Exploring 2-bit QAT: Can Ultra-Compressed Large Models Outperform 4-bit Models Half Their Size?

Original: 2-bit QAT model releases

Reddit users discuss the potential of 2-bit Quantization Aware Training (QAT) for 120B+ models as an alternative to ternary LLMs on consumer hardware.

A popular Reddit thread on r/LocalLLaMA discusses the potential of 2-bit Quantization Aware Training (QAT) for large MoE models (120B to 400B). While current QAT efforts focus on 4-bit, users speculate whether a 2-bit QAT model could fit into consumer hardware (64GB/128GB RAM) and outperform a 4-bit model of half its size. This approach is proposed as a practical alternative to training ternary (1.58-bit) LLMs from scratch.

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