Ternary LLMs stall: BitNet's promise fades as largest model hits 2B parameters
Why did ternary AI models plateau while binary and full-precision models surge ahead?
Deep Dive
Ternary models showed early promise, but the largest is still just 2B parameters. The question remains: why haven't frontier open-weights AI labs adopted them?
Key Points
- Largest ternary model is only 2B parameters (BitNet b1.58 2B)
- No frontier open-weight labs have adopted ternary architectures for production models
- Training instability and lack of hardware acceleration cited as key barriers vs. 4-bit quantization
Why It Matters
Ternary LLMs could have revolutionized on-device AI, but their failure to scale means efficiency gains will come from quantization instead.