Mar 16, 2020

Minimal Code Delivers Hyper-Converged AI Infrastructure, Slashing Costs and Latency for Image Generation at Scale

Minimal Code, a leading innovator in cloud-native and AI infrastructure solutions, has successfully designed and deployed a state-of-the-art hyper-converged private cloud for a client operating a mobile app powered by image generation models like Stable Diffusion.

Ultra-High-Performance Infrastructure

At the heart of the deployment are 5x Gigabyte G492-Z52 servers, each equipped with:

512GB of RAM
AMD EPYC processors
8× NVIDIA ADA A6000 GPUs per server, totaling 40 enterprise-grade GPUs

To support scalable storage and high-throughput data processing, the architecture is backed by a Ceph-based distributed storage system, ensuring fault-tolerant, high-availability data access across the GPU cluster.

AI-Optimized Task Queueing and Control

Minimal Code not only delivered the physical and virtual infrastructure, but also engineered a low-latency AI task queuing system tailored for generative AI workloads. This software layer enables seamless orchestration of image generation requests from the mobile frontend, dramatically reducing queue overhead and maximizing GPU utilization.

The entire pipeline—from image generation requests to model execution—is now handled in a tightly integrated, fully private, and customizable environment, giving the client full operational control and data sovereignty.

Tangible Business Impact

With this new private AI cloud:

Infrastructure and cloud compute costs have significantly decreased
Image generation times were reduced drastically, improving user experience
Full control over model performance, updates, and compliance is now possible without relying on external cloud providers

Turnkey AI Stack, From Design to Deployment

Minimal Code’s team led the full lifecycle—from architectural design to infrastructure deployment and backend queue development. The system is purpose-built to support modern AI workloads, including fine-tuned diffusion models and multimodal pipelines, while offering robust observability and failover mechanisms.