Multi-GPU LLM Deployment Pipeline
Optimized inference pipeline for large language models across multiple NVIDIA GPUs.
Overview
Engineered a high-performance deployment pipeline for running large language models across multi-GPU setups, with a focus on VRAM optimization and inference speed. The system supports dynamic model loading, batch processing, and efficient memory management.
Implemented sophisticated scheduling algorithms to maximize GPU utilization while minimizing latency. The pipeline includes comprehensive monitoring, automatic failover mechanisms, and A/B testing capabilities for model comparison.
The solution reduced infrastructure costs by 40% while maintaining sub-second response times for most queries.
Key Outcomes & Metrics
- Reduced inference latency by 45% through optimized batching
- Achieved 92% GPU utilization across 4-GPU setup
- Cut infrastructure costs by 40% vs. single-GPU baseline
Technical Challenges
This project presented several interesting technical challenges including system architecture design, performance optimization, and integration with existing infrastructure. The solutions implemented demonstrate expertise in machine learning and production-grade software development.