Hugging Face ZeroGPU

Hugging Face ZeroGPU

Rules and patterns for ML demos on Hugging Face Spaces with ZeroGPU hardware. Covers @spaces.GPU, duration and quota tuning, process isolation, the CUDA availability model, concurrency safety, and CUDA build constraints.

Scope

This skill is for Gradio SDK Spaces using ZeroGPU hardware. Docker and Static Spaces cannot schedule onto ZeroGPU, and Streamlit apps now run as Docker Spaces — so this skill applies only to Gradio. For general Gradio coding (components, layouts, event listeners), see the huggingface-gradio skill in this repo. The authoritative ZeroGPU docs live at https://huggingface.co/docs/hub/spaces-zerogpu — refer to them for the current backing GPU, runtime version lists, and tier thresholds, all of which change over time.

Reference Files

Hardware

ZeroGPU exposes two GPU sizes that map to a fraction of the backing card:

Default large gives half a physical GPU, so memory bandwidth and compute are significantly lower than the full card's specs. Use xlarge only when the workload genuinely needs the extra memory or compute.

Backing GPU changes without notice. ZeroGPU has already migrated across GPU generations several times; older write-ups may name A100 or H200, but those are outdated. For the current backing GPU and exact per-size VRAM, always check the ZeroGPU docs before sizing workloads.

Basic Pattern

import spaces
import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="...", device="cuda")

@spaces.GPU
def generate(prompt: str) -> str:
    return pipe(prompt, max_new_tokens=100)[0]["generated_text"]

Key rules:

1. Instantiate models at module scope and call .to("cuda") eagerly. ZeroGPU handles the actual device mapping transparently (see CUDA availability model below).
2. Decorate GPU functions with @spaces.GPU. The decorator is a no-op outside ZeroGPU, so it is safe to keep in all environments.
3. Set duration to match the realistic worst-case workload (default 60s). The platform pre-checks requested duration against the user's remaining quota — not against the actual run time — so a 10-second task left at the 60s default fails with quota exceeded as soon as the user's remaining quota drops below 60s. Smaller declared duration also ranks higher in the node-level queue. See "Duration and Quota" below.
4. torch.compile is NOT supported. Use PyTorch ahead-of-time compilation (AoTI) (torch 2.8+) instead.
5. Use size="xlarge" sparingly. It allocates the full backing GPU, but costs 2x quota and tends to queue longer.

@spaces.GPU(duration=120)
def generate_image(prompt: str):
    return pipe(prompt).images[0]

CUDA Availability Model

Real GPU access is only available inside @spaces.GPU-decorated functions. Outside those functions, the GPU is not attached to the process.

However, import spaces monkey-patches torch so that:

• torch.cuda.is_available() returns True globally.
• .to("cuda") / device="cuda" calls at module scope succeed without error.

This is intentional. Module-scope model.to("cuda") calls register tensors with the ZeroGPU backend, which writes them to a disk offload directory at a startup "pack" step and frees the corresponding RAM. When a @spaces.GPU call lands, a forked GPU worker process streams those weights from disk into VRAM via a pinned-memory pipeline. Warm workers (reused across requests on the same GPU slot) keep weights resident on the GPU and skip the disk → VRAM step. The user-facing rule: write device="cuda" at module scope and it works — see references/how-zerogpu-works.md for the full lifecycle.

Do not run inference or CUDA kernels at module scope — the real GPU is not attached, so operations either silently run on CPU or fail.

Device selection idiom still works

The standard idiom remains correct under ZeroGPU:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModel.from_pretrained("...").to(device)

• ZeroGPU — is_available() is True (monkey-patched), so the model is registered for automatic device migration.
• Dedicated GPU Spaces / local GPU — is_available() is genuinely True.
• CPU Spaces / local CPU — resolves to "cpu".

Do not hardcode device="cuda" — it breaks on CPU-only environments.

Eager loading is the right default

Load models at module scope, not lazily on first request. The Space process starts before any user arrives, so cold-start cost is paid once. Lazy loading (global model; if model is None: ..., @lru_cache wrappers, factory functions instantiating on first call) just pushes that cost onto the first user.

