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.

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
TypeLanguage models
TaskText gen
Parameters (lead)11.6B
Context4,096 tokens
Runs withtransformers
Released2025-06-11
Popularity5k downloads / month
Weights23.3 GB (Flex-reddit-2x7B-1T (FP32), file size)
LicenceOpen weights

What it runs on

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

Weights 23.3 GB (file size) · KV cache 524 MB per 1,000 tokens of context, at 16 bits (vLLM's default for this build; an 8-bit cache halves it) · runtime overhead from 690 MB on a small card · context up to 4,096 tokens.

CardRequests at once
4K, its whole window tokens each
Requests at once
32K tokens each
Longest single
request
Counted
memory
RTX 3060 12 GB … RTX 4090 24 GB
4 smaller cards
———
RTX 5090 32 GB3—all 4K31.0 GB
L40S 48 GB9—all 4K44.0 GB
A100 80 GB25—all 4K78.2 GB
H100 80 GB23—all 4K78.1 GB
RTX PRO 6000 Blackwell 96 GB30—all 4K93.8 GB
DGX Spark (GB10) 128 GB unified37—all 4K107 GB
H200 141 GB51—all 4K138 GB
B200 180 GB68—all 4K176 GB
2× RTX 4060 Ti 16 GB
tensor parallel
2—all 4K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
10—all 4K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
10—all 4K23.4 GB a card
2× RTX 5090 32 GB
tensor parallel
17—all 4K31.0 GB a card
Memory needed at each load
Requests at once4K, its whole window tokens each32K tokens each
126.1 GB—
534.7 GB—
841.1 GB—
1658.3 GB—
3292.7 GB—
64161 GB—

On one card, with vLLM's small-card settings (2,048 tokens a step). Cards of 70 GB and more reserve more per request and more overhead — each row above uses its own card's settings.

Estimates, not measurements, checked against published vLLM startup logs. The weights are the build's file size; the cache is calculated from its config (multi-head attention); the overhead is an estimate of vLLM's own memory with that card's default settings. "Requests at once" is how many requests of that length vLLM admits — its reservation at full length, with --max-model-len set to that length; requests that stay shorter fit more. "Longest single request" is the most one request can hold there: below the model's maximum, vLLM starts only with --max-model-len set at or under it. "Counted memory" is vLLM's default 92 % of what CUDA reports for the card (the DGX Spark: about 100 GiB of its shared 128 GB). A tensor-parallel split pools the cards' memory and speeds each token up, at the cost of the link between them; a layer split (llama.cpp) holds more but does not make one request faster. Assumes vLLM 0.10 or later.

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.

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