Model reference · open weights

EuroLLM1.5-SFT

LLMs utter-project Text gen 1 build Licence not stated 27 dl/mo

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.

  • EuroLLM1.5-SFT is a 9.2 billion parameter text generation model developed by utter-project.
  • It is designed as an AI assistant specialized in European languages and supports a context length of 40960 tokens.
  • The model is currently a preview release and has not been aligned to human preferences.

Summary of the utter-project/EuroLLM1.5-9B-SFT model card, 2026-11-05

What it is

Released byutter-project
Released2026-08-29
Parameters9.2B
VRAM18.3 GB for the weights

What it runs on

Memory and cards for EuroLLM1.5-9B-SFT (BF16)

18.3 GBweights, file size
172 MBcache per 1K tokens
728 MBruntime overhead, at least
40,960 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
RTX 3090 24 GB324K23.4 GB
RTX 4090 24 GB324K23.4 GB
RTX 5090 32 GB82all 40K31.0 GB
L40S 48 GB174all 40K44.0 GB
A100 80 GB4110all 40K78.2 GB
H100 80 GB399all 40K78.1 GB
RTX PRO 6000 Blackwell 96 GB5012all 40K93.8 GB
DGX Spark (GB10) 128 GB unified5914all 40K107 GB
H200 141 GB8120all 40K138 GB
B200 180 GB10827all 40K176 GB
2× RTX 3060 12 GB
tensor parallel
219K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
71all 40K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
194all 40K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
194all 40K23.4 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
120.4 GB24.7 GB
526.1 GB47.2 GB
830.3 GB64.1 GB
1641.6 GB109 GB
3264.1 GB199 GB
64109 GB380 GB

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

From the model card

What utter-project says about EuroLLM1.5-SFT

Read 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.

Results

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.

Run the model

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))

Bias, Risks, and Limitations

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).

Citation

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.

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