Date: 05.07.2025

NVIDIA Tesla M10 GPU

Table of Contents

• Overview
• Instructions

Overview

Limitations

• Linux only; there is no driver for Windows
• This GPU is considered outdated; future versions of NVIDIA drivers may drop support for it
• Required external fan

Test environment

• Workstation 40 GB RAM, 200GB SSD, 750W Power supply
• Ubuntu 24.04 LTS HWE Kernel
• Install python 3.12

My test environment: HP Z440

Instructions

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
  

Check CUDA in Python

• Preparing PyTorch

  mkdir -p ~/llm && cd ~/llm
  python3 -m venv .venv_llm
  source ./.venv_llm/bin/activate
  python -m pip install --upgrade pip
  pip install "torch==2.5.0" "torchvision==0.20.0" "torchaudio==2.5.0" --index-url https://download.pytorch.org/whl/cu124
  pip install "bitsandbytes==0.44.1"
  python3 -c "import torch; print(torch.__version__); print(torch.cuda.is_available());print(torch.cuda.get_device_name(0));"
  

• Expected response

  2.5.0+cu124
  True
  Tesla M10
  

Dry-run!

Mistral 7b

• Get the Mistral:

  git lfs install
  git clone https://huggingface.co/mistralai/Mistral-7B-v0.1 mistral
  

• Prepare Python environment for CUDA 12:

  mkdir -p ~/llm && cd ~/llm
  python3 -m venv .venv_llm_mistral
  source ./.venv_llm_mistral/bin/activate
  python -m pip install --upgrade pip
  pip install "torch==2.5.0" "torchvision==0.20.0" "torchaudio==2.5.0" --index-url https://download.pytorch.org/whl/cu124
  pip install transformers accelerate
  

• Create script test_bad_cuda_mistral.py:

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import time

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

model_path = "/home/sysadmin/llm/mistral"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model     = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    offload_folder="offload",
    torch_dtype=torch.bfloat16
)

generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

print(generator("Tell the story about sun.", max_new_tokens=120)[0]["generated_text"])

Stable Diffusion v1.5

• Get the StableDiffusion 1.5

git lfs install
git clone https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5 sd1.5

• Prepare Python environment for CUDA:

mkdir -p ~/llm && cd ~/llm
python3 -m venv .venv_llm_sd1.5
source ./.venv_llm_sd1.5/bin/activate
python -m pip install --upgrade pip
pip install "torch==2.5.0" "torchvision==0.20.0" "torchaudio==2.5.0" --index-url https://download.pytorch.org/whl/cu124
pip install transformers accelerate diffusers safetensors

• Create script test_bad_cuda_sd1.5.py:

from diffusers import StableDiffusionPipeline
import torch

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

model_path = "/home/sysadmin/llm/sd1.5"

pipe = StableDiffusionPipeline.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="balanced",
    offload_folder="offload",
    safety_checker=None,
    feature_extractor=None,
    use_safetensors=True,
    local_files_only=True
)


out = pipe(
    prompt= "cat sitting on a chair",
    height=512, width=512, guidance_scale=9, num_inference_steps=80)
image = out.images[0]

image.save("test.png", format="PNG")

It works!

But looks ugly.