---
name: estimador-memoria-modelos-hf
description: Herramienta CLI que calcula la memoria VRAM/RAM necesaria para ejecutar modelos de Hugging Face (Safetensors o GGUF) sin necesidad de descargarlos. Incluye estimación opcional de caché KV para LLMs y VLMs.
license: Apache-2.0
metadata:
  id: 96d5dbf2
  slug: estimador-memoria-modelos-hf
  titulo: "Estimador de memoria para modelos Hugging Face"
  servicio: IA-Ingenieria-MLOps
  categoria_recurso: IA-Generativa
  tipo: analisis
  nivel: intermedio
  idioma: es
  idioma_original: en
  acceso: gratis
  precio_eur: 0
  plataformas: [CLI, Linux, macOS, Windows]
  dependencias: [uv, uvx, HF_TOKEN (opcional, para modelos privados)]
  licencia: { spdx: Apache-2.0, redistribuible: true, uso_comercial: true }
  fuente:
    repo: huggingface/skills
    url: https://github.com/huggingface/skills/tree/main/skills/hf-mem
    commit: c68f1b0
    autor: huggingface
    nombre_original: hf-mem
    duplicados_en: []
  seguridad: { veredicto: seguro, riesgo: bajo, escaneado: "2026-06-14", motor: "grep-estatico+auditor-llm" }
  ficha:
    que_hace: "Estima la memoria GPU/RAM necesaria para cargar e inferir con cualquier modelo del Hub de Hugging Face."
    como_lo_hace: "Usa peticiones HTTP Range para leer solo los metadatos del modelo (sin descargar pesos) y calcula el uso de memoria para pesos y caché KV."
  content_hash: "96d5dbf24b18535814b77037dee4fa4c93f4d5422e2048d1b121802b70d3f5a1"
  version: 1.0.0
---

`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
```