Model reference · open weights
Flex-reddit-2x7B-1T is an open-weight language model from allenai. Flex-reddit-2x7B-1T (FP32) weighs 23.3 GB; the smallest configuration that runs it is 2× RTX 4060 Ti 16 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the allenai/Flex-reddit-2x7B-1T model card, 2026-10-01
What it is
| Released by | allenai |
|---|---|
| Released | 2025-06-11 |
| Parameters | 11.6B |
| VRAM | 23.3 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 4K each | |||
| RTX 3060 12 GB … RTX 4090 24 GB 4 smaller cards | |||
| RTX 5090 32 GB | 3 | all 4K | 31.0 GB |
| L40S 48 GB | 9 | all 4K | 44.0 GB |
| A100 80 GB | 25 | all 4K | 78.2 GB |
| H100 80 GB | 23 | all 4K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 30 | all 4K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 37 | all 4K | 107 GB |
| H200 141 GB | 51 | all 4K | 138 GB |
| B200 180 GB | 68 | all 4K | 176 GB |
| 2× RTX 4060 Ti 16 GB tensor parallel | 2 | all 4K | 15.4 GB a card |
| 2× RTX 4090 24 GB tensor parallel | 10 | all 4K | 23.4 GB a card |
| 2× RTX 3090 24 GB tensor parallel | 10 | all 4K | 23.4 GB a card |
| 2× RTX 5090 32 GB tensor parallel | 17 | all 4K | 31.0 GB a card |
| Requests at once | 4K tokens each |
|---|---|
| 1 | 26.1 GB |
| 5 | 34.7 GB |
| 8 | 41.1 GB |
| 16 | 58.3 GB |
| 32 | 92.7 GB |
| 64 | 161 GB |
One card, with vLLM's small-card settings.
From the model card
FlexOlmo-7x7B-1T (without router training) is a Mixture-of-Experts with 33B total parameters, combining independently trained experts on public-mix, news, math, code, academic texts, creative writing, and Reddit data. The public-mix expert is trained on 1T tokens of public data while the other experts are branched from the public-mix expert and trained on 50B tokens of their respective data.
This information and more can also be found:
Install transformers with version 4.57.0 or newer and run:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_NAME = "allenai/Flex-reddit-2x7B-1T"
TOKENIZER_NAME = "allenai/dolma2-tokenizer"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to(DEVICE)
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
inputs = tokenizer("Bitcoin is", return_tensors="pt")
inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
out = model.generate(**inputs, max_length=64)
print(tokenizer.decode(out[0]))
| Model | MC9 | Gen5 | MMLU | MMLU Pro | AGIEval | BBH | Math2 | NewsG | PoemG | SciRIFF5 | Code4 | Avg. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Prev. Public model | 68.7 | 58.8 | 55.9 | 26.2 | 39.9 | 35.7 | 8.2 | 76.0 | 47.8 | 48.1 | 1.1 | 42.4 |
| Individual | ||||||||||||
| Math | 62.5 | 44.3 | 50.6 | 24.1 | 42.0 | 45.6 | 53.1 | 42.6 | 28.0 | 50.7 | 15.8 | 41.8 |
| Code | 40.5 | 39.4 | 29.5 | 14.5 | 27.4 | 38.1 | 6.0 | 45.1 | 28.2 | 48.0 | 21.0 | 30.7 |
| News | 46.5 | 48.6 | 36.4 | 15.2 | 25.7 | 30.9 | 2.5 | 77.7 | 26.9 | 47.0 | 0.0 | 32.5 |
| Creative Writing | 42.7 | 43.9 | 31.5 | 11.6 | 23.3 | 27.6 | 1.7 | 56.9 | 67.5 | 42.4 | 0.0 | 31.7 |
| Academic | 41.0 | 45.2 | 33.8 | 14.8 | 24.1 | 32.4 | 6.5 | 51.8 | 23.0 | 52.0 | 0.0 | 29.5 |
| 64.7 | 36.5 | 56.1 | 25.5 | 35.5 | 19.7 | 2.5 | 54.1 | 8.6 | 32.7 | 1.7 | 30.7 | |
| Combined | ||||||||||||
| BTM (top-2) | 68.7 | 57.7 | 59.4 | 28.3 | 43.2 | 44.3 | 23.1 | 73.6 | 54.4 | 46.3 | 24.0 | 47.6 |
| FlexOlmo-7x7B-1T | 65.6 | 44.7 | 50.9 | 22.1 | 37.2 | 35.6 | 25.4 | 55.8 | 39.0 | 45.9 | 10.6 | 39.3 |
| FlexOlmo-7x7B-1T-RT | 70.6 | 59.7 | 60.0 | 30.5 | 44.6 | 45.9 | 47.7 | 79.7 | 67.6 | 54.5 | 11.3 | 52.0 |
@misc{flexolmo,
title={FlexOlmo: Open Language Models for Flexible Data Use},
author={Weijia Shi and Akshita Bhagia and Kevin Farhat and Niklas Muennighoff and Jacob Morrison and Evan Pete Walsh and Dustin Schwenk and Shayne Longpre and Jake Poznanski and Allyson Ettinger and Daogao Liu and Margaret Li and Mike Lewis and Wen-tau Yih and Dirk Groeneveld and Luca Soldaini and Kyle Lo and Noah A. Smith and Luke Zettlemoyer and Pang Wei Koh and Hannaneh Hajishirzi and Ali Farhadi and Sewon Min},
year={2025},
eprint={2507.00000},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://allenai.org/papers/flexolmo},
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.