Local Development: Just Install spaces

Do not wrap import spaces in try/except and redefine spaces.GPU as a no-op fallback for local runs. Off-ZeroGPU, the spaces package is already a true no-op:

• Heavyweight behavior (CUDA monkey-patching, client init, startup hooks) is gated on the SPACES_ZERO_GPU env var, set only on ZeroGPU.
• @spaces.GPU returns the undecorated function unchanged off-ZeroGPU.
• Top-level import spaces performs only lightweight imports.

The Gradio SDK base image installs spaces on every hardware tier. So even after duplicating a Space onto a dedicated GPU (T4, L4, A10G, etc.) or CPU basic, no code changes are needed — import spaces still succeeds and @spaces.GPU becomes a transparent passthrough.

Anti-pattern

try:
    import spaces
except ImportError:
    class spaces:  # type: ignore
        @staticmethod
        def GPU(func=None, **kwargs):
            return func if func else (lambda f: f)

Problems:

1. The fallback must mimic every @spaces.GPU call shape — bare decorator, duration=..., size=..., generators, aoti_* helpers — and drifts as the spaces API grows.
2. It hides spaces from requirements.txt, even though the Space needs it at deploy time.
3. It solves a non-problem: the real package is already a no-op locally.

Do this instead

Add spaces to dependencies and import it unconditionally:

import spaces

@spaces.GPU
def generate(prompt: str) -> str:
    ...

Duration and Quota

Three things happen when you declare @spaces.GPU(duration=N):

1. Tier-max check — each visitor tier has a per-call duration cap. Declaring duration larger than the cap fails immediately with ZeroGPU illegal duration, regardless of remaining quota. (Tier numbers change over time — see the ZeroGPU docs.)
2. Quota pre-check — the platform compares requested duration against the user's remaining quota. If remaining < requested, the call fails with ZeroGPU quota exceeded — even if the actual work would have fit. The error message shows the explicit numbers, e.g. "60s requested vs. 30s left". A 10-second task left at the default 60s therefore blocks the user once their remaining quota drops below 60s.
3. Queue priority — the queue is node-level (requests from all Spaces on the same node compete for GPU slots), and shorter declared duration ranks higher.

All three favor declaring the smallest realistic duration — including for short tasks. Explicit @spaces.GPU(duration=15) on a 10-second task avoids premature quota exceeded rejections and ranks higher in the queue.

xlarge doubles the request. requested = N * 2 when size="xlarge", both for the tier-max check and the quota pre-check. So @spaces.GPU(duration=60, size="xlarge") is internally a 120s request.

Dynamic duration for variable workloads

For workloads whose runtime depends on inputs, pass a callable that estimates per request. A static high duration locks out low-tier users (whose tier cap may be smaller than the static value) and unnecessarily reserves quota for light inputs.

def estimate_duration(prompt, steps):
    return int(steps * 3.5)

@spaces.GPU(duration=estimate_duration)
def generate(prompt, steps):
    return pipe(prompt, num_inference_steps=steps).images[0]

For the full distinction between illegal duration vs quota exceeded, runs-per-day limits, the 24h quota window, and pay-as-you-go billing, see references/how-quota-works.md.

Process Isolation and Pickle

@spaces.GPU-decorated functions run in a separate process managed by the ZeroGPU scheduler. Arguments and return values cross the process boundary via pickle serialization.

Consequences:

• Only picklable objects can be passed in or returned. Open file handles, database connections, locks, lambdas, and closures over unpicklable state will raise PicklingError.
• Do NOT return CUDA tensors directly. Unpickling a CUDA tensor in the main process triggers torch.cuda._lazy_init(), which ZeroGPU blocks. Convert to CPU first: return tensor.cpu() or tensor.cpu().numpy().
• CPU tensors, numpy arrays, PIL Images, and plain Python objects work fine.
• Large objects incur serialization overhead. Prefer lightweight returns (tensors, arrays, file paths, base64 strings) over complex object graphs.