Date: 03.09.2025

NVIDIA Tesla V100 GPU SXM2

Limitations

• Required SXM2 to PCIE adapter that looks ugly and not reliable

Test environment

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

My test environment: HP Z440 + NVIDIA Tesla V100 SXM2

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

• Priparing 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 responce

  2.5.0+cu124
  True
  Tesla V100-SXM2-16GB
  

Dry-run!

Mistral 7b

• Preapre 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

• Get the Mistral:

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

• Create script test_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,
    dtype=torch.bfloat16
).to("cuda")

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

• Preapre 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

• Get the StableDiffusion 1.5

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

• Create script test_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,
    safety_checker=None,
    feature_extractor=None,
    use_safetensors=True,
    local_files_only=True
).to("cuda")

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")

Benchmark

Prepare benchmark pp512 tg128 PyTorch pp512 and tg128 LLM Benchmark

Run test

Check nvidia-smi during each test while true; do nvidia-smi; sleep 1; done

• With quantization

python ./app.py -m ../mistral --tests pp512 tg128 --dtype fp16 --batch 1 --attn sdpa --ubatch 128 --warmup 3 --iters 10 --quant 4bit

It works!