Show HN: Avoiding the Memory Wall by computing LLM inference directly inside RAM
The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices. Moving AI on-device is a brilliant and necessary strategy. Achieving this on a phone requires extreme quantization, such as PrismML’s ternary weights.However, a critical hardware reality often overlooked by the software world is that fitting the weights in RAM is not equivalent to moving them.
- ▪The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices.
- ▪Moving AI on-device is a brilliant and necessary strategy.
- ▪Achieving this on a phone requires extreme quantization, such as PrismML’s ternary weights.However, a critical hardware reality often overlooked by the software world is that fitting the weights in RAM is not equivalent to moving them.
Opening excerpt (first ~120 words) tap to expand
The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices. Moving AI on-device is a brilliant and necessary strategy. It ensures absolute user privacy in alignment with EU regulations, fundamentally shifts the economics away from costly cloud inference, and paves the way for a significant hardware upgrade supercycle as users seek true AI-capable silicon.To create a smart on-device "Semantic Router," models need to reach the 27B+ parameter scale.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.