One-Click GPU Templates: Deploy PyTorch, Hugging Face, TensorFlow & ONNX Models Instantly

Setting up a GPU-accelerated ML environment from scratch means installing CUDA drivers, configuring Docker GPU runtime, picking the right base image, and wiring up a web server. That's hours of DevOps work before you even get to your actual model. SnapDeploy's one-click GPU templates skip all of that — choose a template, click Deploy, and have a running GPU inference endpoint in 2-3 minutes.

Each template is a fully working application: a Dockerfile with CUDA pre-configured, a FastAPI server for HTTP inference, and all necessary dependencies pre-installed. They're designed as starting points — fork the repo, swap in your model, and you have a production GPU inference API.

4 GPU Templates Available

1. PyTorch Inference

Stack: PyTorch + CUDA 12.1 + FastAPI

A ready-to-use PyTorch environment for deploying models via a REST API. Pre-installed with torch, torchvision, and uvicorn. Load your .pt or .pth model, define your API endpoint, and deploy.

Best for: Custom PyTorch model serving, image classification (ResNet, EfficientNet), NLP inference (BERT, GPT-2), object detection (YOLO), any PyTorch workload.

Example use case: You've trained a custom image classifier in PyTorch. Export it as a .pt file, add it to the template, define a /classify endpoint, and you have a GPU-accelerated classification API.

Source: github.com/AAR-Labs/pytorch-gpu-snapdeploy

2. Hugging Face Transformers

Stack: Transformers + Accelerate + PyTorch

Run any model from the Hugging Face Hub. Pre-loaded with transformers, accelerate, and torch. Change the model name in the code, deploy, and you have a GPU-accelerated Hugging Face inference API. The accelerate library handles GPU memory management automatically.

Best for: Text generation, sentiment analysis, summarization, translation, question answering, named entity recognition — any Hugging Face pipeline task.

Example use case: Deploy a sentiment analysis API using distilbert-base-uncased-finetuned-sst-2-english. Change one line in the template code to set the model name, deploy, and you have GPU-accelerated sentiment analysis.

Source: github.com/AAR-Labs/huggingface-gpu-snapdeploy

3. TensorFlow

Stack: TensorFlow 2.x + CUDA

TensorFlow with full GPU support. Great for Keras models and TF Serving workloads. Includes tensorflow with CUDA acceleration pre-configured — tf.config.list_physical_devices('GPU') returns the available GPU immediately.

Best for: Keras model serving (.h5 or SavedModel format), TensorFlow Lite conversion + serving, image recognition, time series prediction, any TensorFlow workload.

Example use case: You have a Keras image recognition model trained with model.save('my_model.h5'). Load it in the template with tf.keras.models.load_model('my_model.h5') and serve predictions via FastAPI.

Source: github.com/AAR-Labs/tensorflow-gpu-snapdeploy

4. ONNX Runtime

Stack: ONNX Runtime + TensorRT

High-performance inference for ONNX models with GPU acceleration via NVIDIA TensorRT. Convert your PyTorch or TensorFlow model to ONNX format and deploy for optimized inference speed. ONNX Runtime with TensorRT can be 2-5x faster than running the same model in native PyTorch or TensorFlow.

Best for: Production inference where latency matters, cross-framework model deployment (train in PyTorch, deploy as ONNX), optimized serving pipelines, batch inference.

Example use case: You've trained a model in PyTorch and exported it to ONNX format with torch.onnx.export(). Deploy it on ONNX Runtime for faster inference than the original PyTorch model, with automatic TensorRT optimization.

Source: github.com/AAR-Labs/onnx-gpu-snapdeploy

Step-by-Step: Deploy a GPU Template

1. Log in to your SnapDeploy dashboard at snapdeploy.dev/login
2. Click "New Container" and select GPU as the compute type
3. Choose a GPU tier — T4 ($0.50/hr, 16 GB VRAM) or A10G ($1.00/hr, 24 GB VRAM)
4. Select an AI template from the template gallery — PyTorch, Hugging Face, TensorFlow, or ONNX
5. Click Deploy — SnapDeploy clones the template repository, builds the Docker image with CUDA, and starts it on a dedicated GPU instance
6. Wait 2-3 minutes for the build to complete. The template includes pre-built CUDA layers that speed up the build.
7. Access your API at the public URL shown (e.g., your-app.snapdeploy.app)

That's it. You now have a GPU inference endpoint with free SSL, accessible from anywhere.

Customizing a Template

Templates are starting points, not locked configurations. Every template is an open-source GitHub repository that you can fork and modify. The typical customization workflow:

1. Fork the template repository on GitHub
2. Add your model — either include model files directly, or modify the code to download from Hugging Face Hub, S3, or another URL at startup
3. Modify the API — change the FastAPI endpoints in app.py to match your input/output format
4. Add dependencies — update requirements.txt with any additional Python packages
5. Push to GitHub
6. Deploy your fork as a new GPU container on SnapDeploy — connect your forked repository instead of using the template directly

All templates use port 8000 by default. If you change the port in your FastAPI app, update the container port setting in SnapDeploy to match.

Which Template Should I Use?

If you're not sure, start with the Hugging Face template on T4. It's the most flexible — the Hugging Face Hub has 400,000+ models covering every major AI task, and the transformers library handles model loading, tokenization, and inference automatically.

GPU Auto-Sleep and Credits

Template containers follow the same serverless GPU rules as all GPU containers on SnapDeploy:

• Auto-sleep after 15 minutes of no incoming HTTP traffic
• Zero credit consumption while sleeping — the GPU is released
• Auto-wake in 2-3 minutes on the next request
• Credits are consumed only while the container is actively running

You need GPU credits to deploy templates. The Starter pack ($5, 10 T4-hours) is enough for several hours of testing and development. Credits expire 90 days after your most recent purchase.

Templates vs. Custom Repository

When should you use a template vs. deploying your own repository directly?

For deploying your own code on GPU, see our GPU deployment guide which covers framework auto-detection, CUDA base image selection, and system dependency handling.

Getting Started

1. Create a free SnapDeploy account
2. Purchase a GPU credit pack — the Starter ($5) gives you 10 T4-hours
3. Create a new GPU container and select a template
4. Click Deploy and wait 2-3 minutes

Your GPU inference API will be live at a public URL with free SSL. Fork the template, add your model, and redeploy to make it your own.