Dev From Scratch: 3/n

Effective AI-assisted development using only local resources

Chapter 3 of “development from scratch”, where we build a competitive and highly effective AI-assisted coding environment using only local models and resources.

This chapter covers running a 30B Qwen3 model at 60-90TPS (we’ll optimize this later).

Link to previous chapter | Link to next chapter | Link to companion GitHub repo

WSL Docker Setup

We should install Docker Engine inside WSL so that nvidia-container-toolkit can interface directly with the WSL Linux kernel via dxgkrnl. This lets WSL sub in for a native host OS by directly managing the GPU resources.

# Install Docker
curl -fsSL https://get.docker.com | sh

# Add user to group
sudo usermod -aG docker $USER && newgrp docker

# Reload
newgrp docker

# Confirm socket (should show CONTAINER ID in red)
docker ps | grep "CONTAINER ID"

# Add Nvidia repositories
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# Update sources and install
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

# Configure NVidia runtime
sudo nvidia-ctk runtime configure --runtime=docker

# Restart docker
sudo systemctl restart docker

Downloading a 30B Model

The casual favorite. Make an account on HuggingFace and get an access token here.

# Install the HuggingFace CLI
brew install huggingface-cli

# Enable Git credential store
git config --global credential.helper store

# Log In (also store as git credential)
hf auth login

# Confirm you're logged in
hf auth whoami

# Save your auth token - replace YOUR_HF_TOKEN with your actual token.
echo 'export HF_TOKEN="YOUR_HF_TOKEN"' >> "$HOME/.zshrc-pre.sh" && source

# Create a folder for HuggingFace models
mkdir "$HOME/HF_Models"

# Add it to the pre-zshrc hook from Chapter 2
echo 'export HF_HOME="$HOME/HF_Models"' >> "$HOME/.zshrc-pre.sh" && source "$HOME/.zshrc"

# Confirm it's available. Your models will download here.
echo $HF_HOME

Qwen3-30B-A3B-NVFP4 is a solid Blackwell “Hello World” model. While it’s technically a 30B model, it uses only ~3.3B active parameters per token, giving it decent throughput on a 5090. It’s not particularly great (even compared to other single-GPU 5090 options) at anything, but it is one of the rare cases where NVFP4 quantization resulted in higher scores on some of the benchmarks we care about.

hf download nvidia/Qwen3-30B-A3B-NVFP4

Running it with vLLM

Blackwell GPUs support NVFP4 (1 sign, 2 exponent, and 1 mantissa bit) models and we’re going to be heavily abusing this. This groups weights into blocks of 16, each of which shares a high-precision 8-bit (E4M3) scale factor and allows the 4-bit quantized coefficients to handle the finer details.

They also come with the 2nd Gen Transformer Engine, which which has Tensor Core instructions that operate directly on the compressed 4-bit blocks. The result of this is much more efficient use of VRAM that lets us run much larger models with only ~28GB of available VRAM (4GB for host OS).

vLLM recently added experimental support for NVFP4 so we don’t have to build TensorRT-LLM from source and compile our models for TRT-LLM to get reasonable TTFT (time to first token) and TPS (tokens per second).

vLLM also comes with a number of optimizations like PagedAttention for the KV cache.

# Build the vLLM container.
chmod +x "$(git root)/docs/002_inference/build.sh"
"$(git root)/docs/002_inference/build.sh"

# Start the vLLM container. This takes A WHILE (8-10 minutes) for precompute.
chmod +x "$(git root)/docs/002_inference/run-model.sh"
"$(git root)/docs/002_inference/run-model.sh"

Chatting with it

# Start the UI server.
chmod +x "$(git root)/docs/002_inference/run-ui.sh"
"$(git root)/docs/002_inference/run-ui.sh"

Head over to http://localhost:1338

First, you’ll have to “warm up” vLLM with a simple hello world prompt. This will take a while, after which you can expect 60-90TPS. We’ll optimize this later. When you first open localhost in your browser, you may see errors. Click on “Check again” and reload!

Bonus: Resource Monitoring

# Install pipx
sudo apt install -y pipx

# Install nvitop
pipx install nvitop

# Add to path
echo 'export PATH="$HOME/.local/bin:$PATH"' >> "$HOME/.zshrc-pre.sh" && source "$HOME/.zshrc"

# Run nvitop
nvitop