hf-mem

You can estimate VRAM requirements for Hugging Face models without a full download by using HTTP Range requests to read Safetensors and GGUF weight metadata remotely, providing instant memory projections for deployment.

Yes, KV cache memory calculations for LLMs and VLMs are supported during inference-time memory profiling, providing experimental projections to help verify if your hardware can handle the specific model and context length.

Yes, you need the uv package manager installed to run the memory estimation skill, as it handles the environment setup required to access the Hugging Face Hub API and process the model weight calculations.

Yes, VRAM estimation works with both Safetensors and GGUF formats, allowing you to verify if your GPU cluster has sufficient capacity for the specific quantization or precision you intend to run before downloading massive models.

The best way to prevent out-of-memory errors is to profile model memory requirements beforehand, calculating the exact VRAM needed for weights and KV cache to ensure your hardware capacity matches the deployment target.

The KV cache memory projections are experimental, meaning they provide estimates rather than exact measurements for inference-time memory, so you should treat the results as planning guidelines for your GPU capacity rather than absolute limits.