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Hugging Face is both a public machine-learning repository and a way to run and call AI applications. Use the Hub to find models and datasets, the huggingface_hub Python or JavaScript clients to manage them, and Spaces to publish interactive demos. A Gradio Space automatically exposes an API, so a browser demo can also become a callable service. Free GPU access is real but limited: current ZeroGPU quotas provide 2 minutes per day without signing in, 5 minutes for free accounts, and 40 minutes for PRO accounts. Longer or predictable workloads need paid Space hardware or a paid Inference Endpoint. This guide shows the complete path from account creation and model selection to authenticated API calls, privacy choices, quotas, billing and production deployment.
What is Hugging Face, and which part should I use?
Think of Hugging Face as three connected products rather than a single model-hosting website:
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- The Hub stores versioned repositories for models, datasets and Spaces. Each repository includes files, metadata and usually a model card or dataset card explaining intended use and limits.
- Spaces run deployable applications. Gradio is the quickest route for a Python demo, while Docker supports a custom runtime and static HTML serves a front end without a server process.
- APIs and clients let scripts download files, inspect repositories, submit inference requests or call a Space without using its web page. Hugging Face maintains official Python and JavaScript clients, and Hub API requests are rate-limited.
Choose a model repository when you need weights or a dataset; choose a Space when you need an interactive application; choose an Inference Endpoint when an application needs dedicated capacity and predictable latency.
How do I create an account and choose Space visibility?
Create or sign in to a Hugging Face account before creating private repositories, using authenticated APIs or consuming most hosted compute. During Space creation, select a visibility level deliberately because it controls who can see the source, assets and application.
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Public Spaces
Public Spaces are discoverable and their repository files can be viewed by anyone. They are appropriate for open demos, tutorials and reproducible examples. Never place API keys, private customer data or proprietary model weights in a public repository.
Protected Spaces
Protected visibility keeps the Space out of normal public discovery while allowing access to people who have permission through Hugging Face authentication. It is useful for a limited audience or an internal preview, but it is not the same as an application with your own user-management layer.
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Private Spaces
Private Spaces restrict repository and app access to you and explicitly authorized collaborators. API callers must supply a token with at least read access. Check the Space’s access settings and token scope whenever a browser can open the app but a script receives an authorization error.
How do I find a model or dataset on the Hub?
Search by task, language, library, license and downloads, then read the repository documentation before downloading anything. Popularity is not a substitute for suitability or permission.
- Open the model or dataset repository and inspect its files, revision history and tags.
- Read the model card or dataset card for intended use, known limitations, evaluation details, safety notes and citation requirements.
- Confirm the license and any access conditions. Some repositories require you to accept terms or request access before files can be downloaded.
- Check the documented input and output format. A text-generation model, an image model and an embedding model require different preprocessing and return different data.
- Pin a revision, commit or version in automated work when reproducibility matters instead of silently following the repository’s latest main branch.
A repository’s README is guidance, not a guarantee that the model is unbiased, secure or suitable for production. Treat downloaded model code as executable software: review custom code, isolate execution where practical and keep credentials outside the repository.
How do I install and authenticate the Hugging Face client?
Install the official Python client for Hub operations, then authenticate with a token created in your account settings. Use a read token for downloading private assets and a write-capable token only in the deployment that truly needs to upload or modify repositories.
python -m pip install -U huggingface_hub
hf auth loginThe login command stores credentials for the local user. In CI or a server, provide the token through a secret or environment variable rather than committing it to source control.
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export HF_TOKEN="your_token"
python -c "from huggingface_hub import whoami; print(whoami())"For programmatic use, the client can receive the token explicitly:
from huggingface_hub import HfApi
import os
api = HfApi(token=os.environ["HF_TOKEN"])
for item in api.list_models(limit=5):
print(item.id)The JavaScript client provides the equivalent Hub operations for Node or browser-based tooling. Consult the current API/OpenAPI reference for method names and payloads, and design around rate limits: cache repository metadata, avoid polling in tight loops and implement exponential backoff after a throttling response.
How do I download and use a model or dataset?
Use the repository’s documented loading method and keep downloaded files in a cache so repeated runs do not redownload large artifacts.
Download selected files
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="owner/repository",
filename="config.json",
revision="main"
)
print(path)Replace the example repository and filename with values from the repository page. For private content, pass a read token or authenticate the environment first. For a complete snapshot, the client also provides snapshot-download functionality; pin a commit hash when a build must remain identical over time.
Load through a task library
Many model cards show a task-specific example using the Transformers, Diffusers, sentence-transformers or datasets ecosystems. Follow that example’s preprocessing and device settings rather than assuming every repository can be loaded by the same class. Verify tensor dtype, maximum input length, image dimensions and output schema before sending user data.
