Model reference · open weights
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.
Summary of the NousResearch/nomos-1 model card, 2026-10-01
What it is
| Released by | NousResearch |
|---|---|
| Released | 2025-12-07 |
| Parameters | 30.5B |
| VRAM | 61.1 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … L40S 48 GB 6 smaller cards | — | — | — | |
| A100 80 GB | 19 | 4 | 158K | 78.2 GB |
| H100 80 GB | 12 | 3 | 103K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 32 | 8 | all 256K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 49 | 12 | all 256K | 107 GB |
| H200 141 GB | 87 | 21 | all 256K | 138 GB |
| B200 180 GB | 131 | 32 | all 256K | 176 GB |
| 2× L40S 48 GB tensor parallel | 30 | 7 | 244K | 44.0 GB a card |
| 4× RTX 4090 24 GB tensor parallel | 34 | 8 | all 256K | 23.4 GB a card |
| 4× RTX 3090 24 GB tensor parallel | 34 | 8 | all 256K | 23.4 GB a card |
| 4× RTX 5090 32 GB tensor parallel | 72 | 18 | all 256K | 31.0 GB a card |
| 2× H100 80 GB tensor parallel | 101 | 25 | all 256K | 78.1 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 63.0 GB | 65.4 GB |
| 5 | 66.2 GB | 78.3 GB |
| 8 | 68.6 GB | 88.0 GB |
| 16 | 75.1 GB | 114 GB |
| 32 | 88.0 GB | 165 GB |
| 64 | 114 GB | 268 GB |
One card, with vLLM's small-card settings.
From 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.
We recommend using Nomos-1 without a system prompt.
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))
python -m sglang.launch_server \
--model-path NousResearch/nomos-1 \
--tp-size 8
vllm serve \
--model NousResearch/nomos-1 \
--tensor-parallel-size 8
We would like to thank the following contributors:
Roger Jin, Jeffrey Quesnelle, Dakota Mahan, Chen Guang, Teknium
jpark, Ibrakhim Ustelbay, Pruthvi Rajaghatta
Samuel Kim, Miron Yurkevich, Adilet Zauytkhan, Rinat Amankos, Alexander Andreyev, Damir Nurlanov, Abuzer Abuov
massiveaxe
@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.