Releasing INTELLECT-2: We’re open-sourcing the first 32B parameter model trained via globally distributed reinforcement learning:

• Detailed Technical Report
• INTELLECT-2 model checkpoint

primeintellect.ai/blog/intellect… To train a model with reinforcement learning in a fully decentralized setting using community-contributed GPUs, we open-source several novel infrastructure components.

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. INTELLECT-2 brings decentralized training into the inference-time compute era:
• Fully async, decentralized reinforcement learning
• Eliminating communication overhead
• Scalable across heterogeneous GPUs worldwide

primeintellect.ai/blog/intellect…

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. Today, we release:
- SYNTHETIC-1: 1.4 million high-quality tasks & verifiers
- Public synthetic data run - allowing anyone to contribute compute
- GENESYS: open, extendable synthetic data generation framework + call for crowdsourcing tasks & verifiers

primeintellect.ai/blog/synthetic…

Today, we release TOPLOC: A Locality Sensitive Hashing Scheme for Verifiable Inference

- Detects modifications to models, prompts, or precision
- Robust across GPU types, tensor parallel configurations and attention kernels
- Up to 100× faster validation than generation
- Reduces memory overhead of proofs by 1000×

primeintellect.ai/blog/toploc

Building the foundation for decentralized, verifiable compute protocols. The Problem: Trust in LLM Inference

In a peer-to-peer setting, ensuring honest behavior among providers requires detecting and penalizing dishonest ones. Providers often make changes, such as:

- Lowering precision
- Compressing KVCache
- Altering model weights or prompts

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. METAGENE-1 is a 7B parameter autoregressive transformer model trained on over 1.5T DNA and RNA base pairs sequenced from wastewater samples.

Website: metagene.ai
Paper: metagene.ai/metagene-1-pap…
Github: github.com/metagene-ai/me…
Hugging Face: huggingface.co/metagene-ai

Releasing INTELLECT-1: We’re open-sourcing the first decentralized trained 10B model:

- INTELLECT-1 base model & intermediate checkpoints
- Pre-training dataset
- Post-trained instruct models by @arcee_ai
- PRIME training framework
- Technical paper with all details This represents a 10× scale-up from our previous work and demonstrates that large-scale model training is no longer confined to large corporations but can be achieved through distributed, community-driven approaches.

Technical report: github.com/PrimeIntellect…

Blogpost: primeintellect.ai/blog/intellect…

Introducing Prime Intellect – democratizing AI development at scale, from compute to intelligence.

We're excited to announce our $5.5M raise from @DistributedG @coinfund_io @CompoundVC @Collab_Currency @protocollabs @ClementDelangue @dylan522p and others

primeintellect.ai/blog/introduci… Our vision
Build infrastructure to aggregate compute, develop distributed training frameworks, and create a protocol for decentralized AI development—enabling anyone to contribute resources, collectively train open models, and share in their ownership.