290
No, you don't need a 'very bespoke AOSP' to turn your phone into a Rabbit R1 — here's proof
(www.androidauthority.com)
this post was submitted on 04 May 2024
290 points (95.9% liked)
Technology
59161 readers
2119 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Last I checked (around the time that LLAMA v3 was released), the performance of local models on CPU also was pretty bad for most consumer hardware (Apple Silicon excepted) compared to GPU performance, and the consumer GPU RAM situation is even worse. At least, when talking about the models that have performance anywhere near that of ChatGPT, which was mostly 70B models with a few exceptional 30B models.
My home server has a 3090, so I can use a self-hosted 4-bit (or 5-bit with reduced context) quantized 30B model. If I added another 3090 I’d be able to use a 4-bit quantized 70B model.
There’s some research that suggests that 1.58 bit (ternary) quantization has a lot of potential, and I think it’ll be critical to getting performant models on phones and laptops. At 1.58 bit per parameter, a 30B model could fit into 6 gigs of RAM, and the quality hit is allegedly negligible.