gpu-optimizer

Expert GPU optimization for consumer GPUs with 8–24GB VRAM. Evidence-based patterns only.

| GPU model | (e.g., RTX 4080 Mobile, RTX 3090, RTX 4090) | | VRAM | (e.g., 12GB, 16GB, 24GB) | | CUDA version | (nvidia-smi → top-right) | | TDP / power limit | (laptop vs desktop affects sustained throughput) | | Driver version | (nvidia-smi → top-left) |

Key constraint: VRAM capacity determines which strategies apply. Patterns below are annotated with minimum VRAM requirements where relevant.

Expert GPU optimization for modern consumer GPUs (8-24GB VRAM). Use this skill when you need to optimize GPU training, speed up CUDA code, reduce OOM errors, tune XGBoost for GPU, migrate NumPy to CuPy, make a model faster, manage GPU memory, optimize VRAM usage, or benchmark PyTorch. Covers mixed precision, gradient checkpointing, XGBoost GPU acceleration, CuPy/cuDF migration, vectorization, torch.compile, and diagnostics. NVIDIA GPUs only. PyTorch, XGBoost, and RAPIDS frameworks. Source: mathews-tom/praxis-skills.