--- title: "hf-mem Memory Estimate: review, setup & limitations · Undominated.ai" canonical: https://undominated.ai/skills/huggingface-hf-mem/ description: "Guides metadata-based estimates for model weight memory and optional KV-cache scenarios without first downloading the weight tensors." --- # hf-mem Memory Estimate: review, setup & limitations · Undominated.ai > Guides metadata-based estimates for model weight memory and optional KV-cache scenarios without first downloading the weight tensors. [← Explore all skills](/skills/) 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. [See setup guidance ↓](#setup)[Original source ↗](https://github.com/huggingface/skills/tree/c3ff942be3e07116b475253f0a8fc4f54682c1a8/skills/hf-mem) SOURCE REVIEW Reviewed 2026-10-14 Evidence 5 linked sources Publisher Hugging Face Licence [Apache-2.0 ↗](https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/LICENSE) Revision c3ff942be3e0 [Read what was—and wasn’t—checked ↓](#review) “Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub” [Hugging Face · upstream description ↗](https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/skills/hf-mem/SKILL.md) Our analysis follows below. 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. ### A good fit for - Screening model and quantisation candidates before a large download - Comparing explicit context and batch assumptions before provisioning an inference trial - Estimate model artifact memory and optional KV-cache size before selecting a test machine. ### Weigh up before choosing - hf-mem describes itself as experimental; its estimate is not a measured peak allocation or a guarantee that a serving workload fits. - The skill pins instruction bytes, not the uvx-resolved package version or Hub model revision; record those separately for reproducible estimates. - When a repository contains both formats, the tool documentation says Safetensors takes precedence unless a GGUF file is selected. - The estimate is not measured peak runtime memory or proof of device fit; allocator, activations, kernels and concurrency add overhead. The unpinned uvx package is a separate executable dependency from this pinned skill. 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. [Upstream setup instructions ↗](https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/README.md) - The README installs additional Hugging Face skills with hf skills add , 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 ### Compatibility Agent Skills-compatible hosts · Claude Code · Codex · Gemini CLI · Cursor · uv ### Read Hub metadata over the network - Read Hub metadata over the network - Execute the separately installed hf-mem package locally - Use a supplied Hub token for authorised private or gated reads - Downloads package code and queries Hub metadata/ranges; gated/private access uses a scoped token and does not authorize model publication. ### Cost model The Apache-2.0 skill has no bundled hosting service; package execution, network access and agent usage remain separate. THE COMPLETE REVIEWED DEFINITION ## Read it before you reuse it. Original source bytes, with attribution. Review the host-specific setup notes above. Copy definition ↗ [Download definition + licence ↗](/resources/skills/huggingface-hf-mem/bundle.zip)[Raw Markdown ↗](/resources/skills/huggingface-hf-mem/definition.md) --- 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 --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 --gguf-file --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 --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 --gguf-file --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. By **Hugging Face**. [Exact upstream source ↗](https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/skills/hf-mem/SKILL.md) · [Licence](/resources/skills/huggingface-hf-mem/LICENSE.txt) · [Attribution](/resources/skills/huggingface-hf-mem/ATTRIBUTION.txt) SHA-256 ee99f9d97e084aa61a80eaec4e6de2265341e71256d918d48fb87a2ac01ce99f Read the applicable licence Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. 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Check upstream before adopting a new version. - [Skill definition at reviewed revision ↗](https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/skills/hf-mem/SKILL.md) Checked 2026-10-14 https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/skills/hf-mem/SKILL.md Supports: summary, upstreamDescription, whySelected, bestFor, limitations, review, install, compatibility, access - [Inspected source: README.md ↗](https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/README.md) Checked 2026-10-14 https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/README.md Supports: review, install, limitations, access - [Inspected source: LICENSE ↗](https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/LICENSE) Checked 2026-10-14 https://github.com/huggingface/skills/blob/c3ff942be3e07116b475253f0a8fc4f54682c1a8/LICENSE Supports: license - [hf-mem tool README at inspected revision ↗](https://github.com/alvarobartt/hf-mem/blob/355089f7b14180b38f6fe2d9e4da664acb3675a7/README.md) Checked 2026-10-14 https://github.com/alvarobartt/hf-mem/blob/355089f7b14180b38f6fe2d9e4da664acb3675a7/README.md Supports: review, limitations, install, access - [Interactive Skills installer source formats ↗](https://github.com/vercel-labs/skills/blob/7407f3893ad4dceab546ac002c3ef806e4000c73/README.md) Checked 2026-10-14 https://github.com/vercel-labs/skills/blob/7407f3893ad4dceab546ac002c3ef806e4000c73/README.md Supports: install KEEP COMPARING ## Other approaches to consider. Related by category or shared topics. These are alternatives to inspect, not a measured quality order. [### Hugging Face LLM Trainer ↗ Prepares and submits managed training jobs with dataset checks, monitoring and explicit persistence of trained artifacts.](/skills/huggingface-huggingface-llm-trainer/)[### Hugging Face Trackio ↗ Adds training metrics and diagnostic alerts, then retrieves run data through Trackio’s CLI or a local or hosted dashboard.](/skills/huggingface-huggingface-trackio/)[### Portable Triton Kernels ↗ Writes and benchmarks portable Triton kernels for NVIDIA and AMD GPUs: block-size discipline, autotune configuration, fp16/bf16/fp8 numerics, masked loads, reductions, correctness tests against a PyTorch reference and publishing to the Kernels Hub.](/skills/huggingface-triton-kernels/) [AI Tools ↗](/tools/)[Skills ↗](/skills/)[Agents ↗](/agents/)[MCP Servers ↗](/mcp-servers/)[Workflows ↗](/workflows/) ## Continue your investigation - [Choose a toolkit](/tools/) - [Inspect execution hosts](/agents/) - [Check tool access](/mcp-servers/) - [Check project dependencies](/check-repo/)