Benchmarking Pocket-Scale Inference(artificialanalysis.ai) |
Benchmarking Pocket-Scale Inference(artificialanalysis.ai) |
I hope there's some kind of inversion in the current chip economics, because I love distributed/democratized/private compute, but currently cloud based LLM inference seems to be much more viable. I don't see local llms meaningfully viable for the general usecase in the near future.
The dram shortage is a combination of high demand + shifting manufacturing to HBM, which consumes more wafer capacity than DDR5
Whether it's actually better for the kind of things people actually use it for... the benchmarks don't really tell you that. (In my experience, no.)
I often have funny experiences where models do great on benchmarks and are awful, or do poorly and are great for my use cases.
And different people use them in different ways, which probably explains why some people think one models is great and others think it sucks.
In my experience even small local models are now surprisingly good at programming and using a computer (bash), i.e. completing agentic tasks, but fall apart quickly in conversation (especially knowledge and understanding).
I am hoping to get to the point of being able to run a personal assistant agent with tools at its disposal.
The results are largely the same, with some outliers.
Results can be seen here: https://0xff.nu/hexbench/
Why do phones and laptops get equipped with NPUs if there's no use for them?
OpenCL and Vulkan are supposed to gain support eventually but given their extremely constrained instruction sets and the wide variance in capabilities between different hardware models I don't imagine they're the sort of thing you'd want to target manually.
I did see one prototype someone built using private ios APIs a few months back. They were able to run a reasonable SLM about an order of magnitude slower than GPU but with a tiny power draw.
The Android ecosystem is much more liberal when it comes to RAM because their Dalvik VM JIT (their Java Android Runtime, partially AOT compiled and partially JITed) design is not particularly memory efficient. But the main issue with Android is that their mid/low end (think the Samsung Galaxy A series, the OnePlus Nords, the Motorola Gs etc.) are very inefficient when it comes to single core compute performance compared to iPhones, and it gets worse once you factor in power efficiency. The high end Android flagships running the Snapdragons elites (especially post Oryon acquisition) have no problems matching if not exceeding Apple hardware performance in terms of raw power but they are much more power hungry.
At the end of the day, the current gen of "pocket scale" LLMs are still far from being able to be deployed at scale on mobile. Maybe in another year or two once RAM prices have fallen enough and mobile manufacturers build a lot more matmul and memory circuits into their SoCs instead of a tiny mostly useless "NPU/tensor processor" that doesn't have enough RAM to run anything useful. Current gen production mobile ML still heavily rely on traditional transfer learning like Apple's Core.ML (tiny pre trained models, with perhaps the final layer inference head swapped out).
If you want a good example of production mobile ML, take a look at FUTO keyboard's transformer model for their keystroke prediction/autocorrect which operates in near real-time.
https://github.com/futo-org/android-keyboard/blob/7ebbf5c8b5...
I would say right now 100M-300M params is about the maximum you can do if you want any reasonable level of performance on your "average" non flagship mobile. I would keep to sub 100M for power efficiency sake.