⚡PrismML Puts a 1-Bit 2B Vision Model on Snapdragon Glasses
TL;DR
PrismML showed its 1-bit Bonsai 2B vision-language model running locally on smart glasses built on Qualcomm's Snapdragon AR1 Gen 1. It needs 0.43GB of weights and runs about twice as fast as a 4-bit 1.7B model.
PrismML showed its 1-bit Bonsai 2B vision-language model running locally on smart glasses built on Qualcomm's Snapdragon AR1 Gen 1. It needs 0.43GB of weights and runs about twice as fast as a 4-bit 1.7B model.
Key Points
Demoed at Qualcomm's Snapdragon Summit on the AR1 Gen 1 platform
Bonsai 2B VLM weights total 0.43GB versus 1.66GB for a comparable 4-bit 1.7B model, a 3.83x cut
Throughput hits 15.36 tokens/sec versus 7.44, roughly 2.06x faster
PrismML was founded by Caltech researchers and is advised by UC Berkeley's Ion Stoica
No shipping glasses with the model have been announced yet
Why It Matters
If 1-bit quantization holds its accuracy, 'what am I looking at' queries can run on-device with no cloud round trip. That changes battery, latency and privacy math for wearables.
Quick Facts
Comments
Be the first to comment
Enjoyed this article?
Get it daily. 7am. Free. Reads in 5 minutes.
Join 3,508 builders reading daily.