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I would be curious how much less computationally expensive these models are. Full-blown LLMs are overkill for most of the things I do with them. Does running th
by juujian 3y ago
I would be curious how much less computationally expensive these models are. Full-blown LLMs are overkill for most of the things I do with them. Does running them affect battery life of mobile devices in a major way? This could actually end up saving a ton of electricity. (Or maybe induce even more demand...)
- philjohn 3y agoIt probably helps that Apple Silicon has dedicated die space to the Neural Engine - essentially a TPU. No good for training, great for inference.
- davedx 3y agoI’ve been reading up on this recently but devs say ANE is kinda a pain in the ass to leverage; most OSS is using gpu instead
- anentropic 3y agothese most likely aren't using the Neural Engine the ANE seemed to be optimised for small vision models like you might run on an iPhone a couple of years ago these will be running on the GPU
- smpanaro 2y agoI bet these can all run on ANE. I’ve run gpt2-xl 1.5B on ANE [1] and WhisperKit [2] also runs larger models on it. The smaller ones (1.1B and below) will be usably fast and with quantization I suspect the 3B one will be as well. GPU will still be faster but power for speed is the trade-off currently. [1] 7 tokens/sec https://x.com/flat/status/1719696073751400637 https://x.com/flat/status/1719696073751400637 [2] https://www.takeargmax.com/blog/whisperkit https://www.takeargmax.com/blog/whisperkit
- anentropic 2y agoindeed, but probably not as written currently? i.e they would need converting with e.g. your work in more-ane-transformers
- JKCalhoun 2y agoI wonder if there could be a tiered system where a "dumber" LLM fields your requests but passes them on to a smarter LLM only if it finds it confidence level below some threshold.