Editing Model Compression and Quantization (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a model compression pipeline: (1) Profile the base model: measure latency, memory, and quality on target hardware. (2) Set compression targets: e.g., "must fit in 8GB VRAM, latency <200ms". (3) Apply quantization first: GPTQ or AWQ for LLMs; TensorRT INT8 for vision models. (4) If quality gap remains, apply structured pruning to remove redundant attention heads. (5) Consider distillation if larger compression needed: train a student model 3-10× smaller. (6) Final validation: measure compressed model on all target benchmarks; ensure quality degradation within acceptable bounds. (7) Deploy: GGUF for CPU, ONNX for cross-platform, TensorRT for NVIDIA production.

[[Category:Artificial Intelligence]]
[[Category:Deep Learning]]
[[Category:AI Infrastructure]]
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