Beyond 4-Bit Quantization: Why Ternary Models Are Reshaping LLM Efficiency

Traditional 4-bit quantization (GPTQ, GGUF) has proven practical but still relies on expensive multiply-accumulate operations during inference. Microsoft's BitNet b1.58 introduces a fundamentally different approach—restricting all weights to {-1, 0, +1}—eliminating floating-point math entirely and replacing it with pure addition and subtraction. This extreme compression could enable larger models to run on consumer hardware with substantially faster inference, potentially making the industry's current quantization standards obsolete.