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Prime Intellect

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Open Superintelligence Factory.

Shorts

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust. With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms. Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust. With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms. Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

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Prime Intellect has acquired Moondream We’re bringing Moondream’s open vision models and inference optimizations into our stack Train and deploy multimodal models across vision, video, voice, decision models and computer use

Prime Intellect has acquired Moondream We’re bringing Moondream’s open vision models and inference optimizations into our stack Train and deploy multimodal models across vision, video, voice, decision models and computer use

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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.

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.

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Coming Soon...

Coming Soon...

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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

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

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

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

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Open superintelligence stack Early 2026

Open superintelligence stack Early 2026

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Videos

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Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.

Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.

Prime Intellect

3,224,666 просмотров • 2 месяцев назад

Introducing Prime Inference: We've served trillions of tokens for RL and dedicated customer deployments To own your intelligence, you need to own your inference Unpacking our inference stack

Introducing Prime Inference: We've served trillions of tokens for RL and dedicated customer deployments To own your intelligence, you need to own your inference Unpacking our inference stack

Prime Intellect

223,706 просмотров • 16 дней назад

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.

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.

Prime Intellect

1,450,744 просмотров • 3 месяцев назад

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 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.

Prime Intellect

3,155,803 просмотров • 8 месяцев назад

Together with NVIDIA we post-trained Nemotron 3.5 Super VL on thousands of tasks Reflecting enterprise workflows in sales, operations, marketing, finance We improved the AutomationBench-AA score by ~70% on the public split

Together with NVIDIA we post-trained Nemotron 3.5 Super VL on thousands of tasks Reflecting enterprise workflows in sales, operations, marketing, finance We improved the AutomationBench-AA score by ~70% on the public split

Prime Intellect

19,203 просмотров • 3 дней назад

Introducing Prime Sandboxes: MicroVM sandboxes purpose-built for RL training. Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.

Introducing Prime Sandboxes: MicroVM sandboxes purpose-built for RL training. Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.

Prime Intellect

119,369 просмотров • 25 дней назад

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 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

Prime Intellect

1,819,967 просмотров • 1 год назад

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

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

1,152,207 просмотров • 10 месяцев назад

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 🦋

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 🦋

Prime Intellect

790,985 просмотров • 2 лет назад

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.

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.

Prime Intellect

624,503 просмотров • 1 год назад

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.

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.

Prime Intellect

125,935 просмотров • 4 месяцев назад

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.

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.

Prime Intellect

360,859 просмотров • 1 год назад

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.

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.

Prime Intellect

354,097 просмотров • 1 год назад

We found a universal sandbox exploit. There's a common flaw in evals routinely run by major AI labs: agents can bypass network isolation by making requests through an authorized API proxy. Just running the eval itself can be unsafe.

We found a universal sandbox exploit. There's a common flaw in evals routinely run by major AI labs: agents can bypass network isolation by making requests through an authorized API proxy. Just running the eval itself can be unsafe.

Prime Intellect

34,640 просмотров • 1 месяц назад

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

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

Prime Intellect

267,053 просмотров • 1 год назад

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.

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.

Prime Intellect

89,464 просмотров • 7 месяцев назад

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

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

Prime Intellect

260,002 просмотров • 2 лет назад

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 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.

Prime Intellect

198,754 просмотров • 1 год назад

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.

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

55,114 просмотров • 5 месяцев назад

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 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.

Prime Intellect

103,807 просмотров • 1 год назад