Deploying a GPU-Optimized Image Enhancement API for a Mobile App

Client

A fast-growing mobile photo-editing startup aiming to provide users with high-quality image enhancement directly from their smartphone.

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Project Overview

The client needed a real-time, high-quality image enhancement engine that could process thousands of images uploaded daily from their mobile app. Their main challenge was slow device-side processing and inconsistent quality when using small mobile-friendly models.

They wanted to integrate GFPGAN and Real-ESRGAN through a cloud-hosted API capable of running on GPU machines with fast response time, scalable architecture, and low operational cost.

We designed and deployed a GPU-optimized Image Enhancement API with automated scaling, caching, and CDN delivery.

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Key Challenges

1. Heavy AI Models

• GFPGAN and Real-ESRGAN require GPU acceleration.

• CPU-only processing was too slow for real-time mobile usage.

2. Large Traffic Spikes

• User traffic fluctuated heavily during weekends and evenings.

• Required auto-scaling and load-balanced endpoints.

3. Multi-Version Model Support

• Some users needed facial enhancement (GFPGAN).

• Others needed general upscaling (Real-ESRGAN).

• Client requested a unified API.

4. Secure & Fast Delivery

• Needed a fast global response time for image uploads/downloads.

• Required CDN integration and safe temporary storage.

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Our Solution

1. GPU-Optimized Server Environment

We built a custom GPU stack using:

• NVIDIA CUDA + cuDNN

• Dockerized model containers

• Nginx reverse proxy for routing

• FastAPI/Python inference backend

The models were optimized with:

• FP16 inference (50% memory reduction)

• ONNX Runtime + TensorRT acceleration

• Preloaded model weights to reduce cold-start time

2. Unified Image Enhancement API

A simple REST API supported:

• /enhance/face → GFPGAN

• /enhance/upscale → Real-ESRGAN

• /enhance/custom → combined pipeline

API supported:

• Base64 or file upload

• Webhooks

• Progress tracking

• Async batch processing

3. Scalable Cloud Infrastructure

We deployed the service using:

• AWS EC2 (g4dn & g5 GPU instances)

• Auto Scaling Groups for GPU workers

• Application Load Balancer

• S3 + Cloudflare CDN for storage & delivery

Traffic routing:

• Small requests → low-cost GPU instances

• Heavy job queues → auto-spin high-end GPUs

4. Image Storage & CDN Delivery

To ensure fast global delivery:

• Temporary enhanced images stored on S3

• Distributed via Cloudflare CDN

• Clean-up policy automatically removes files after 24 hours

5. Monitoring & Security

• Prometheus + Grafana for GPU metrics

• CloudWatch for API-level monitoring

• IP rate-limiting

• Presigned URL for secure upload/download

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Architecture Diagram (Text Description)

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Results / Impact

🚀 4x Faster Processing

Average image enhancement time dropped from 12+ seconds (CPU) to 2.7 seconds (GPU-accelerated).

📈 40% Reduction in Cloud Costs

Auto-scaling and mixed GPU instance strategy cut operational costs significantly.

🌍 Global <200ms Latency

With Cloudflare CDN + S3 caching optimizations.

⭐ 2.3× Increase in User Engagement

App usage surged after introducing high-quality enhancement.

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Tools & Technologies Used

• GFPGAN, Real-ESRGAN, ONNX Runtime, TensorRT

• Python, FastAPI, Docker

• AWS EC2 (g4dn/g5), S3, Route 53

• Cloudflare CDN

• Nginx Reverse Proxy

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Conclusion

By deploying a GPU-optimized image enhancement infrastructure, we enabled the client to deliver studio-quality photo enhancement directly within their mobile app—at high speed, low latency, and optimal cost.

This setup now processes tens of thousands of images per day, scaling automatically as their user base grows.

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