Prime Intellect

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Open Superintelligence Stack

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Prime Intellect

Shorts

We're excited to introduce Arcee.ai's Trinity Large model. An open 400B parameter Mixture of Experts model, delivering frontier-level performance with only 13B active parameters. Trained in collaboration between Arcee, Datology and Prime Intellect.

This month, Poolside’s Laguna XS.2 is free to train on Prime Intellect Lab. First come, first serve while reserved capacity lasts.

Over the past months, Cohort I of our RL Residency has been shipping. Highlights - continual learning - automating AI research (from GPU programming to RL itself) - embodied environments - multi-agent systems - materials science discovery

Coming Soon...

Open superintelligence stack Early 2026

Videos

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Announcing our $130M Series A to build the Open Superintelligence Stack Led by Radical Ventures, with NVIDIA, Intel Capital, Dell Capital, and existing investors Train, deploy, and continuously improve your own models using our stack. Own your intelligence.

Introducing Lab: A full-stack platform for training your own agentic models Build, evaluate and train on your own environments at scale without managing the underlying infrastructure. Giving everyone their own frontier AI lab.

Introducing the Environments Hub RL environments are the key bottleneck to the next wave of AI progress, but big labs are locking them down We built a community platform for crowdsourcing open environments, so anyone can contribute to open-source AGI

Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack Achieving state-of-the-art performance for its size across math, code and reasoning Built using the same tools we put in your hands, from environments &amp; evals, RL frameworks, sandboxes &amp; more

Today, we are launching Hosted Evaluations on the platform. Running evals is an infra problem: harnesses, sandboxes, hours of compute, hundreds of parallel runs. Running evals is hard. Until now.

Announcing INTELLECT-1: the first-ever decentralized training of a 10B model Scaling decentralized training 10x beyond prior efforts. Anyone can join us to build open-source AGI 🦋

Releasing METAGENE-1: In collaboration with researchers from USC, we're open-sourcing a state-of-the-art 7B parameter Metagenomic Foundation Model. Enabling planetary-scale pathogen detection and reducing the risk of pandemics in the age of exponential biology.

Launching SYNTHETIC-2: our next-gen open reasoning dataset and planetary-scale synthetic data generation run. Powered by our P2P inference stack and DeepSeek-R1-0528, it verifies traces for the hardest RL tasks. Contribute towards AGI via open, permissionless compute.

Today we’re launching INTELLECT-2: The first decentralized 32B-parameter RL training run open to join for anyone with compute — fully permissionless. Scaling towards frontier reasoning across coding, math and science.

We worked with Ramp Labs to train Fast Ask using Lab A small RL-trained subagent that helps the Ramp Sheets agent find answers in spreadsheets. The resulting FastAsk model outperformed Opus 4.6, while obtaining Haiku-level speeds at even lower costs.

Prime Intellect Day Saturday — March 14 (Pi Day) 4-11pm — The Melody of SF Talks on frontier model training, agentic RL, computer use, coding agents, diffusion LLMs, automating AI &amp; science, robotics — and why the singularity will not be singular.

Today, we're laying the foundation to accelerate open & decentralized AI Introducing our protocol & testnet: A peer-to-peer compute and intelligence network. Enabling collective creation, ownership, and access of sovereign open-source AI Towards an open superintelligence future

Introducing SYNTHETIC-1: Collaboratively generating the largest synthetic dataset of verified reasoning traces for math, coding and science using DeepSeek-R1. Join us to contribute compute towards state-of-the-art open reasoning models.

Introducing OpenDiLoCo, an open-source implementation and scaling of DeepMind’s Distributed Low-Communication (DiLoCo) method, enabling globally distributed AI model training.

We are excited to share a preview of our peer-to-peer decentralized inference stack Engineered for consumer GPUs and high-latency networks — plus a research roadmap to scale it to a planetary-scale decentralized inference engine.

We're excited to support Arcee.ai's Trinity models A family of open base Mixture of Experts models pretrained in collaboration between Arcee, Datology, and Prime Intellect Releasing Trinity Nano (6B) and Mini (26B) today, with Trinity Large still in training

Our Inference API is live. Use it to evaluate environments on the Hub. + Simple `prime env eval` cmd + One endpoint for all models + 56 models and growing + Unified billing with compute + Viewer for rewards and rollouts + Share results on Hub

Our new website is live

Introducing INTELLECT-3: Scaling RL to a 100B+ MoE model on our end-to-end stack Achieving state-of-the-art performance for its size across math, code and reasoning Built using the same tools we put in your hands, from environments & evals, RL frameworks, sandboxes & more

Prime Intellect Day Saturday — March 14 (Pi Day) 4-11pm — The Melody of SF Talks on frontier model training, agentic RL, computer use, coding agents, diffusion LLMs, automating AI & science, robotics — and why the singularity will not be singular.