Model reference · open weights
INTELLECT-3 is an open-weight language model from PrimeIntellect. INTELLECT-3-FP8 (FP8) weighs 108 GB; the smallest configuration that runs it is 4× RTX 5090 32 GB.
Summary of the PrimeIntellect/INTELLECT-3 model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | PrimeIntellect |
|---|---|
| Released | 2025-11-26 |
| Parameters | 106.9B |
| VRAM | 108 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … DGX Spark (GB10) 128 GB unified 10 smaller cards | — | — | — | |
| H200 141 GB | 13 | 3 | 108K | 138 GB |
| B200 180 GB | 35 | 8 | all 128K | 176 GB |
| 4× RTX 5090 32 GB tensor parallel | 5 | 1 | 47K | 31.0 GB a card |
| 2× H100 80 GB tensor parallel | 19 | 4 | all 128K | 78.1 GB a card |
| 2× A100 80 GB tensor parallel · FP8 without its speed-up here | 28 | 7 | all 128K | 78.2 GB a card |
| 4× L40S 48 GB tensor parallel | 39 | 9 | all 128K | 44.0 GB a card |
| 2× RTX PRO 6000 Blackwell 96 GB tensor parallel | 40 | 10 | all 128K | 93.8 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 111 GB | 116 GB |
| 5 | 118 GB | 141 GB |
| 8 | 122 GB | 159 GB |
| 16 | 135 GB | 209 GB |
| 32 | 159 GB | 308 GB |
| 64 | 209 GB | 505 GB |
One card, with vLLM's small-card settings.
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| INTELLECT-3 ↗ | 106.9B | BF16 | 214 GB | 2× H200 141 GB |
| INTELLECT-3-FP8 (above) ↗ | 106.9B | FP8 | 108 GB | 4× RTX 5090 32 GB |
From the model card
Trained with prime-rl and verifiers Environments released on Environments Hub Read the Blog & Technical Report
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.
INTELLECT-3 achieves best-in-class performance on math, coding, and reasoning benchmarks:
| Benchmark | MATH-500 | AIME24 | AIME25 | LCB | GPQA | HLE | MMLU-Pro |
|---|---|---|---|---|---|---|---|
| INTELLECT-3 | 98.1 | 90.8 | 88.0 | 69.3 | 74.4 | 14.6 | 81.9 |
| GLM-4.5-Air | 97.8 | 84.6 | 82.0 | 61.5 | 73.3 | 13.3 | 73.9 |
| GLM-4.5 | 97.0 | 85.8 | 83.3 | 64.5 | 77.0 | 14.8 | 83.5 |
| DeepSeek R1 0528 | 87.3 | 83.2 | 73.4 | 62.5 | 77.5 | 15.9 | 75.3 |
| DeepSeek v3.2 | 96.8 | 88.1 | 84.7 | 71.6 | 81.4 | 17.9 | 84.6 |
| GPT-O5S 120B | 96.0 | 75.8 | 77.7 | 69.9 | 70.0 | 10.6 | 67.1 |
| Model | HuggingFace |
|---|---|
| INTELLECT-3 | PrimeIntellect/INTELLECT-3 |
| INTELLECT-3-FP8 | PrimeIntellect/INTELLECT-3-FP8 |
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_r1
The 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_r1
@misc{intellect3,
title={INTELLECT-3: Technical Report},
author={Prime Intellect Team},
year={2025},
url={https://huggingface.co/PrimeIntellect/INTELLECT-3}
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.