Traditional Quantization vs 1.58-Bit Ternary Models: A Practical Comparison

If you've been running local LLMs, you already know the drill: download a 70B model, quantize it to 4-bit with GPTQ or GGUF, cross your fingers, and hope your GPU doesn't catch fire. It works. It's practical. But there's a fundamentally different approach gaining serious traction — ternary quantization at 1.58 bits per weight. The concept behind projects like Ternary Bonsai and Microsoft's BitNet b1.58 research is almost absurdly simple: what if every weight in your model could only be -1, 0, or