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AI theory, architecture, models
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Module 22
Efficient AI: Deployment and Optimization
1
Model Quantization for Size & VRAM Reduction
Apply model quantization techniques (INT8, 4-bit) to reduce model size and VRAM usage
2
Knowledge Distillation for Model Compression
Implement knowledge distillation to transfer knowledge from a large teacher model to a smaller student model
3
GGUF Conversion & llama.cpp Deployment for CPU Inference
Convert and deploy models in GGUF format for CPU-based inference using llama.cpp
4
Setting Up Local Inference Servers
Set up local inference servers using tools like Ollama and text-generation-webui
5
Optimizing Large Model Training on Limited Hardware
Apply mixed-precision training and gradient accumulation for training large models on limited hardware
6
Distributed Training Strategies
Implement distributed training strategies like Data Parallelism and Pipeline Parallelism
Previous module
Specialized Generative Applications
Next module
Emerging Architectures and Research Frontiers