Model reference · open weights

Flex-reddit-2x7B-1T

LLMs allenai Text gen 1 build Open weights 5k dl/mo

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.

  • Flex-reddit-2x7B-1T is a text-generation model developed by allenai that functions as a Mixture-of-Experts with 11.6B parameters.
  • It is designed for English language tasks and supports a context length of 4096 tokens.
  • The model is released under the apache-2.0 licence.

Summary of the allenai/Flex-reddit-2x7B-1T model card, 2026-10-01

What it is

Released byallenai
Released2025-06-11
Parameters11.6B
VRAM23.3 GB for the weights

What it runs on

Memory and cards for Flex-reddit-2x7B-1T (FP32)

23.3 GBweights, file size
524 MBcache per 1K tokens
690 MBruntime overhead, at least
4,096 tokenscontext max
CardRequests at onceContext maxMemory
4K each
RTX 3060 12 GB … RTX 4090 24 GB
4 smaller cards
RTX 5090 32 GB3all 4K31.0 GB
L40S 48 GB9all 4K44.0 GB
A100 80 GB25all 4K78.2 GB
H100 80 GB23all 4K78.1 GB
RTX PRO 6000 Blackwell 96 GB30all 4K93.8 GB
DGX Spark (GB10) 128 GB unified37all 4K107 GB
H200 141 GB51all 4K138 GB
B200 180 GB68all 4K176 GB
2× RTX 4060 Ti 16 GB
tensor parallel
2all 4K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
10all 4K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
10all 4K23.4 GB a card
2× RTX 5090 32 GB
tensor parallel
17all 4K31.0 GB a card
Memory needed at each load
Requests at once4K tokens each
126.1 GB
534.7 GB
841.1 GB
1658.3 GB
3292.7 GB
64161 GB

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

From the model card

What allenai says about Flex-reddit-2x7B-1T

Read 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:

Use

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]))

Evaluation Snapshot

ModelMC9Gen5MMLUMMLU ProAGIEvalBBHMath2NewsGPoemGSciRIFF5Code4Avg.
Prev. Public model68.758.855.926.239.935.78.276.047.848.11.142.4
Individual
Math62.544.350.624.142.045.653.142.628.050.715.841.8
Code40.539.429.514.527.438.16.045.128.248.021.030.7
News46.548.636.415.225.730.92.577.726.947.00.032.5
Creative Writing42.743.931.511.623.327.61.756.967.542.40.031.7
Academic41.045.233.814.824.132.46.551.823.052.00.029.5
Reddit64.736.556.125.535.519.72.554.18.632.71.730.7
Combined
BTM (top-2)68.757.759.428.343.244.323.173.654.446.324.047.6
FlexOlmo-7x7B-1T65.644.750.922.137.235.625.455.839.045.910.639.3
FlexOlmo-7x7B-1T-RT70.659.760.030.544.645.947.779.767.654.511.352.0
  • The evaluation of the individual model refers to the dense model, not the 2x7B MoE model.

Citation

@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.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms