hf-mem
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/huggingface/skills --skill hf-mem- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads hf-mem automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
What are the requirements?
uvinstalled (foruvx)HF_TOKENenv var or--hf-tokenflag (for gated or private models only)
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.
uvx hf-mem --model-id <model-id> --json-output
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.
uvx hf-mem --model-id <model-id> --gguf-file <file-or-path> --json-output
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.
As per the context window, it will be read from the default or overridden with --max-model-len a la vLLM. And, same goes for the KV cache precision, which will default to the model precision unless manually set via --kv-cache-dtype a la vLLM too.
For Safetensors use as:
uvx hf-mem --model-id <model-id> --experimental [--max-model-len N] [--batch-size N] [--kv-cache-dtype auto|bfloat16|fp8|fp8_ds_mla|fp8_e4m3|fp8_e5m2|fp8_inc] --json-output
And, for GGUF use as:
uvx hf-mem --model-id <model-id> --gguf-file <file-or-path> --experimental [--max-model-len N] [--batch-size N] [--kv-cache-dtype auto|F32|F16|Q4_0|Q4_1|Q5_0|Q5_1|Q8_0|Q8_1|Q2_K|Q3_K|Q4_K|Q5_K|Q6_K|Q8_K|IQ2_XXS|IQ2_XS|IQ3_XXS|IQ1_S|IQ4_NL|IQ3_S|IQ2_S|IQ4_XS|I8|I16|I32|I64|F64|IQ1_M|BF16|TQ1_0|TQ2_0|MXFP4] --json-output
Examples
For Transformers with Safetensors weights:
uvx hf-mem --model-id MiniMaxAI/MiniMax-M2 --json-output
For Diffusers with Safetensors weights:
uvx hf-mem --model-id Qwen/Qwen-Image --json-output
For Sentence Transformers with Safetensors weights:
uvx hf-mem --model-id google/embeddinggemma-300m --json-output
With --experimental to include the KV cache estimation for LLMs and VLMs:
uvx hf-mem --model-id mistralai/Mistral-7B-v0.1 --experimental --json-output
And, for LLMs or VLMs with GGUF weights:
uvx hf-mem --model-id unsloth/Qwen3.5-397B-A17B-GGUF --gguf-file Q4_K_M --experimental --json-output
FAQ
What does the hf-mem skill do?
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
How do I install hf-mem?
Run npx skills add https://github.com/huggingface/skills --skill hf-mem in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/hf-mem/ (personal) or .claude/skills/hf-mem/ (project).
Which AI agents can use hf-mem?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is hf-mem safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains hf-mem?
It is published by huggingface in huggingface/skills. It has 862 recorded installs.
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