Localized reading guide
hf-memPurpose, usage and source
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
Source retrieved:
- Source repository
- huggingface/skills
- SKILL.md
skills/hf-mem/SKILL.md- Repository stars
- 11,241
- Repository updated
- 2026-10-30
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License: Apache-2.0
Workflow excerpts are reading aids, not executable instructions from Codez. No third-party Skill was run to produce this page.
The purpose and workflow passages below are upstream material, organized and translated for reading. They are distinct from the separately labeled Codez editorial analysis.
What this Skill does
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
How the upstream workflow works
How to run?
Run with --model-id pointing to the Hugging Face Hub repository which will check that it either contains Safetensors (via model.safetensors, model.safetensors.index.json if sharded, or model_index.json for Diffusers) or GGUF model weights within. If the repository contains GGUF model weights in multiple precisions / quantizations, the estimations will be on a per-file basis, whereas for inference you won't load all of those but rather only a single precision. This being said, for GGUF you might as well need to provide --gguf-file to target the specific file (or path if sharded) you want to run. Additionally, hf-mem comes with an --experimental flag that will also calculate the KV cache memory requirements too, useful for large-language models, meaning it applies to LLMs (...ForCausalLM), VLMs (...ForConditionalGeneration), and GGUF models. For Safetensors use as:
Overview
hf_mem estimates the required memory for inference, including model weights and an optional KV cache, for Safetensors and GGUF for models on the Hugging Face Hub using HTTP Range requests i.e., without downloading or loading any weights locally.
When to use?
User asks how much VRAM or memory a model needs to run User wants to know if a model fits on their GPU or a given instance User references a Hugging Face model ID or URL and asks about inference requirements
Upstream passage · L8 ↗uvx hf-mem --model-id <model-id> --json-outputRequirements and setup
What are the requirements?
uv installed (for uvx) HF_TOKEN env var or --hf-token flag (for gated or private models only)
Limits and quality checks
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Read further in the source
Related Skills
Translation and scope
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