Use a dataset responsibly
Dataset cards may document licensing, personally identifiable information, demographic imbalance and intended research use. Preserve the card’s attribution and restrictions in your own project, and do not expose a private dataset through a public Space or client-side application.
How do I create a Hugging Face Space?
From your profile, choose New Space, name the repository, select visibility and choose an SDK. The three supported patterns serve different needs:
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| Space type | Best use | Runtime and cost behavior |
|---|---|---|
| Gradio | Fast Python interface for a model or workflow | Simple UI components and an automatically generated callable API; CPU, ZeroGPU or paid hardware |
| Docker | Custom web server, system packages or non-Gradio framework | Full container control; requires compute while running |
| Static HTML | Documentation, a client-side demo or a front end calling another service | Static Spaces are free for everyone and do not provide server-side model compute |
Build a minimal Gradio Space
A basic repository normally contains an app.py and a dependency file such as requirements.txt. Keep the interface explicit so the generated API has understandable input and output names.
import gradio as gr
def greet(name):
return f"Hello, {name}!"
with gr.Blocks() as demo:
name = gr.Textbox(label="Name")
output = gr.Textbox(label="Greeting")
gr.Button("Run").click(greet, inputs=name, outputs=output, api_name="greet")
demo.launch()Commit the files to the Space. The build log shows dependency installation and startup errors; the app page shows the rendered interface after a successful build.
When Docker or static HTML is the better choice
Choose Docker when your application needs a custom process, specialized system libraries or a framework that is not represented by Gradio components. Choose static HTML when all computation occurs in the browser or when the page calls an external API. A static Space cannot replace a GPU-backed server for a large model.
How can I call a Gradio Space through an API?
Every Gradio Space is automatically available as an API endpoint. The Space’s Use via API panel is the authoritative contract: it lists endpoint names, input types, output types and generated examples for that specific revision.
- Open the Space and select Use via API.
- Record the named endpoint, often the
api_nameassigned in the Gradio code, and inspect each parameter’s expected type. - Choose a client. Use
gradio_clientin Python,@gradio/clientin JavaScript, or the displayed REST request for a language-neutral integration. - Pass a token with read permission when the Space is private or protected. Keep that token on your server; do not embed it in browser JavaScript delivered to untrusted users.
- Handle queueing and asynchronous results. A request may return an event or job identifier before inference finishes, particularly on shared GPU hardware.
Python client pattern
from gradio_client import Client
import os
client = Client(
"owner/space-name",
hf_token=os.environ.get("HF_TOKEN")
)
result = client.predict(
"hello",
api_name="/greet"
)
print(result)Copy the exact argument order and endpoint name from the Space documentation; the sample values above are illustrative. If the Space author changes the interface, regenerate your client call from the updated API panel.
JavaScript client pattern
import { Client } from "@gradio/client";
const app = await Client.connect("owner/space-name", {
hf_token: process.env.HF_TOKEN
});
const result = await app.predict("/greet", ["hello"]);
console.log(result.data);For REST, use the request shown by the Space’s documentation rather than guessing a URL or JSON shape. This avoids mismatches for file uploads, streaming outputs and endpoints with multiple inputs.
Is Hugging Face GPU access free?
Only in a limited, shared form. ZeroGPU provides quota-based access to eligible Spaces; it is not an unlimited dedicated graphics processor. Current daily allowances documented by Hugging Face are:
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| Access level | ZeroGPU allowance | What it means |
|---|---|---|
| Unauthenticated visitor | 2 minutes per day | Brief testing without an account |
| Free account | 5 minutes per day | Small experiments and demos |
| PRO account | 40 minutes per day | More interactive testing, still quota-limited |
These figures are from Hugging Face documentation accessed October 25, 2026, and quotas or eligibility can change. Shared capacity means requests may wait in a queue. A short model call can still feel slow if the Space is busy, and a long-running job can exhaust the daily allowance before it completes.
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ZeroGPU schedules eligible functions on shared hardware and enforces daily usage. A paid Space reserves the selected hardware while it is starting or running, giving you a clearer capacity expectation but charging by usage. Neither option removes the need to optimize model size, batching and timeout handling.
How much does a Hugging Face Space GPU cost?
Space hardware is billed by the minute while the Space is starting or running. The following examples are the current hourly rates shown in Hugging Face’s Spaces overview, accessed October 25, 2026:
| Hardware example | Rate | Approximate cost for 10 hours of billed runtime |
|---|---|---|
| Nvidia T4 small | $0.40/hour | $4.00 |
| 1× Nvidia L4 | $0.80/hour | $8.00 |
| Nvidia A100 large | $2.50/hour | $25.00 |
The 10-hour figures are simple multiplication of the listed hourly rates, not a promotional price or a guaranteed monthly bill. Actual charges depend on the hardware selected and the minutes consumed. Pause or stop an idle Space, and configure sleep where available, because upgraded hardware continues to cost money while the Space is starting or running.
