Hugging Face Spaces Free Tier: Host AI Demos & ZeroGPU

Quick answer: Hugging Face Spaces gives you free, no-credit-card hosting for AI demos: a public *.hf.space URL, Git-push deploys, a 2 vCPU / 16 GB CPU container, and — via ZeroGPU — time-sliced access to a shared Nvidia A100 80GB. Free CPU is unmetered; free GPU is quota-capped in seconds per day. No other consumer host hands you a real datacenter GPU for $0.

Spaces is the most under-used free platform in AI hosting. The catch: it’s built for one job — hosting an interactive AI demo. It’s not a generic VPS, a 24/7 backend, or a place for a multi-page Next.js app. Used in its lane, it replaces a pipeline that would cost $20-50/month on Render or Railway.

Free Tier at a Glance

Resource	Free Tier Includes	Notes
Hardware	CPU Basic: 2 vCPU, 16 GB RAM	Default; no payment method required
Storage	~50 GB ephemeral disk	Wiped on each rebuild; use the Hub for weights
Bandwidth	Unmetered (fair use)	No published hard cap
Build minutes	Unmetered	Triggered by every Git push
Sleep policy	Sleeps after 48 hours idle	First visit wakes it; cold start ~30-90s
GPU access	ZeroGPU — Nvidia A100 80GB	Free, shared, time-sliced via @spaces.GPU
ZeroGPU quota (free)	Limited daily seconds	Visible in account settings; resets daily
ZeroGPU quota (Pro $9/mo)	~8x higher daily seconds	Same hardware, more time
Region	Not user-selectable	Free Spaces run on Hugging Face’s managed infrastructure; no region picker — check current docs
Commercial use	Allowed	No non-commercial clause, but the free tier gives no uptime guarantee
Custom domain	Paid Pro feature	Free Spaces serve from *.hf.space only
Private Spaces	Paid Pro feature	Free Spaces are public by default
SDKs supported	Gradio, Streamlit, Docker, Static, JupyterLab	All work on free CPU; ZeroGPU is Gradio + Docker

The short version: free CPU is unlimited and unmetered; free GPU is shared and quota-capped. For a hobby demo run a few times a day, the free tier is genuinely free forever.

How Spaces Works

Every Space is a Git repo on huggingface.co. You push code, the Hub builds a Docker image, and a container starts behind a public hf.space URL. Two design decisions make it unusual:

• Git is the deploy interface. No deploy CLI, no build panel — push to main and the Space rebuilds, the way Vercel, Netlify, and Cloudflare Pages work, except Hugging Face hosts the Git remote itself.
• Weights live on the Hub, not in your image. A 7B model is 14 GB. Instead of bundling it, the SDK pulls weights at runtime via huggingface_hub, cached on a shared layer so rebuilds don’t re-download.

The result fits the shape of an AI demo, not a generic web app.

The ZeroGPU System

ZeroGPU is what makes Spaces different from every other free host. You don’t reserve a GPU; your Space declares which functions need one, and a scheduler allocates a pooled A100 for the duration of that call:

• Your Space runs on the CPU container by default.
• Functions decorated with @spaces.GPU get pulled into a GPU worker when invoked.
• Each call counts against your daily quota, measured in seconds of actual GPU time.
• Cold-start to an A100 worker is typically under 5 seconds, often under 2.
• Free accounts get a smaller daily quota than Pro ($9/mo), but ZeroGPU is not paywalled.

This works because AI demos are bursty — a model runs inference for a few seconds, then sits idle while a human reads the output. ZeroGPU multiplexes those bursts across many Spaces, charging quota only for compute seconds. For a hobbyist, that means a real image-gen demo, a Whisper tool, or a fine-tuned 7B chatbot on a public A100-backed URL, at no cost.

Pick an SDK

• Gradio — the default for ~90% of Spaces. Built by Hugging Face, deepest ZeroGPU integration (one-line decorator), best for demos with sliders, image/audio inputs, or a chat UI.
• Streamlit — best for data-heavy dashboards over a DataFrame. Does not officially support ZeroGPU; prefer Gradio for inference demos.
• Docker — for anything the framework SDKs can’t do: FastAPI backend, custom React frontend, non-Python runtime. Must listen on port 7860. Supports ZeroGPU via the spaces package.
• Static — a single-page HTML demo with no server (transformers.js, WebGPU). No container, no cold start, no quota.
• JupyterLab — a hosted notebook on a public URL. Less used, since Colab and Kaggle cover the same niche with free GPUs.

Deploy Your First Space (Gradio)

Create a free account at huggingface.co/join, choose New Space, pick Gradio + CPU Basic — Free + Public. You now have a Git repo. Clone it and add code:

git clone https://huggingface.co/spaces/your-name/your-space
cd your-space

app.py:

import gradio as gr
from transformers import pipeline

pipe = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

def classify(text):
    result = pipe(text)[0]
    return f"{result['label']} ({result['score']:.2%})"

demo = gr.Interface(
    fn=classify,
    inputs=gr.Textbox(label="Your text"),
    outputs=gr.Textbox(label="Sentiment"),
    title="Sentiment Analysis Demo",
    examples=["I loved this movie.", "The plot was a mess."],
)

if __name__ == "__main__":
    demo.launch()

requirements.txt:

gradio
transformers
torch

Then push:

git add app.py requirements.txt
git commit -m "Initial sentiment demo"
git push

Within 30-60 seconds the Space builds and your demo is live at your-name-your-space.hf.space. The Hub generates a TLS URL, pulls the weights automatically, and serves the app behind a queue. Every subsequent git push redeploys. No dashboard, no DNS step.

