Best scenario for “vibe coding” is to use vLLM or SGLang with 128k context-window max, primarily because of the concurrency support, which is what code assistants will rely on.
I would definitely would use the great work of @eugr , that already works for 1 -2 sparks: GitHub - eugr/spark-vllm-docker: Docker configuration for running VLLM on dual DGX Sparks
Please check also this discussion to get more background:
Run VLLM in Spark
Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
Create environment
uv venv .vllm --python 3.12
source .vllm/bin/activate
Install Pytorch
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
Install flashinfer and triton
uv pip install xgrammar triton flashinfer-python --prerelease=allow
git clone --recursive https://github.com/vllm-project/vllm.git
cd vllm
python3 use_existing_torch.py
uv pip install -r requiremen…