When should I use a paid Space instead of an Inference Endpoint?
Use a paid Space when you need a user-facing demo, custom UI or application logic. Use an Inference Endpoint when you need a dedicated deployment path for a model, predictable capacity and a service-oriented interface.
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| Decision factor | Space | Inference Endpoint |
|---|---|---|
| Primary purpose | Interactive app and demo | Dedicated model inference service |
| Runtime choices | Static, CPU, ZeroGPU or paid GPU | Configured dedicated compute |
| Scaling and latency | Shared queue or app-specific capacity | Designed for production traffic and predictable capacity |
| Billing | Hardware usage, computed by the minute | Usage-based pricing for the selected endpoint compute |
| Account requirement | Free options exist; paid hardware requires billing setup | Payment method required |
Hugging Face’s current access guide gives starting examples of $0.032 per CPU core-hour and $0.50 per GPU-hour for Inference Endpoints. These are starting examples, not a universal quote: the configured hardware, region and runtime determine the actual charge.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I call hosted inference from my own server?
Use InferenceClient when you want provider-backed inference without building a Space yourself. The client routes requests to a supported provider and exposes task-oriented methods. Provider availability, model support and billing differ, so check the selected model’s provider information before coding a dependency on it.
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from huggingface_hub import InferenceClient
import os
client = InferenceClient(
provider="auto",
api_key=os.environ["HF_TOKEN"]
)
response = client.text_generation(
"Write a one-sentence summary of this text.",
model="owner/model-name"
)
print(response)Hugging Face may apply monthly credits to eligible accounts, while providers can impose their own usage pricing or limits. Treat credits as an allowance rather than a promise of free, unlimited inference, and monitor consumption in the account billing view.
What commonly goes wrong, and how do I fix it?
The Space builds but will not start
- Read the build log for a missing package, incompatible Python version or failed system dependency.
- Pin a compatible dependency version in
requirements.txtand rebuild. - Check that the application listens on the port and startup command expected by the selected SDK or Docker configuration.
An API call returns “not found” or “unauthorized”
- Verify the exact owner and Space name, including capitalization.
- For a private or protected Space, pass a valid read-capable token and confirm that the token’s account has access.
- Open Use via API again; endpoint names and parameter order can change when the app code changes.
Requests stay in a queue
Shared hardware can be busy. Reduce input size, avoid duplicate submissions, display a progress state and handle asynchronous job results. If queueing prevents a service-level requirement from being met, move to paid hardware or an Inference Endpoint rather than repeatedly retrying.
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Check whether the account’s daily ZeroGPU quota has been consumed by earlier requests, including tests from another browser or integration. Waiting for the quota reset or moving the workload to paid hardware are the supported remedies; creating repeated anonymous sessions is unreliable and can violate service controls.
The bill is higher than expected
- Inspect whether a paid Space is still running or repeatedly restarting after a crash.
- Pause idle Spaces and use sleep settings where available.
- Record the selected hardware and runtime minutes in deployment monitoring; an upgrade from a CPU or shared setup to an A100 changes the rate substantially.
What is a practical path from experiment to production?
- Validate the task locally. Start with a small public model or dataset, read its card and confirm input/output behavior.
- Publish a minimal Gradio Space. Keep the UI and inference function separate, add an explicit API name and test the generated endpoint.
- Protect credentials and data. Move secrets to environment variables, choose private or protected visibility when appropriate and remove sensitive logs.
- Measure real usage. Record latency, queue time, memory use, failures and average input size instead of relying on a single successful browser test.
- Control spend. Pause idle paid hardware, set operational limits and estimate minutes from observed traffic.
- Choose the production service. Keep the Space for a demo or custom workflow; migrate the model to a dedicated Inference Endpoint when uptime, capacity or latency becomes a contractual requirement.
Frequently Asked Questions
Can I download Hugging Face models without an account?
Public repository files can often be viewed and downloaded anonymously, but an account is needed for private content, accepted-gated terms and higher ZeroGPU access. Anonymous ZeroGPU use is limited to 2 minutes per day under the current documentation.
Why does a Space API call return a job instead of the result?
Gradio can queue work on shared hardware, so the client may first receive an event or job identifier. Poll or await that event according to the generated client example, and show progress rather than issuing uncontrolled retries.
Can I keep a paid Space running all month?
Technically yes, but billing continues for every minute the selected hardware is starting or running. Calculate the hourly rate against expected runtime, configure sleep or pause idle deployments, and monitor restarts before committing to continuous operation.
Do model licenses cover commercial use automatically?
No. Each model and dataset repository states its own license, terms and restrictions in its card or metadata. Check those conditions, plus any provider or dataset privacy requirements, before shipping a commercial product.
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