Estimate Model Memory Requirements Without Downloading

A skill for hf_mem, a CLI tool that estimates the memory required for inference - model weights plus an optional KV cache - for Safetensors and GGUF models on the Hugging Face Hub, using HTTP Range requests so no weights are downloaded or loaded locally. uvx hf-mem --model-id <model-id> --json-output checks that the repo contains Safetensors (model.safetensors, a sharded model.safetensors.index.json, or model_index.json for Diffusers) or GGUF weights. For GGUF repos with multiple precisions/quantizations, estimation is per-file, so --gguf-file <file-or-path> targets the specific precision actually intended for inference rather than estimating every variant. An --experimental flag adds KV cache memory estimation, applicable to LLMs (...ForCausalLM), VLMs (...ForConditionalGeneration), and GGUF models - the context window is read from the model default or overridden with --max-model-len (vLLM-style), and KV cache precision defaults to the model's own precision unless set via --kv-cache-dtype, which for Safetensors accepts auto/bfloat16/fp8/fp8_ds_mla/fp8_e4m3/fp8_e5m2/fp8_inc and for GGUF accepts a much longer list of quantization types (F32, F16, Q4_0 through Q8_K, the IQ family, BF16, TQ1_0/TQ2_0, MXFP4, and more). Documented examples cover Transformers models with Safetensors weights (MiniMaxAI/MiniMax-M2), Diffusers models (Qwen/Qwen-Image), Sentence Transformers (google/embeddinggemma-300m), an LLM with KV cache estimation via --experimental (mistralai/Mistral-7B-v0.1), and a GGUF LLM/VLM targeting a specific quantized file (unsloth/Qwen3.5-397B-A17B-GGUF --gguf-file Q4_K_M --experimental).

Use it when a user asks how much VRAM or memory a model needs to run, whether a model fits on their GPU or a given instance, or references a Hugging Face model ID/URL and asks about inference requirements. Requires uv installed (for uvx) and an HF_TOKEN environment variable or --hf-token flag for gated or private models only.

Input is a Hugging Face model ID (and for GGUF, the specific file/precision to target) plus optional flags for KV cache estimation, context length, batch size, and cache dtype. Output is a JSON report of estimated memory requirements for model weights and, with --experimental, KV cache.

uvx hf-mem --model-id <model-id> --json-output

Reads model metadata directly from the Hugging Face Hub via HTTP Range requests - no local download, no GPU or inference framework required to run the estimation itself.

Developers and ML practitioners deciding whether a Hugging Face model fits their available VRAM before downloading or deploying it - checking Safetensors or GGUF weight memory alone, or including KV cache requirements for LLM/VLM inference at a given context length and precision.