A research paper from UC Santa Cruz and accompanying writeup discussing how AI researchers found a way to run modern, billion-parameter-scale LLMs on just 13 watts of power. That’s about the same as a 100W-equivalent LED bulb, but more importantly, its about 50 times more efficient than the 700W of power that’s needed by data center GPUs like the Nvidia H100 and H200, never mind the upcoming Blackwell B200 that can use up to 1200W per GPU.
The work was done using custom FGPA hardware, but the researchers clarify that (most) of their efficiency gains can be applied through open-source software and tweaking of existing setups. Most of the gains come from the removal of matrix multiplication (MatMul) from the LLM training and inference processes.
How was MatMul removed from a neural network while maintaining the same performance and accuracy? The researchers combined two methods. First, they converted the numeric system to a “ternary” system using -1, 0, and 1. This makes computation possible with summing rather than multiplying numbers. They then introduced time-based computation to the equation, giving the network an effective “memory” to allow it to perform even faster with fewer operations being run.
The mainstream model that the researchers used as a reference point is Meta’s LLaMa LLM. The endeavor was inspired by a Microsoft paper on using ternary numbers in neural networks, though Microsoft did not go as far as removing matrix multiplication or open-sourcing their model like the UC Santa Cruz researchers did.
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Robin Edgar
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