The thing you were about to install already runs in a browser
The hardest part of using an open model is not the model. It is CUDA versions, a Python environment that fights you, six gigabytes of dependencies and a laptop that does not have the memory. A Space skips all of it. Someone has already written the code, pinned the dependencies and pointed it at a model, and the Hub runs the whole thing behind a web page.
Search the Spaces tab for what you want in plain words — *remove background*, *transcribe audio*, *upscale*, *caption image*, *extract table from pdf*. Sort by likes or trending to find the ones people actually use. A phone browser works perfectly well; a Space is just a web page, and the only real constraint on a phone is uploading a large file over a mobile connection.
Why it is asleep, and why there is a queue
Two things confuse new users, and both have simple mechanisms.
Sleeping. A Space on free hardware pauses itself after a stretch of inactivity — 48 hours by default — to stop the Hub running thousands of idle containers. Your visit wakes it. The container has to start, install nothing (it is already built) but load the model into memory, which for a mid-sized model is tens of seconds. A blank page or a *Space is starting* message is not a broken Space. Wait a minute before concluding anything.
The queue. Gradio, the framework most Spaces use, processes requests one at a time by default. If nine other people clicked *Submit* just before you, you are ninth. The interface usually tells you your position. On a popular Space at a busy hour this is the whole of the experience, and it is the strongest argument for duplicating it, which is the next lesson.
The hardware shelf
Every Space runs on a tier chosen by its owner.
- CPU basic, the free default: roughly 2 virtual CPUs, 16 GB of RAM, around 50 GB of disk, no GPU. Free with no time limit.
- ZeroGPU: a shared pool of high-end GPU capacity, allocated for the duration of a single function call. Free to use with a per-person quota, larger for PRO subscribers. Because the GPU is acquired per call, there is start-up overhead on each request and long-running or stateful GPU work does not fit the model well.
- Rented GPUs: T4, L4, A10G and up, billed by the hour. Roughly forty cents to a few dollars an hour depending on the card, as of late 2025. Billed for wall-clock time while the Space is running, not for requests served.
What free CPU genuinely does
More than people expect, as long as the work is one file at a time:
- Background removal and matting.
- Speech to text with Whisper at the tiny, base or small sizes, on clips of a few minutes.
- Image captioning, OCR, document layout extraction.
- Text classification, named entity extraction, translation with small models.
- Embedding text for search.
- Modest image upscaling, audio trimming, format conversion, subtitle generation.
And what it does not do, with the reason:
- Text-to-image at modern quality. SDXL or Flux on two CPU cores is minutes to tens of minutes per image, and the larger models will not fit in 16 GB at full precision at all. The arithmetic, not the software, is what stops you.
- Chat with a 7B model at conversational speed. It will run and it will produce a word every second or two. Fine for a demo, unusable for a conversation.
- Video generation, or anything real-time.
If a Space you find is doing one of those things for free, it is on ZeroGPU or its owner is paying, and both mean queues.
Before you upload anything
A Space is a stranger's program. Open its Files tab and read app.py — most are under two hundred lines and you can see exactly what happens to your file. Check the *Community* tab too: the discussions there are where people report that a Space has been broken for three weeks.
And apply the rule from the last lesson without negotiating with yourself: if the file is a client's, is under an agreement, or contains someone's personal data, do not upload it to a Space you do not control. Duplicate it instead, which takes one click and is the next lesson.
Do this now
Find a Space that does something you currently pay for or currently do by hand. Run it once on a real file. Then open its app.py and find the line where the model is loaded — that name is a model on the Hub you could run yourself.
Before you move on