AI article
Quantization, Explained: From FP32 to INT4, and What Each Precision Costs You
Community description: In LoRA & DoRA and our other posts on large models, we keep running into the same wall: memory....
Dev.to | Nov 5, 2026 | Aleksei Romanov
Automated excerpt
PTQ / QAT — Post-Training Quantization: quantize a finished model. FP16 prioritizes precision (10 mantissa bits) at the expense of exponent range (5 bits). FP32 and TF32 share range; TF32 and FP16 share mantissa precision; BF16 borrows FP32's range inside FP16's footprint.
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