--- title: "VRAM & GPU Cost Calculator" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/89ccf316-64e9-4549-8b74-1e0830d099c6" updated: "2026-11-12" --- # VRAM \& GPU Cost 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 to run or fine-tune, and shows the cheapest GPU setups with live rental prices. For people planning to host or train a model. VRAM \& GPU Cost 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 fastgpu ## About ### What it is VRAM \& GPU Cost Calculator is a browser page that estimates the GPU memory a model needs. You pick a Hugging Face model, and it sizes memory for running, fine-tuning with LoRA, full fine-tuning, generating, transcribing or embedding. It then lists the cheapest GPU setups that fit and prices them live. ### Who it suits It suits people who plan to host or train a model and want to know what hardware to rent before they spend anything. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Machine learning | ML engineers | Check which GPUs can hold a model | Look up a model and read the cheapest setups | | Startups | Founders | Estimate the cost of serving a model to users | Read the modelled cost per million tokens | | Research | Researchers | Plan a fine-tuning run | Compare LoRA and full fine-tuning memory | | Hobbyist AI | Enthusiasts | Find out if a model fits a home GPU | Try the 4-bit setting | Skip it if you need a measured benchmark, since the serving cost is a model and real stacks usually do worse. ### Where to use it - A web browser, on the Space's page at Hugging Face, with no sign-in - A local copy of the page files on Windows, macOS or Linux, opened from index.html | Use case | Who | What they get back | | --- | --- | --- | | Size a model's memory | Engineers | GPU memory needed for the task and precision you choose | | Find cheap hardware | Founders | GPU setups that hold the model, priced live with a link to each offer | | Estimate serving cost | Operators | A modelled cost per 1M tokens, with the working shown | ### How to set it up The online page needs nothing and asks for no sign-in. A local copy needs Python only to fetch the files. - A web browser - Python and pip, only to fetch a local copy - Windows, macOS or Linux for the local copy 1. Install the Hugging Face hub library. 2. Download the Space's files into a folder named vram-and-gpu-cost-calculator. 3. Move into that folder. 4. Load index.html in a web browser. ```sh pip install -U huggingface_hub hf download fastgpu/vram-and-gpu-cost-calculator --repo-type space --local-dir vram-and-gpu-cost-calculator cd vram-and-gpu-cost-calculator ``` ### How to use it Pick a model in the search box, or paste its org and name. If your model is missing from the Hub, enter its size, for example 70B. Then read the memory figure, the GPU setups and the serving cost. | Tool | What it does | | --- | --- | | Search box | Lists thousands of popular public models, and falls back to the Hub's own search as you type | | Precision | Chooses the number format for the estimate: as the model was published, or 16, 8 or 4 bits | | Weights menu | For a GGUF repo, lists each build with its file size so memory is sized from the file you pick | | Requests and context | Sets how many requests run at once and how long the context is, one request at 4K tokens by default | ### Example You type 70B because the model is not on the Hub. The page shows how much GPU memory a model of that size needs, then lists the cheapest setups that hold it with their live prices. ### For newcomers and experts VRAM is the memory on a graphics card, and a model must fit in it to run well. Start by picking a model you know and read the cheapest setup the page lists. The weights part of the estimate multiplies the parameter count by the bytes each one takes: 2 for 16-bit, 1 for 8-bit, half a byte for 4-bit. The KV cache and some headroom are added on top. A full fine-tune counts about 16 bytes per parameter plus activations, while LoRA counts frozen weights, adapters and optimizer state. A mixture of experts is sized on all its parameters. For GGUF, the estimate uses the size of the build file you pick. In practice such a build holds more than four bits for every parameter. The serving cost is modelled from memory bandwidth, so treat it as a floor. Pick a model or type a size → Metadata is read from the Hub → Memory is sized for your task → Live GPU prices are matched → Cost per 1M tokens is modelled ### Good to know - Coverage: The built-in list holds 14,192 well-downloaded public models. The list skips models small enough to need no rented GPU, and builds aimed at Apple silicon, CPUs or phones. - Limit: The cost per 1M tokens is a model and not a measurement. - License: mit. - Cost to run: none named. The page needs no sign-in and uses a free match API. - Data it reaches: Each model's metadata on the Hub, such as the parameter count and quantization config, and FastGPU's match API for sizing and live prices. ## README ````markdown # VRAM & GPU Cost Calculator Estimates how much GPU memory a Hugging Face model needs to run or fine-tune, and shows the cheapest GPU setups with live rental prices. For people planning to host or train a model. Made by fastgpu 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: VRAM & GPU Cost Calculator is a browser page that estimates the GPU memory a model needs. You pick a Hugging Face model, and it sizes memory for running, fine-tuning with LoRA, full fine-tuning, generating, transcribing or embedding. It then lists the cheapest GPU setups that fit and prices them live. Who it suits: