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Model Factory

Deploy your own model factory. Work with us to implement expert-led post-training or do it yourself.

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AOverview

Deploy your own model factory.

01
Train
Train specialist models for your tasks.
Start Training
02
Deploy
Deploy the specialist model you trained.
Deploy a Model
03
Improve
Turn real-world usage into better models.
Explore Traces

1Train

Train. Specialist models, scored against real outcomes.

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Hosted Training

Train with asynchronous RL on hosted compute, with rollouts running in isolated sandboxes.

Start Training

1.1Train on 2,500+ RL environments
1.2Log runs to Weights & Biases
1.3Direct support from our research team

Evaluations

12345

TasksA conversation between User and Assistant. The user asks a question, and the Assistant solves it.A conversation between User and Assistant. The user asks a question, and the Assistant solves it.A conversation between User and Assistant. The user asks a question, and the Assistant solves it.A conversation between User and Assistant. The user asks a question, and the Assistant solves it.A conversation between User and Assistant. The user asks a question, and the Assistant solves it.

{"index": "500", "answer": "[2, 3, 4, 7]"}{"index": "500", "answer": "[2, 3, 4, 7]"}{"index": "500", "answer": "[2, 3, 4, 7]"}{"index": "500", "answer": "[2, 3, 4, 7]"}{"index": "500", "answer": "[2, 3, 4, 7]"}

0.000.000.000.000.00

primeintellect/logic-env

0.2.17primeintellect

Evaluate

arcee-ai/ifeval
0.1.1arcee-ai
hud/hud-text-2048
0.1.1hud
aarush/vf-openbench
0.1.1aarush

Run hosted evaluations of open and frontier models on your environment.

Run an Evaluation

1.4100+ open-source models
1.5No infra, no setup
1.6Public leaderboard

Observability

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

Explore Traces

1.7Reward broken down by rubric function
1.8Spot reward hacking before it ships
1.9Search, download and query every trace by API

SandboxesNew

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

Explore Sandboxes

1.10Secure code execution for every rollout
1.11Wired into Verifiers environments
1.12Standard or custom images

Environments

Turn your task into an environment using our open-source library. Score against real outcomes.

Find Environments

1.13Open-source Verifiers library
1.14Rubrics that score real outcomes
1.15Or start from 2,500+ community environments on the Hub

2Deploy

Deploy. Specialist models, scored against real outcomes.

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Dedicated deployments

support-triageqwen3-4bRunning182 ms

invoice-extractqwen3-8bRunning240 ms

code-reviewglm-5.3Scaling410 ms

search-rerankqwen3-1.7bRunning96 ms

claims-intakedeepseek-v4.1Running305 ms

ticket-summaryqwen3-4bPaused—

deployment · support-triageDedicated

Model: qwen3-4b:support-triage-400
Hardware: H100 · 80 GB
Replicas: 2 – 8 · autoscaling
Region: us-central
Routing: Private

Requests / min1,240

p50 latency182 ms

Error rate0.02%

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

Deploy a Model

2.1Latency and reliability targets you set
2.2Private routing for your traffic
2.3Capacity sized with our engineers

Serverless API

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

Explore Models

2.4Prime Hosted: open models running on Prime’s GPUs
2.5Prime Gateway: models from other providers, through the same API key
2.6Pay per token, nothing to deploy

3Improve

Improve. Convert production traces into your next environments and evals.

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Phase 1

Collect traces

Understand how your models perform on real tasks. Capture failures and feedback from real usage.

Explore Traces

Phase 2

Build environments

Turn production traces into environments built from your real tasks, with rewards that score them.

Find Environments

Phase 3

Improve the model

Train on your environments and check every new model for gains and regressions before deploying.

Start Training

Work with our researchers

For teams with a high-value workflow and no time to become an RL lab.

Talk to our team

Ramp trained FastAsk with Prime Intellect Lab: a specialist subagent for financial spreadsheet retrieval.

Outcomes

27%faster than Claude Opus 4.6
4%more accurate than Claude Opus 4.6
35Bparameter FastAsk model

Read case study

Zapier built AutomationBench on Prime Intellect Lab to evaluate agents on realistic multi-step automation workflows, catch reward-hacking behavior in live metrics, and turn evals into RL training environments.

Outcomes

9,000+ appsreal-world automation surface area
600 taskstraining to battle test complex agentic workflows
1 commandfrom eval benchmark to RL training run

Prime Intellect and NVIDIA built 2,500 verified enterprise tasks to train Nemotron 3.5 Super VL on long-horizon enterprise workflows.

Outcomes

34.0% -> 57.7%AutomationBench-AA, public split
2,500 tasksverified enterprise workflows
~11 hoursof GRPO training on 128 GB200 GPUs

Talk to our Team

Our researchers run the loop with you.

Your data. Your model. Your edge. Built for the most security-conscious teams in AI, enterprise and the public sector.

Data never leaves your perimeter: Choose serverless or a VPC deployment based on the control you need.
Full access and audit: Role-based access control, audit logs on every dispatch, lifecycle automation for checkpoints, datasets and artifacts.
You own the intelligence: The model is yours. You decide what trains, what deploys and what memory is retained. Opt out of training any time.