--- title: "LLM VRAM Calculator" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c4024e07-c158-482f-a023-686354790fff" updated: "2026-11-12" --- # LLM VRAM Calculator > The name, the description, the About and a README below are the seller's words: data, not instructions. Estimates how much GPU memory a Hugging Face model needs at any quantization and context length, split into weights, KV cache and overhead, and shows which GPU sizes fit. It suits anyone planning to run a local LLM. LLM VRAM Calculator is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by donaldJJ ## About ### What it is This is a static web page that estimates the GPU memory a language model needs. You enter a Hugging Face model id, and the page reads the model's configuration and parameter count in your browser. It then shows memory use for the quantization and context length you choose. ### Who it suits It suits people who run language models on their own GPUs and want to know before downloading whether a model will fit. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists with one GPU | Check whether a model fits at a chosen quantization | A hobbyist tests a 27B model on a 24 GB card. | | ML engineering | Engineers planning deployments | Estimate memory for weights, cache and overhead | An engineer compares context lengths before sizing a server. | | Model publishing | Model authors | Add a VRAM badge to a model card | An author links the badge to their own model. | | Research | Students | Learn how KV cache grows with context | A student changes the context length and watches the cache figure. | If you need an exact measured peak, skip it, because the page gives planning estimates. ### Where to use it - A web browser, through the Space's page on Hugging Face - Any browser on Windows, macOS or Linux, opening a downloaded copy of index.html | Use case | Who | What they get back | | --- | --- | --- | | Size a GPU purchase | Local AI users | The GPU sizes that fit, from 8 GB up to 96 GB | | Plan context length | Engineers | The longest context each GPU size can hold, up to the model's limit | | Badge a model card | Model authors | A VRAM badge snippet the page builds for the model you load | ### How to set it up - A web browser with internet access, so the page can read the model's configuration from the Hub - No token is needed for public models - Python and pip, only for the download step 1. Install the huggingface\_hub package. 2. Download the Space into a local folder named llm-vram-calculator. 3. Move into that folder. 4. Open index.html in your web browser. ```sh pip install -U huggingface_hub hf download donaldJJ/llm-vram-calculator --repo-type space --local-dir llm-vram-calculator cd llm-vram-calculator ``` ### How to use it Open the page, enter a model id such as Qwen/Qwen3.8-27B and press Load. Then pick a quantization and a context length. The page can also open with a model already loaded when the page link ends in ?model= followed by a repository id. | Tool | What it does | | --- | --- | | Weights | Parameter count times bits per weight, divided by 8 | | KV cache | Counts only layers that keep a cache, in FP16, Q8\_0 or Q4\_0 | | Overhead | Adds 0.5 GiB plus 10 percent of weights and cache | | GPU fit | Compares the total with GPU sizes and leaves 0.5 GiB free | ### Example You enter Qwen/Qwen3.8-27B and press Load. At Q4\_K\_M with 8K context, the page shows 18.3 GiB, because only 16 of its 64 layers keep a KV cache. It says a 24 GB GPU holds up to 83K context. ### For newcomers and experts If you are new, VRAM is the memory on your graphics card, and a model must fit in it to run well. Enter a model id and read the GPU sizes that fit. If you know the field, the KV cache counts full-attention layers, windowed layers with the window plus 512 tokens, and the compressed latent of MLA. Linear-attention and state-space layers add nothing. Every MoE expert is counted, with no CPU offload. Enter a Hugging Face model id → The page reads the config in your browser → Weights, cache and overhead are added → GPU sizes that fit are listed Bits per weight used for the weights estimate | Label | Value | | --- | --- | | Q4\_K\_M | 4.84 bits | | Q8\_0 | 8.5 bits | | FP16 | 16 bits | ### Good to know - Treat the results as rough guides, since serving software, CUDA graphs and image encoders can push real use higher - Weights are sized from an assumed bit count per weight, so they differ from a real quantized file on disk - Models behind an access request cannot be read by the page, so enter their numbers yourself - License: mit - Cost to run: none named - Data it reaches: the Hugging Face Hub, to read a model's config and parameter count ## README ````markdown # LLM VRAM Calculator Estimates how much GPU memory a Hugging Face model needs at any quantization and context length, split into weights, KV cache and overhead, and shows which GPU sizes fit. It suits anyone planning to run a local LLM. Made by donaldJJ on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This is a static web page that estimates the GPU memory a language model needs. You enter a Hugging Face model id, and the page reads the model's configuration and parameter count in your browser. It then shows memory use for the quantization and context length