It suits people who plan to host or train a model and want to know what hardware to rent before they spend anything. ```table Industry | Who uses it | How they use it | Example Machine learning | ML engineers | Check which GPUs can hold a model | Look up a model and read the cheapest setups Startups | Founders | Estimate the cost of serving a model to users | Read the modelled cost per million tokens Research | Researchers | Plan a fine-tuning run | Compare LoRA and full fine-tuning memory Hobbyist AI | Enthusiasts | Find out if a model fits a home GPU | Try the 4-bit setting ``` Skip it if you need a measured benchmark, since the serving cost is a model and real stacks usually do worse. Where to use it: - A web browser, on the Space's page at Hugging Face, with no sign-in - A local copy of the page files on Windows, macOS or Linux, opened from index.html ```table Use case | Who | What they get back Size a model's memory | Engineers | GPU memory needed for the task and precision you choose Find cheap hardware | Founders | GPU setups that hold the model, priced live with a link to each offer Estimate serving cost | Operators | A modelled cost per 1M tokens, with the working shown ``` How to set it up: The online page needs nothing and asks for no sign-in. A local copy needs Python only to fetch the files. - A web browser - Python and pip, only to fetch a local copy - Windows, macOS or Linux for the local copy 1. Install the Hugging Face hub library. 2. Download the Space's files into a folder named vram-and-gpu-cost-calculator. 3. Move into that folder. 4. Load index.html in a web browser. ```sh pip install -U huggingface_hub hf download fastgpu/vram-and-gpu-cost-calculator --repo-type space --local-dir vram-and-gpu-cost-calculator cd vram-and-gpu-cost-calculator ``` How to use it: Pick a model in the search box, or paste its org and name. If your model is missing from the Hub, enter its size, for example 70B. Then read the memory figure, the GPU setups and the serving cost. ```table Tool | What it does Search box | Lists thousands of popular public models, and falls back to the Hub's own search as you type Precision | Chooses the number format for the estimate: as the model was published, or 16, 8 or 4 bits Weights menu | For a GGUF repo, lists each build with its file size so memory is sized from the file you pick Requests and context | Sets how many requests run at once and how long the context is, one request at 4K tokens by default ``` Example: You type 70B because the model is not on the Hub. The page shows how much GPU memory a model of that size needs, then lists the cheapest setups that hold it with their live prices. For newcomers and experts: VRAM is the memory on a graphics card, and a model must fit in it to run well. Start by picking a model you know and read the cheapest setup the page lists. The weights part of the estimate multiplies the parameter count by the bytes each one takes: 2 for 16-bit, 1 for 8-bit, half a byte for 4-bit. The KV cache and some headroom are added on top. A full fine-tune counts about 16 bytes per parameter plus activations, while LoRA counts frozen weights, adapters and optimizer state. A mixture of experts is sized on all its parameters. For GGUF, the estimate uses the size of the build file you pick. In practice such a build holds more than four bits for every parameter. The serving cost is modelled from memory bandwidth, so treat it as a floor. ```flow Pick a model or type a size Metadata is read from the Hub Memory is sized for your task Live GPU prices are matched Cost per 1M tokens is modelled ``` Good to know: - Coverage: The built-in list holds 14,192 well-downloaded public models. The list skips models small enough to need no rented GPU, and builds aimed at Apple silicon, CPUs or phones. - Limit: The cost per 1M tokens is a model and not a measurement. - License: mit. - Cost to run: none named. The page needs no sign-in and uses a free match API. - Data it reaches: Each model's metadata on the Hub, such as the parameter count and quantization config, and FastGPU's match API for sizing and live prices. ## On Hugging Face, 13 November 2026 - Likes: 39 - Built with: static - License: mit ## Links - Space: https://huggingface.co/spaces/fastgpu/vram-and-gpu-cost-calculator - Files: https://huggingface.co/spaces/fastgpu/vram-and-gpu-cost-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 [fastgpu/vram-and-gpu-cost-calculator on Hugging Face](https://huggingface.co/spaces/fastgpu/vram-and-gpu-cost-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 | | --- | --- | --- | --- | --- | | VRAM \& GPU Cost Calculator (this page) | 39 | static | mit | 2026-11-12 | | [LLM VRAM Calculator](https://mindket.com/p/c4024e07-c158-482f-a023-686354790fff) | 3 | static | mit | 2026-09-30 | | [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 | | [Z-Image Lookalike LoRA Index](https://mindket.com/p/b982ec25-ae82-40ac-9834-49980598d3bd) | 189 | static | apache-2.0 | 2026-10-30 | | [Granite Docling 258M WebGPU](https://mindket.com/p/d4317b48-bbb7-4a8d-8c83-a8c60857aad1) | 163 | static | apache-2.0 | 2025-10-06 | 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: 2026-11-12 - First published: 2026-10-08 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fastgpu/vram-and-gpu-cost-calculator on Hugging Face](https://huggingface.co/spaces/fastgpu/vram-and-gpu-cost-calculator) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/89ccf316-64e9-4549-8b74-1e0830d099c6) - [Its page on Mindket](https://mindket.com/p/89ccf316-64e9-4549-8b74-1e0830d099c6)