Learn how quantization makes large models faster and easier to run without requiring high-end hardware. You’ll apply INT8 and INT4 methods, compare post-training vs. quantization-aware training, and measure how accuracy is affected. You’ll also use calibration techniques to minimize trade-offs, giving you the skills to balance efficiency with performance in real-world scenarios.

Discover how to streamline inference so models respond faster and run more efficiently in production. You’ll practice advanced batching, KV-cache management, and token scheduling to cut latency while improving throughput. You’ll also explore memory-saving techniques beyond quantization, ensuring your models remain reliable and cost-effective under real-world system loads.

Learn how to make the most of available hardware by tuning GPU performance. You’ll use tools like nvidia-smi and PyTorch profiler to spot bottlenecks, and apply strategies such as mixed precision, gradient checkpointing, and memory mapping. These practices help you adapt models to limited resources while maintaining stability and quality in training or inference.

Prepare models for deployment across platforms and measure how well they perform once optimized. You’ll convert models into formats like ONNX for cross-platform use and benchmark them to evaluate speed, memory, and throughput. By practicing these workflows, you’ll gain the ability to deliver models that are portable, production-ready, and backed by clear performance data.