MODEL DEVELOPMENT / Hugging Face
hf-mem Memory Estimate
Guides metadata-based estimates for model weight memory and optional KV-cache scenarios without first downloading the weight tensors.
“Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub”
01 / THE REASONING
Why this made the selection.
- Keeps an exact GGUF choice, context length, batch size and cache dtype visible in the estimation workflow.
- Uses the separate hf-mem tool’s metadata requests instead of treating model parameter count as a complete memory measurement.
02 / THE REVIEW RECORD
What we actually inspected.
Source review has boundaries.
A clear record is more useful than a “safe” badge.
Material inspected
- skills/hf-mem/SKILL.md
- README.md
- LICENSE
- alvarobartt/hf-mem@355089f7b14180b38f6fe2d9e4da664acb3675a7/README.md
Our findings
- The skill exposes separate model-file and KV-cache parameters and requests machine-readable output.
- The tool README describes HTTP Range reads of weight metadata, with KV-cache estimation behind its experimental option.
- Private or gated repositories require a token; the instructions do not require one for public metadata.
- The definition distinguishes per-file GGUF estimates from loading all quantizations and marks KV-cache estimation experimental with explicit context, batch and dtype controls.
Not established by this review
- No skill installation, consuming-agent workflow or external service operation was executed.
- Source inspection does not establish runtime compatibility, security certification or workload performance.
The review applies to the material and revision named here. A newer upstream release can change its behavior.
03 / PUT IT TO WORK
Add a skill to your workflow.
- The README installs additional Hugging Face skills with hf skills add <skill-name>, and its skill table names this skill hf-mem. No command with the name filled in is given.
- The README says the Claude Code and Cursor marketplace entries expose only hf-cli.
- Use the downloadable copy on this page, or compare an installed copy with revision c3ff942be3e07116b475253f0a8fc4f54682c1a8.
Before you start
- The hf CLI for hf skills add
- uv, for uvx
THE COMPLETE REVIEWED DEFINITION
Read it before you reuse it.
Original source bytes, with attribution.
Review the host-specific setup notes above.
---
name: hf-mem
description: Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
---
`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?
- `uv` installed (for `uvx`)
- `HF_TOKEN` env var or `--hf-token` flag (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.
```bash
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.
```bash
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:
```bash
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:
```bash
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:
```bash
uvx hf-mem --model-id MiniMaxAI/MiniMax-M2 --json-output
```
For Diffusers with Safetensors weights:
```bash
uvx hf-mem --model-id Qwen/Qwen-Image --json-output
```
For Sentence Transformers with Safetensors weights:
```bash
uvx hf-mem --model-id google/embeddinggemma-300m --json-output
```
With `--experimental` to include the KV cache estimation for LLMs and VLMs:
```bash
uvx hf-mem --model-id mistralai/Mistral-7B-v0.1 --experimental --json-output
```
And, for LLMs or VLMs with GGUF weights:
```bash
uvx hf-mem --model-id unsloth/Qwen3.5-397B-A17B-GGUF --gguf-file Q4_K_M --experimental --json-output
```
The download contains SKILL.md. Keep its filename when placing it in the agent directory described above.
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04 / FOLLOW THE EVIDENCE
The source trail.
Our notes are separate from the original resource.
Check upstream before adopting a new version.
https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/skills/hf-mem/SKILL.md
Supports: summary, upstreamDescription, whySelected, bestFor, limitations, review, install, compatibility, access
https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/README.md
Supports: review, install, limitations, access
- Inspected source: LICENSE ↗Checked
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Supports: license
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Supports: review, limitations, install, access
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Supports: install