How executing floating point arrays locally forces mathematically rounded sacrifices.
A completely unquantized 70 Billion parameter model cannot fit into standard consumer VRAM due to sheer mathematical girth. GGUF formats compress these tensor matrices dynamically.
"Quantization forces neural network arrays into 4-bit representation, discarding immense precision to achieve portability."
— Open Weights Coalition
For developers operating Duplex with Ollama locally, you are interfacing with Q4 or Q8 quantized weights. The UI remains identically sleek, but the linguistic reasoning capability drops proportional to the compression quotient.