluations against any environment directly from the web UI or CLI, using Prime Inference or your own OpenAI-compatible endpoints. Results are saved and viewable in the terminal UI or on the dashboard. ## Architecture Training is powered by the [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) framework. The three key components are: * **Trainer** — Consumes batches and updates model weights * **Inference** — Serves the model via an OpenAI-compatible API with live weight updating * **Orchestrator** — Manages environment logic, schedules rollout requests, and coordinates the training loop Each Hosted Training run gets its own trainer, inference, and orchestrator pods, sized by the `[deployment]` block in your config. ## What You Can Do | Capability | Description | | - | - | | **RL Training** | Train open-weights models with reinforcement learning on your custom environments | | **Evaluation** | Benchmark any OpenAI-compatible model against environments on the Hub | | **Environment Development** | Build, test, and share environments using the verifiers library | | **Synthetic Data Generation** | Generate rollout data for downstream use | ## Key Libraries The platform integrates with two core open-source libraries: **[verifiers](https://github.com/PrimeIntellect-ai/verifiers)** — The environment framework. Provides building blocks for datasets, rubrics, tools, sandboxes, and multi-turn interactions. Environments built with verifiers are portable across local evaluation, hosted evaluation, and Hosted Training. **[prime-rl](https://github.com/PrimeIntellect-ai/prime-rl)** — The training framework. Supports fully asynchronous distributed RL at scale with FSDP2 training and vLLM inference. Used both for self-hosted training on your own GPUs and as the backend for Hosted Training. ## Getting Started 1. [Create a Prime Intellect account](https://app.primeintellect.ai) 2. Install the Prime CLI: `uv tool install prime` 3. Authenticate: `prime login` 4. See what you can run: `prime train models` Launch your first on-demand Hosted Training run. Walk through a complete Hosted Training run. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.