Hi all, my friend and I have developed a GPU inference system (no external API dependencies) for our generative AI social media app drippi (please see our company Instagram page @drippi.io https://www.instagram.com/drippi.io/ where we showcase some of the results). We've recently decided to sell our company and all of its assets, which includes the entire codebase, along with our proprietary GPU inference system and all the deep learning models used within (no external API dependencies), our tech and IP, our app, our domain name, and our social media accounts @drippiresearch (83k+ followers), @drippi.io, etc.

- Link to the app on the App Store: https://apps.apple.com/us/app/drippi/id6450683517
- Link to the @drippiresearch Instagram page: https://www.instagram.com/drippiresearch/
- Link to the @drippi.io Instagram page: https://www.instagram.com/drippi.io/

About drippi and its tech
Drippi is a generative AI social media app that lets you take a photo of your friend and put them in any outfit + share with the world. Take one pic of a friend or yourself, and you can put them in all sorts of outfits, simply by typing down the outfit's description. The app's user receives 4 images (2K-resolution) in less than 10 seconds, with unlimited regenerations.

Our core tech is a scalable + high performance Kubernetes-based GPU inference system and server cluster with our self-hosted models (no external API calls, see the "Backend Inference Server" section in our tech stack description for more details). The entire system can also be easily repurposed to perform any generative AI/model inference/data processing tasks because the entire architecture is super customizable.

Please email team@drippi.io for more details.

Metrics
- Close to 1,000 users
- 2,700+ posts (each post has up to 4 generated images) in total
- 10,000+ images generated each week

About us
Ex-NVIDIA Engineer + Ex-Stripe Business Analyst

Tech Stack
Backend Inference Server:
- Tech Stack: Kubernetes, Docker, NVIDIA Triton Inference Server, Flask, Gunicorn, ONNX, various deep learning libraries (PyTorch, HuggingFace Diffusers, transformers, etc.), MongoDB
- A scalable + high performance Kubernetes-based GPU inference system and server cluster with self-hosted models (no external API calls). Highlights:
- A custom deep learning model GPU inference system built with the industry standard NVIDIA Triton Inference Server. Supports features like dynamic batching, etc.
- The inference engine supports various model formats, such as Python models (e.g. HuggingFace Diffusers/transformers), ONNX, TensorFlow, TensorRT, TorchScript, OpenVINO, DALI, etc. All models are self-hosted and can be easily swapped and customized.
- A client-facing multi-processed and multi-threaded Gunicorn server that handles concurrent incoming requests and communicates with the GPU inference engine.
- A customized pipeline (Python) for orchestrating model inference.
- Supports user authentication.
- Supports real-time inference metrics logging in MongoDB database.
- Supports GPU utilization + health metrics monitoring.
- All programs and their dependencies are encapsulated in Docker containers.
- Models:
- Clothing + body part segmentation model
- Background masking model
- Diffusion inpainting model
- Prompt enhancement LLM model
- Super resolution model
- NSFW detection model
- Notes:
- All models mentioned above are self-hosted and require no external API calls.
- All models mentioned above fit together in a single GPU with 24 GB of memory.

Backend Database Server:
- Tech Stack: Express, Node.js, MongoDB
- Highlights:
- Feed recommendation algorithm
- Common social network features

App Frontend:
- Tech Stack: React Native, Firebase Authentication + Notification
- Highlights:
- Picture taking/cropping/selecting
- Common social network features