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> Currently, only LLaMA-7B is supported since I haven't figured out how to merge the tensors of the bigger models. However, in theory, you should be able to run 65B on a 64GB MacBook

Suddenly the choices Apple made with its silicon are looking like pure genius as there will be a lot of apps using this that are essentially exclusive to their platform. Even with the egregious ram pricing.

With a lot of fine tuning if you squint this is a useful/convincing "ChatGPT on a laptop" minus the corporate lobotomy only a few short months after release. Very exciting! I actually care about the upcoming Mac Pro now.

$3999 Mac Studio with 64GB ram. +$800 for 128GB.

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Given that the RAM is directly accessible by CPU, GPU and DPU/Neural cores, it really is premium RAM.

Apple's visionary hardware team has finally caught up with Apple's visionary high RAM prices! :)

Also um... Idk if anyone cares but apple didn't do anything to make unified memory.

All arm chips do this lmao

Apple just has the best known arm chips with the highest mobile performance (yes faster server arm chips exist too)

It’s amazing how Apple “doesn’t do” anything, but manages to define industry trends over and over again!

That's what brand recognition and brand loyalty (and the money they generate) allows you to do. This is not necessarily a bad thing since it forces other competitors - who like to artificially limit their products - to actually get of their greedy asses and compete.

Yeah which is why... Unified memory on Chromebooks and android devices as well as surface pro serve the same purpose.

It's marketed as an upsell by apple because it's an advantage of ARM but many people will never care to learn what that means

LLaMA doesn't perform very well with answering questions.

If you ask it "What color is the sky?" It will reply with something like "Why is ice cold? Why do we exist?"

LLaMA isn't built on RLHF, so it may be necessary to create a more extensive prompt. For example:

```

You are a super intelligent honest question-answering system.

Q: What's 2+2?

A: 4

Q: What color is the sky?

```

This is Commodore 64 tier answers

That's the point, even C64 tier examples in one shot or few shot learning do wonders for changing the behavior of the model.

That's just an example

It performs very well, but you have to give it the right prompt and model params. I imagine it will be ChatGPT level once it is trained with RLHF

Ask dumb questions receive dumb answers.

Wonder why AMD/Intel/Nvidia haven’t invented some sort of device that allows the processor and graphics to share memory like Apple has done.

The Nvidia Jetson/Tegra line did (and does) unified memory. Released 2014[0]. Nvidia already has ARM cores. They’re just terrible compared to Apple, Samsung, etc.

This is what the attempted ARM acquisition by Nvidia was about - with the ARM talent, IP, etc they’d be able to integrate more than just memory (GPU, CPU, connectivity via Mellanox, etc).

Regulators shut it down (for good reason) but I can’t help but think we would have seen some really interesting and revolutionary platforms come from it.

[0] - https://en.m.wikipedia.org/wiki/Tegra#Tegra_K1

Nvidia actually does support this via unified memory. It’s actually an amazing performance trick since you can avoid launching a bunch of kernels just to ferry data back and forth. I did this on a a GTX 1080 for a school project to speed up random shuffling of large amounts of data.

However, even without this feature, you can implement this sort of thing manually in most cases and you’re already being careful on a GPU to respect the cache (only working with one contiguous set of data of memory at a time).

Really, we just need some good systems devs working on running these huge models.

They have, see eg this from 10 years ago: https://www.tomshardware.com/news/AMD-HSA-hUMA-APU,22324.htm...

Implementing the software support and getting operating systems to play along and fragmentation between GPU vendors, as always with GPUs on x86, have been the problems. From all accounts it's been working reasonably well on the consoles though.

Also chicken-and-egg: low GPU compute usage uptake outside of games has meant it's not improved lately.

Both AMD and Intel have APUs. However they’re limited by being minority products (so very low support from software) and often have limits on how much ram can be accessed by the GPU and/or have very weak GPUs.

PCIe devices like GPUs can access system memory.

Integrated GPUs also access system memory via the same bus as the CPU.

It’s not really a new technique. Apple just shipped a highly integrated unit with large memory bandwidth.

That's a pretty important just. They chose to go down this path at this time and shipped, now I can run the 64B Llama on a widely available $3,999 prosumer device.

How much would a PC that can do that currently cost me and can I have it by tomorrow?

puts on my jaded realist hat

It's a great option if you have the hardware and want the speed. It's table stakes when other vendors like Nvidia, Intel, Qualcomm and Microsoft have acceleration though. Raw-compute-for-the-buck has always been a blowout with Apple Silicon GPUs, and it's not any prettier now that 4nm 40XX series cards are available. Hell, an Intel A770 with 16gb of VRAM is still cheaper than adding 16gb of RAM to a Mac Mini.

