Model reference · open weights
EuroLLM1.5-SFT is an open-weight language model from utter-project. EuroLLM1.5-9B-SFT (BF16) weighs 18.3 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the utter-project/EuroLLM1.5-9B-SFT model card, 2026-11-05
What it is
| Released by | utter-project |
|---|---|
| Released | 2026-08-29 |
| Parameters | 9.2B |
| VRAM | 18.3 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … RTX 4060 Ti 16 GB 2 smaller cards | ||||
| RTX 3090 24 GB | 3 | 24K | 23.4 GB | |
| RTX 4090 24 GB | 3 | 24K | 23.4 GB | |
| RTX 5090 32 GB | 8 | 2 | all 40K | 31.0 GB |
| L40S 48 GB | 17 | 4 | all 40K | 44.0 GB |
| A100 80 GB | 41 | 10 | all 40K | 78.2 GB |
| H100 80 GB | 39 | 9 | all 40K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 50 | 12 | all 40K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 59 | 14 | all 40K | 107 GB |
| H200 141 GB | 81 | 20 | all 40K | 138 GB |
| B200 180 GB | 108 | 27 | all 40K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 2 | 19K | 11.6 GB a card | |
| 2× RTX 4060 Ti 16 GB tensor parallel | 7 | 1 | all 40K | 15.4 GB a card |
| 2× RTX 4090 24 GB tensor parallel | 19 | 4 | all 40K | 23.4 GB a card |
| 2× RTX 3090 24 GB tensor parallel | 19 | 4 | all 40K | 23.4 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 20.4 GB | 24.7 GB |
| 5 | 26.1 GB | 47.2 GB |
| 8 | 30.3 GB | 64.1 GB |
| 16 | 41.6 GB | 109 GB |
| 32 | 64.1 GB | 199 GB |
| 64 | 109 GB | 380 GB |
One card, with vLLM's small-card settings.
From the model card
⚠️ PREVIEW RELEASE: This is a preview version. The model is still under development and may have limitations in performance and stability. Use with caution in production environments.
We compare EuroLLM1.5-9B-SFT against the previous EuroLLM releases (EuroLLM-9B-2512 and EuroLLM-22B-2512), the European open model Apertus-v1.5-8B, and two strong open-weight models of similar size, Qwen3.5-9B and Gemma 4 12B (both with thinking enabled), on English and multilingual benchmarks covering math, reasoning, coding, instruction following and knowledge.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "utter-project/EuroLLM1.5-9B-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto")
messages = [
{
"role": "system",
"content": "You are EuroLLM --- an AI assistant specialized in European languages that provides safe, educational and helpful answers.",
},
{
"role": "user", "content": "What is the capital of Portugal? How would you describe it?"
},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
outputs = model.generate(**inputs, max_new_tokens=32768, do_sample=True, temperature=0.6, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
EuroLLM1.5-9B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
If you use our work, please cite the following until the EuroLLM1.5 technical report is released:
@misc{ramos2026eurollm22btechnicalreport,
title={EuroLLM-22B: Technical Report},
author={Miguel Moura Ramos and Duarte M. Alves and Hippolyte Gisserot-Boukhlef and João Alves and Pedro Henrique Martins and Patrick Fernandes and José Pombal and Nuno M. Guerreiro and Ricardo Rei and Nicolas Boizard and Amin Farajian and Mateusz Klimaszewski and José G. C. de Souza and Barry Haddow and François Yvon and Pierre Colombo and Alexandra Birch and André F. T. Martins},
year={2026},
eprint={2602.05879},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2602.05879},
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.