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Prime Intellect
Prime Intellect builds the 'Open Superintelligence Stack' — an integrated compute, training, inference, and sandbox platform that lets companies train, deploy, and continuously improve their own AI models and agents. It serves AI startups, 'neolabs', and enterprises (over 6,000 customers, including Ramp and Zapier) that want to own their model optimization loop rather than rely solely on closed frontier labs.
San Francisco, USA
Prime Intellect
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Description
Prime Intellect is a decentralized AI platform democratizing AI development through global compute aggregation and collaborative model training. It empowers developers with affordable computing resources and a sustainable open-source model, fostering innovation in AI research and development.Category: Infrastructure
Lab
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Description
Lab is a training platform built around the concept of 'environments' — packages of tasks (data, tools, simulators), a harness (sandboxes, context management, custom programs), and reward metrics used to evaluate and improve models. Its components include Hosted Training (large-scale reinforcement learning with managed trainer nodes, rollout inference, orchestration, scheduling, auto-scaling, and weight sync, priced per token via a multi-tenant architecture), Hosted Evaluations, an Environments Hub, Adapter Deployments, Prime Inference (an OpenAI-compatible inference API), and Prime Sandboxes. The workflow lets users evaluate a baseline model, train with reward signal from real tasks using the open-source prime-rl trainer, deploy trained LoRA adapters via an OpenAI-compatible API, and iterate. Launched in beta earlier in the year and made generally available on May 7, 2026, Lab has been used by hundreds of researchers, startups, and enterprises to run more than 10,000 training jobs spanning math, code, browser tasks, games, customer support, and enterprise workflows. For general availability, Hosted Training supports 14 models from NVIDIA, OpenAI, Meta, and Qwen ranging from 1B to 70B parameters. Lab is positioned as part of Prime Intellect's broader Open Superintelligence Stack mission of open infrastructure, environments, models, and training systems.Category: AI Agent Platform
INTELLECT-3
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Description
INTELLECT-3 is a 106B parameter Mixture-of-Experts model trained by Prime Intellect using a two-stage recipe (supervised fine-tuning followed by large-scale reinforcement learning) on a 512 NVIDIA H200 GPU cluster over roughly two months. It was trained end-to-end with prime-rl, Prime Intellect's asynchronous, production-scale RL framework, using agentic RL environments built with the verifiers library and hosted on the community Environments Hub, spanning math, code, science, logic, deep research, and software engineering tasks. Training also relied on Prime Sandboxes, a high-throughput, secure code execution layer for agentic coding rollouts, and a custom compute orchestration stack (Ansible provisioning, Slurm/Cgroup v2, Lustre storage, DCGM/Prometheus observability) managing the H200 cluster. The full recipe — model weights, training frameworks, datasets, RL environments, and evaluations — is open-sourced, and the model can be used via a hosted chat interface (chat.primeintellect.ai) or Prime Intellect's Inference API, with Parasail and Nebius serving as inference providers.Category: Compute Network
SYNTHETIC-2
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Description
SYNTHETIC-2 is a large-scale open dataset released by Prime Intellect on July 10, 2025, consisting of four million collaboratively generated and verified reasoning traces, produced using frontier-size models such as DeepSeek-R1-0528 sharded across distributed compute workers via pipeline parallelism. Over 1,250 GPUs (ranging from consumer 4090s to H200 clusters) joined the three-day run, with contributions verified through TOPLOC v2 proofs of computation to ensure honest, tamper-resistant inference. Prime Intellect's global orchestration infrastructure matches GPU nodes into geographically optimized groups, manages node heartbeats, task assignments, and failure recovery, and exposes an orchestrator API for deploying distributed inference workloads. The dataset covers a comprehensive set of new and existing verifiable reasoning tasks (math, coding, code output prediction, JSON/schema adherence, sentence unscrambling, ASCII tree formatting, and precise text extraction), split into SFT and RL subsets with difficulty annotations, and is released as four Hugging Face dataset splits (SYNTHETIC-2, SYNTHETIC-2-SFT-verified, SYNTHETIC-2-SFT-unverified, SYNTHETIC-2-RL). It is intended as the foundation for Prime Intellect's next distributed reinforcement learning runs and future INTELLECT models, serving AI researchers, model trainers, and developers looking to distill or train reasoning-capable language models using open, verifiable data.Category: Compute Network