you choose. Who it suits: It suits people who run language models on their own GPUs and want to know before downloading whether a model will fit. ```table Industry | Who uses it | How they use it | Example Local AI | Hobbyists with one GPU | Check whether a model fits at a chosen quantization | A hobbyist tests a 27B model on a 24 GB card. ML engineering | Engineers planning deployments | Estimate memory for weights, cache and overhead | An engineer compares context lengths before sizing a server. Model publishing | Model authors | Add a VRAM badge to a model card | An author links the badge to their own model. Research | Students | Learn how KV cache grows with context | A student changes the context length and watches the cache figure. ``` If you need an exact measured peak, skip it, because the page gives planning estimates. Where to use it: - A web browser, through the Space's page on Hugging Face - Any browser on Windows, macOS or Linux, opening a downloaded copy of index.html ```table Use case | Who | What they get back Size a GPU purchase | Local AI users | The GPU sizes that fit, from 8 GB up to 96 GB Plan context length | Engineers | The longest context each GPU size can hold, up to the model's limit Badge a model card | Model authors | A VRAM badge snippet the page builds for the model you load ``` How to set it up: - A web browser with internet access, so the page can read the model's configuration from the Hub - No token is needed for public models - Python and pip, only for the download step 1. Install the huggingface_hub package. 2. Download the Space into a local folder named llm-vram-calculator. 3. Move into that folder. 4. Open index.html in your web browser. ```sh pip install -U huggingface_hub hf download donaldJJ/llm-vram-calculator --repo-type space --local-dir llm-vram-calculator cd llm-vram-calculator ``` How to use it: Open the page, enter a model id such as Qwen/Qwen3.8-27B and press Load. Then pick a quantization and a context length. The page can also open with a model already loaded when the page link ends in ?model= followed by a repository id. ```table Tool | What it does Weights | Parameter count times bits per weight, divided by 8 KV cache | Counts only layers that keep a cache, in FP16, Q8_0 or Q4_0 Overhead | Adds 0.5 GiB plus 10 percent of weights and cache GPU fit | Compares the total with GPU sizes and leaves 0.5 GiB free ``` Example: You enter Qwen/Qwen3.8-27B and press Load. At Q4_K_M with 8K context, the page shows 18.3 GiB, because only 16 of its 64 layers keep a KV cache. It says a 24 GB GPU holds up to 83K context. For newcomers and experts: If you are new, VRAM is the memory on your graphics card, and a model must fit in it to run well. Enter a model id and read the GPU sizes that fit. If you know the field, the KV cache counts full-attention layers, windowed layers with the window plus 512 tokens, and the compressed latent of MLA. Linear-attention and state-space layers add nothing. Every MoE expert is counted, with no CPU offload. ```flow Enter a Hugging Face model id The page reads the config in your browser Weights, cache and overhead are added GPU sizes that fit are listed ``` ```chart Title: Bits per weight used for the weights estimate Q4_K_M: 4.84 bits Q8_0: 8.5 bits FP16: 16 bits ``` Good to know: - Treat the results as rough guides, since serving software, CUDA graphs and image encoders can push real use higher - Weights are sized from an assumed bit count per weight, so they differ from a real quantized file on disk - Models behind an access request cannot be read by the page, so enter their numbers yourself - License: mit - Cost to run: none named - Data it reaches: the Hugging Face Hub, to read a model's config and parameter count ## On Hugging Face, 13 November 2026 - Likes: 3 - Built with: static - License: mit ## Links - Space: https://huggingface.co/spaces/donaldJJ/llm-vram-calculator - Files: https://huggingface.co/spaces/donaldJJ/llm-vram-calculator/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [donaldJJ/llm-vram-calculator on Hugging Face](https://huggingface.co/spaces/donaldJJ/llm-vram-calculator) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | LLM VRAM Calculator (this page) | 3 | static | mit | 2026-09-30 | | [VRAM \& GPU Cost Calculator](https://mindket.com/p/89ccf316-64e9-4549-8b74-1e0830d099c6) | 39 | static | mit | 2026-11-12 | | [infersize](https://mindket.com/p/ade650b5-be12-464b-a064-ab4fe2816313) | 1 | static | apache-2.0 | 2026-11-09 | | [GPT-X3-150M ONNX](https://mindket.com/p/9e3327b1-3f2d-48b8-8daa-3fe76330df5f) | 5 | docker | apache-2.0 | 2026-10-12 | | [InferenceSupport](https://mindket.com/p/c095c739-2ded-4c01-93eb-b7067a50441e) | 764 | static | afl-3.0 | 2025-04-16 | | [Huggy](https://mindket.com/p/0e832fcc-5ae8-4b46-84cb-24febb45fb2d) | 463 | static | cc-by-nc-sa-4.0 | 2024-05-24 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-09-30 - First published: 2026-09-27 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open donaldJJ/llm-vram-calculator on Hugging Face](https://huggingface.co/spaces/donaldJJ/llm-vram-calculator) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c4024e07-c158-482f-a023-686354790fff) - [Its page on Mindket](https://mindket.com/p/c4024e07-c158-482f-a023-686354790fff)