BitNet

"The most significant and immediate benefit of forcing weights into {−1,0,1} is the elimination of floating-point multiplication, which is the most expensive operation in modern deep learning hardware.

"We are currently running BitNet b1.58 on chips that are not optimized to run INT8 additions, on top of which the entire architecture stands.

"This implies that there still exist some efficiency gains left unexplored. 

"If BitNet can achieve an 8-9x speedup on hardware that is suboptimal, then the potential gains on hardware that is specifically designed for integer addition —such as Groq’s LPUs —could be even more substantial.

"This architecture also offers us a realistic pathway towards deploying large 70B+ parameter models, directly on local edge devices like mobile phones and laptops, without compromising intelligence."

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