Date: 25.01.2026

Pixtral 12b CUDA low memory GPU PyTorch Test

Test environment

• Workstation 40 GB RAM, 500GB SSD, 750W Power supply
• Ubuntu 24.04 LTS HWE Kernel
• Install python 3.12

My test environment: HP Z440 + NVIDIA RTX 3090

Ubuntu preparation

sudo apt-get install --install-recommends linux-generic-hwe-24.04
hwe-support-status --verbose
sudo apt dist-upgrade
sudo reboot

Driver setup

• Install drivers nvidia-driver-570

sudo apt install nvidia-driver-570 clinfo
sudo reboot

• Check installation

nvidia-smi
clinfo

• Install dev tools

sudo apt install -y python3-venv python3-dev git git-lfs

Dry-run Pixtral 12b

• Preapre python environment for CUDA:

mkdir -p ~/llm && cd ~/llm
python3 -m venv .venv_llm_pixtral
source ./.venv_llm_pixtral/bin/activate
python -m pip install --upgrade pip
pip install "torch==2.7.1" "torchvision==0.22.1" "torchaudio==2.7.1" --index-url https://download.pytorch.org/whl/cu128
pip install transformers accelerate

• Get the Pixtral 12b model:

git lfs install
git clone https://huggingface.co/mistral-community/pixtral-12b pixtral

• Put test picture to /home/sysadmin/llm/pixtral/2.jpeg

• Create script test_bad_cuda_pixtral.py:

from transformers import AutoProcessor, LlavaForConditionalGeneration, set_seed
import torch
from PIL import Image

print("GPU available:", torch.cuda.is_available())
print("GPU name:", torch.cuda.get_device_name(0))

model_path = "/home/sysadmin/llm/pixtral"

seed = torch.seed() % (2**32)
print(f"Using seed: {seed}")
set_seed(seed)

processor = AutoProcessor.from_pretrained(model_path)
model = LlavaForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
)

img_links = [
"/home/sysadmin/llm/pixtral/2.jpeg", 
]
prompt = "<s>[INST]Describe the image.\n[IMG][/INST]"

inputs = processor(text=prompt, 
    images=img_links, 
    return_tensors="pt"
)

inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)

generate_ids = model.generate(
    **inputs,     
    max_new_tokens=1000, #32768,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    # repetition loop prevention
    repetition_penalty=1.1,
    no_repeat_ngram_size=4)

print(processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])

• Run test

python test_bad_cuda_pixtral.py

Enjoy the result!