It's good stuff, but pitched a bit hard with all the marketing. From where I'm standing, it looks like Apple is playing catch-up with their GPUs and acceleration tech.

tentatively removes hat of jaded realism

>How much would a PC that can do that currently cost me and can I have it by tomorrow?

At the moment, seems like Apple has an edge here. On PC for single GPU you need an NVIDIA A40, which used prices for is about $2500, and not at retail stores.

If you don't mind having two GPUs then two $800 3090 GPUs works, but that's a workstation build you'll have to order from Puget or something. That's probably faster than Apple.

My gut instinct is that there's some low hanging fruit here and in the next couple weeks 64B Llama will run comparably or faster on any PC with a single 4090/3090 and 64 or 128 GB of system memory. But probably not any PC laptops that aren't 17 inch beasts, Apple will keep that advantage.

…and for models that require 64GB of VRAM? 120GB of VRAM?

You can get a 128GB UMA mac for less than a single 48GB a100, let alone a single 96GB a100.

I think Apple got incredibly lucky here, but I don’t see how the PC world catches them any time soon. We’ve all known that UMA is theoretically better for ages, but Apple’s timing couldn’t be better. And scale economies mean they can sell the same chip to people who need 100GB of system RAM and people who need 100GB of VRAM.

If they can get their GPU / neural performance up and sort out their terrible relationship with academic research, they could snipe ML away from nvidia. It seems very unlikely, but it’s kind of stunning that it’s even in the realm of possibility.

> they could snipe ML away from nvidia.

If Nvidia announced tomorrow that they were cancelling every datacenter deal they had, open-sourcing CUDA and publishing their entire patent library to the creative commons, I would still not believe you.

This is a fun project for people with Apple Silicon machines who want to participate in the AI happenings, but I don't think you can warp it into a call for Nvidia's head. Let's wait until Apple pulls the curtains on their rackmount Mac Pros, so we can compare it with Nvidia's ARM server offerings: https://www.nvidia.com/en-us/data-center/grace-cpu/

Whoa, who’s calling for nvidia’s head? Not me.

My point was that the PC architecture of separate system and GPU memory is hitting a wall that means inefficiency and higher prices.

I have little doubt that Nvidia’s attempted acquisition of ARM was in part because nvidia recognized this. I expect they are exploring other UMA approaches. But it will be hard in the fragmented, not-vertically-integrated model.

Apple’s advantage here is one platform that can scale: it is hard to imagine Grace and similar running Windows on developer’s desktops. Maybe!

But my point was that, shockingly, Apple has a chance here. A small chance, as I said, but I don’t think anyone (including Apple) saw just how soon UMA was going to become a competitive advantage.

Nvidia doesn't need to acquire ARM to sell systems with unified memory. The Tegra boards are all mixed-address-space systems, and CUDA lets you manipulate memory over PCI. They see the writing on the wall, which is why they've been building systems for the past decade that reflect this philosophy.

If you think it's hard to imagine Nvidia hardware running on a developer desktop, wait until you hear about what happened when Macs tried entering the server market.

You can buy a prebuilt pc with a 4090 for less - which is significantly more powerful but still in the 3xxx$.

You could go cheaper with a 3090 which has the same vram and it's just slower.

I think the best combo is a serious Nvidia pc for AI + a cheap MacBook air for portability.

You need two 3090s or 4090s to fit 65B even in 4bit. It's a big one.

That said, if you're fine with slower speeds then two P40s could get the job done for $150 each. (Not sure how much slower this would go though.)

There's even an Asus laptop with a 4099 for 3999$

Laptop 4090 is desktop 4080

Intel GPUs have had that feature for over two decades, and it was also called UMA; synonymous with cheap and slow, before Apple hyped that term and made a UMA system that actually had decent performance.

1. They have

2. CPUs and GPUs typically disagree on whether they want high bandwidth or low latency, apple managed to keep both happy but it's very hard to do on a PC where the RAM,CPU, and GPU are quite far apart and also nowhere near as homogenous as Apple have them.

Nvidia didn't implement it in their OpenCL drivers because OpenCL's shared virtual memory spec is so terrible that khronos made it optional in OpenCL 3.0 and Intel (the only company actually having high quality OpenCL implementations) dropped shared virtual memory like a hot potato and instead introduced their own unified shared memory extension for OpenCL which they use for SyCL and oneAPI.

Games don't want slow memory accesses because it tanks the fps so there was no incentive to have a working implementation (except in laptops).

And also - vram is a moat that keeps the cost of "professional" cards absurdly high without making them actually faster than consumer cards.

as pointed out, both Jetson and UM do this, but the upcoming Grace does it at higher bandwidth than the apple chip.

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