GPU-Optimized AI Workloads
Deployed real-time AI inference pipeline on GPU clusters, achieving 85% lower latency and 40% higher throughput compared to CPU-based systems for enterprise clients.
85%
Latency Reduction
40K+
Inferences/Second
90%
Model Accuracy
Overview
Built AI inference platform that leverages GPU parallelism for large-scale machine learning models, delivering real-time predictions for retail and fintech clients.
Challenges
- Real-time inference with <10ms latency requirements
- Scaling ML models across distributed GPU clusters
- Maintaining <99.99% availability SLAs
- Energy efficiency on enterprise GPU farms
Solutions
- TensorRT-optimized kernel for mixed-precision training
- Distributed GPU load balancing with Kubernetes
- Quantized models with lossless compression
- Power-aware compute scheduling for 20% energy efficiency gain
Results
- 12x speedup over traditional CPU deployment pipelines
- $3.2M annual savings in compute costs
- 97% customer satisfaction rate for prediction accuracy
- 100% compliant with ISO 27001 and AI ethics frameworks