Model reference · open weights

nomos-1

LLMs NousResearch Text gen 1 build Open weights 389 dl/mo

nomos-1 is an open-weight language model from NousResearch. nomos-1 (FP32) weighs 61.1 GB; the smallest configuration that runs it is H100 80 GB.

  • Nomos-1 is a 30.5B parameter text-generation model by NousResearch, specialized for mathematical problem-solving and proof-writing in natural language.
  • It is a specialization of Qwen3-30B-A3B-Thinking-2507, trained in collaboration with Hillclimb AI, and features a context length of 262,144 tokens.
  • The model is released under the Apache 2.0 license.

Summary of the NousResearch/nomos-1 model card, 2026-10-01

What it is

Released byNousResearch
Released2025-12-07
Parameters30.5B
VRAM61.1 GB for the weights

What it runs on

Memory and cards for nomos-1 (FP32)

61.1 GBweights, file size
98 MBcache per 1K tokens
1.1 GBruntime overhead, at least
262,144 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … L40S 48 GB
6 smaller cards
———
A100 80 GB194158K78.2 GB
H100 80 GB123103K78.1 GB
RTX PRO 6000 Blackwell 96 GB328all 256K93.8 GB
DGX Spark (GB10) 128 GB unified4912all 256K107 GB
H200 141 GB8721all 256K138 GB
B200 180 GB13132all 256K176 GB
2× L40S 48 GB
tensor parallel
307244K44.0 GB a card
4× RTX 4090 24 GB
tensor parallel
348all 256K23.4 GB a card
4× RTX 3090 24 GB
tensor parallel
348all 256K23.4 GB a card
4× RTX 5090 32 GB
tensor parallel
7218all 256K31.0 GB a card
2× H100 80 GB
tensor parallel
10125all 256K78.1 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
163.0 GB65.4 GB
566.2 GB78.3 GB
868.6 GB88.0 GB
1675.1 GB114 GB
3288.0 GB165 GB
64114 GB268 GB

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

From the model card

What NousResearch says about nomos-1

Read the model card

We release Nomos 1, a specialization of Qwen/Qwen3-30B-A3B-Thinking-2507 for mathematical problem-solving and proof-writing in natural language. Nomos-1 was trained in collaboration with Hillclimb AI.

Nomos 1 is designed to be used with the Nomos Reasoning Harness, which we open-source concurrently.

On Putnam 2025, Nomos 1 scores 87/120 when wrapped in the Nomos reasoning harness. Under the same conditions, Qwen/Qwen3-30B-A3B-Thinking-2507 scores 24/120.

Usage

We recommend using Nomos-1 without a system prompt.

Hugging Face

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "NousResearch/nomos-1"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": (
            "Solve the following problem and show your reasoning:\n\n"
            "Let a, b, c be positive real numbers such that abc = 1. "
            "Prove that\n"
            "\\[\n"
            "  \\frac{1}{1+a} + \\frac{1}{1+b} + \\frac{1}{1+c} \\ge 1.\n"
            "\\]"
        ),
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=400,
        temperature=0.6,
        top_p=0.95,
        top_k=20,
        do_sample=True,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

SGLang

python -m sglang.launch_server \
  --model-path NousResearch/nomos-1 \
  --tp-size 8

vLLM

vllm serve \
  --model NousResearch/nomos-1 \
  --tensor-parallel-size 8

Acknowledgements

We would like to thank the following contributors:

Nous Research

Roger Jin, Jeffrey Quesnelle, Dakota Mahan, Chen Guang, Teknium

Hillclimb AI

jpark, Ibrakhim Ustelbay, Pruthvi Rajaghatta

Math experts

Samuel Kim, Miron Yurkevich, Adilet Zauytkhan, Rinat Amankos, Alexander Andreyev, Damir Nurlanov, Abuzer Abuov

Other

massiveaxe

Citation

@misc{nomos_model2025,
  title        = {Nomos Model},
  author       = {Jin, Roger and Quesnelle, Jeffrey and Mahan, Dakota and Guang, Chen and Teknium, Ryan and Park, Jun and Ustelbay, Ibrakhim and Kim, Samuel and Yurkevich, Miron and Zauytkhan, Adilet and Amankos, Rinat and Andreyev, Alex and Nurlanov, Damir and Abuov, Abuzer and massiveaxe, Askar},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/NousResearch/nomos-1}},
  note         = {Model release},
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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