Model Quantization (FP16, INT8 & INT4)
Post-Training Quantization (PTQ) vs Quantization-Aware Training (QAT) for Edge Serving
#Inference#Quantization#INT8#INT4#PTQ#QAT#Edge Serving
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Hosting full FP32 or FP16 unquantized neural models on expensive 80GB A100 GPUs for simple text classification or embedding tasks.
Why It Fails in Production:
Costs thousands of dollars per month in GPU cloud hosting when INT8 or INT4 quantization fits the model onto a standard $20/month CPU instance with zero noticeable drop in accuracy.
Targeted Algorithm (Model Quantization (FP16, INT8 & INT4))
Latency:3ms (INT8 CPU via ONNX)
Cost / 1M Ops:$0.00
Determinism:Identical Output Accuracy
Generative LLM Alternative
Latency:Full FP16 GPU required
Cost / 1M Ops:$4,000/mo Cloud VM
Determinism:Massive Waste of VRAM