Model reference · open weights

INTELLECT-3

LLMs PrimeIntellect Text gen 2 builds Open weights 8k dl/mo

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.

  • INTELLECT-3 is a 106.9B parameter Mixture-of-Experts reasoning model developed by PrimeIntellect for text generation tasks.
  • It supports a context length of 131,072 tokens and operates in English.
  • The model is released under the MIT license.

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 byPrimeIntellect
Released2025-11-26
Parameters106.9B
VRAM108 GB for the weights

What it runs on

Memory and cards for INTELLECT-3-FP8 (FP8)

108 GBweights, file size
188 MBcache per 1K tokens
1.7 GBruntime overhead, at least
131,072 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … DGX Spark (GB10) 128 GB unified
10 smaller cards
———
H200 141 GB133108K138 GB
B200 180 GB358all 128K176 GB
4× RTX 5090 32 GB
tensor parallel
5147K31.0 GB a card
2× H100 80 GB
tensor parallel
194all 128K78.1 GB a card
2× A100 80 GB
tensor parallel · FP8 without its speed-up here
287all 128K78.2 GB a card
4× L40S 48 GB
tensor parallel
399all 128K44.0 GB a card
2× RTX PRO 6000 Blackwell 96 GB
tensor parallel
4010all 128K93.8 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
1111 GB116 GB
5118 GB141 GB
8122 GB159 GB
16135 GB209 GB
32159 GB308 GB
64209 GB505 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
INTELLECT-3 ↗ 106.9BBF16 214 GB2× H200 141 GB
INTELLECT-3-FP8 (above) ↗ 106.9BFP8 108 GB4× RTX 5090 32 GB

From the model card

What PrimeIntellect says about INTELLECT-3

Read the model card

Trained with prime-rl and verifiers Environments released on Environments Hub Read the Blog & Technical Report

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:

BenchmarkMATH-500AIME24AIME25LCBGPQAHLEMMLU-Pro
INTELLECT-398.190.888.069.374.414.681.9
GLM-4.5-Air97.884.682.061.573.313.373.9
GLM-4.597.085.883.364.577.014.883.5
DeepSeek R1 052887.383.273.462.577.515.975.3
DeepSeek v3.296.888.184.771.681.417.984.6
GPT-O5S 120B96.075.877.769.970.010.667.1

Model Variants

ModelHuggingFace
INTELLECT-3PrimeIntellect/INTELLECT-3
INTELLECT-3-FP8PrimeIntellect/INTELLECT-3-FP8

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_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

Citation

@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.

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