Check whether your GPU can run a model — VRAM fit, CUDA version, quantization options — before you waste time loading it.
badgr-gpu-doctor infers a model's parameter count from its name, estimates VRAM per quantization level, and compares it against your GPU's known or detected VRAM — all before you spend a minute loading the model.
Auto-detects your GPU and VRAM via nvidia-smi, or accepts --gpu / --vram-gb / --cuda-version overrides for CI or remote checks.
Calculates a min–max VRAM range for the model at the chosen quantization (fp16, int8, auto, int4), including KV cache and runtime overhead.
Prints a compatible/incompatible verdict with suggestions (e.g. lower quantization, cap max_model_len), or the AI Badgr cloud command via --run-with-badgr.
npx badgr-gpu-doctor --model meta-llama/Llama-3.1-8B-InstructRequires Node.js 18+. nvidia-smi in PATH is optional, only needed for auto-detection.
Point it at a HuggingFace model ID. It auto-detects your GPU, or you can specify one manually — useful in CI where nvidia-smi isn't available.
npx badgr-gpu-doctor --model Qwen/Qwen3-32B --gpu RTX4090
npx badgr-gpu-doctor --model Qwen/Qwen3-32B --gpu A100 --jsonbadgr-gpu-doctor — model: Qwen/Qwen3-32B gpu: RTX4090
✓ Estimated VRAM needed: 19–26 GB (int4 quantization)
✗ Available VRAM: 24 GB (RTX 4090) — borderline, may OOM at runtime
Suggestion: cap max_model_len to reduce KV cache memory, or use a larger GPU.
To run this model in the cloud:
npx badgr-gpu-doctor --model Qwen/Qwen3-32B --run-with-badgrnpx badgr-gpu-doctor --model meta-llama/Llama-3.1-8B-Instruct — no account needed to get started.