Using ZeroGPU: One Decorator, One Setting

DistilBERT runs fine on free CPU. ZeroGPU earns its keep on models that need a GPU — Stable Diffusion, Whisper, Llama 3, Flux. To enable it: switch hardware to ZeroGPU in settings, add the spaces package to requirements.txt, and decorate the heavy function:

import gradio as gr
import spaces
import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    torch_dtype=torch.float16,
).to("cuda")

@spaces.GPU(duration=60)
def generate(prompt: str):
    image = pipe(prompt, num_inference_steps=25).images[0]
    return image

demo = gr.Interface(
    fn=generate,
    inputs=gr.Textbox(label="Prompt"),
    outputs=gr.Image(label="Generated"),
)

demo.launch()

Three things to notice:

• duration=60 is the max seconds the call may hold the GPU (default 60). Set it lower for fast functions so quota accounting is accurate, higher for slow ones to avoid timeouts.
• The pipeline loads at module level, outside the decorated function — ZeroGPU moves the loaded model to the GPU worker on call, then back. Load once, dispatch many times.
• Quota is consumed in seconds of actual runtime: a 4-second generation costs 4 seconds, not 60. Free quota resets every 24 hours.

Limits and Gotchas

• Sleep after 48h idle. The next visitor triggers a 30-90s cold start. Fine for personal demos; there’s no free “always-on” setting.
• Storage is ephemeral. The ~50 GB disk is wiped on every rebuild. For persistent state, pay $5/mo for the Persistent Storage add-on or use an external store (Supabase, Neon, S3, a Hub dataset). Hub-pulled weights are cached and not re-downloaded.
• Port 7860 is hardcoded. Gradio/Streamlit do this by default; Docker Spaces must EXPOSE 7860.
• Public repos by default. Never hardcode keys — use Settings → Secrets, which injects them as env vars at runtime without committing them.
• Quota is a per-account pool shared across all your ZeroGPU Spaces. One busy Space starves the others. Pro’s ~8x quota is the main reason heavy demo-shippers upgrade.
• No custom domain on free. You get username-spacename.hf.space. Custom domains are Pro-only.

Spaces vs Alternatives

• vs Google Colab — Colab gives an interactive T4 notebook, but no public URL others can hit. Use Colab to experiment; use Spaces to share the result.
• vs Replicate — Replicate serves a model as a per-second API with no UI (and no free tier). Spaces ships the UI; Replicate ships the API.
• vs Modal — Modal is Python-native serverless GPU with monthly free credits and no built-in UI. Spaces is “AI demo on a GPU”; Modal is “Python function on a GPU.”
• vs Ollama — Ollama runs models locally, fully private, on your own hardware. Use it for local/private work; use Spaces when you need a public URL.
• vs Cloudflare Workers AI — Workers AI serves a fixed catalog of pre-deployed models; you can’t upload a fine-tune. For your own trained model, Spaces is the only option of the two.

Decision tree

• Share an AI demo with a public URL? → Spaces
• Private API endpoint billed per call? → Replicate or Modal
• 24/7 production backend with custom domain + SLA? → Render, Railway, or a real cloud
• Free GPU for personal experimentation only? → Google Colab or Kaggle
• Keep weights and inference private? → Ollama on your own hardware

What the Free Tier Comfortably Handles

• Demo a fine-tuned model — a 50-line Gradio chat + @spaces.GPU = a public 7B chatbot in 20 minutes.
• Image generation — Stable Diffusion, Flux, Kandinsky: pipeline at module level, @spaces.GPU on generate, gr.Image output.
• Whisper transcription — openai/whisper-large-v3; the free A100 does ~30 min of audio in ~30s. Pairs with the free Whisper APIs we benchmarked.
• RAG Q&A — a free embedding model + in-memory Chroma + a free LLM, chunks held in RAM. See our free vector database guide for production alternatives.
• Side-by-side model comparison — two pipelines, one input, two columns.

For anything past toy demos, instrument with a free tracing layer like Langfuse — a few import lines wrap every model call for prompt, completion, latency, and cost.

Frequently Asked Questions

Do I need a credit card?

No. Free CPU Basic and ZeroGPU access work on accounts with no payment method. You only add a card to upgrade to a paid GPU tier or Pro plan.

Is ZeroGPU really free, and what GPU is it?

Yes — a daily quota measured in seconds of GPU time, on a time-sliced Nvidia A100 80GB. Free accounts get a smaller quota than Pro ($9/mo), but there’s no signup credit that runs out; the quota resets every 24 hours indefinitely. You don’t pick the GPU — the scheduler assigns one when your @spaces.GPU function is invoked.

Can I use Spaces as a backend API?

Sort of. A Gradio app exposes a REST endpoint at /api/predict, and a Docker Space can expose any FastAPI route — but the free tier gives no uptime guarantee. If customers pay to call this API, host on Replicate, Modal, or a real cloud.

How do I add an API key without leaking it?

Space → Settings → Variables and secrets → New secret. It’s exposed as an environment variable at runtime and never enters the Git repo. This is how you wire in OpenRouter, Together AI, or any paid API.

Getting Started

1. Sign up for Hugging Face — 60 seconds.
2. Create a new Gradio Space on the free CPU tier.
3. Push the sentiment snippet above and watch it deploy. Add @spaces.GPU when you need the A100. The price stays zero.

Related Reads

• 7 Best Free Web Hosting Platforms for Developers in 2026
• Render Free Hosting Review 2026: Deploy Web Apps, Databases, and Cron Jobs for Free
• Vercel vs Netlify vs Cloudflare Pages: Free Frontend Hosting Compared
• Ollama: Run AI Models Locally for Free
• Langfuse: Free Open-Source LLM Observability

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Originally published at toolfreebie.com.