PrimeIntellect/INTELLECT-3
INTELLECT-3
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<p align="center"> <strong>INTELLECT-3: A 100B+ MoE trained with large-scale RL</strong> <br><br> Trained with <a href="https://github.com/PrimeIntellect-ai/prime-rl">prime-rl</a> and <a href="https://github.com/PrimeIntellect-ai/verifiers">verifiers</a> <br> Environments released on <a href="https://app.primeintellect.ai/dashboard/environments">Environments Hub</a> <br> Read the <a href="https://primeintellect.ai/blog/intellect-3">Blog</a> & <a href="https://storage.googleapis.com/intellect-3-paper/INTELLECT3Technical_Report.pdf">Technical Report</a> <br> <a href="https://x.com/primeintellect">X</a> | <a href="https://discord.gg/RC5GvMbfDf">Discord</a> | <a href="https://app.primeintellect.ai/dashboard/create-cluster">Prime Intellect Platform</a> </p>
Introduction
INTELLECT-3 is a 106B (A12B) parameter Mixture-of-Experts reasoning model post-trained from GLM-4.5-Air-Base using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL).
Training was performed with prime-rl using environments built with the verifiers library. All training and evaluation environments are available on the Environments Hub.
The model, training frameworks, and environments are open-sourced under fully-permissive licenses (MIT and Apache 2.0).
For more details, see the technical report.
Evaluation
INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks:
Model Variants
Serving with vLLM
The BF16 version can be served on 2x H200s:
vllm serve PrimeIntellect/INTELLECT-3 \
--tensor-parallel-size 2 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder \
--reasoning-parser deepseek_r1The FP8 version can be served on a single H200:
vllm serve PrimeIntellect/INTELLECT-3-FP8 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder \
--reasoning-parser deepseek_r1Citation
@misc{intellect3,
title={INTELLECT-3: Technical Report},
author={Prime Intellect Team},
year={2025},
url={https://huggingface.co/PrimeIntellect/INTELLECT-3}
}