247 points acossta 4 days ago 100 comments
Bric3d 4 days ago | parent
Telemakhos 4 days ago | parent
jprd 4 days ago | parent
macintux 4 days ago | parent
mgerdts 3 days ago | parent
simbas 4 days ago | parent
sdoering 4 days ago | parent
Basically SETI for AI workloads? At least, this is the impression the landingpage left me with.
I am a bit lost, tbh.
railka 4 days ago | parent
There is no distributed execution of a single LLM across multiple GPUs using tensor parallelism here. Instead, it relies on task-level parallelism-breaking down a large workload into independent subtasks that run across separate machines. For example, generating embeddings for a million documents.
alt227 4 days ago | parent
greggsy 4 days ago | parent
pjmlp 4 days ago | parent
gizajob 4 days ago | parent
pjmlp 4 days ago | parent
https://developer.nvidia.com/gpudirect
And this is because I can't be bothered to dive into older papers, so recent NVIDIA stuff will do.
spacedcowboy 3 days ago | parent
*: hell my own compiler is moving to multi-GPU on a single computer and networked devices are the obvious next step.
juliendorra 4 days ago | parent
dekhn 4 days ago | parent
it's a good idea but you often end up with huge tail latencies, slight incompatibilities due to fleet heterogeneity, and a database/scheduler management problem.
veexx103 4 days ago | parent
What is it actually like?
tom1337 4 days ago | parent
pm90 4 days ago | parent
sigmoid10 4 days ago | parent
alt227 4 days ago | parent
macintux 4 days ago | parent
r_lee 4 days ago | parent
ecshafer 4 days ago | parent
jacquesm 4 days ago | parent
lostmsu 4 days ago | parent
nxobject 3 days ago | parent
(Interestingly enough, I think PRC and ROC are combined in public financials.)
reachableceo 3 days ago | parent
Also they have the ability to put in sufficiently large amount purchase orders to buy up whatever capacity they want.
Even at ludicrous markup prices from TSMC, ifs going to be orders of magnitude cheaper than in house a fab.
pjmlp 4 days ago | parent
Swift's main reason to go open source and have good Linux support was naturally for app developers to be able to use it on server side, which naturally wasn't going to be macOS.
I macOS wasn't required for developing iDevices apps, they would surely sell even less desktops.
nxobject 3 days ago | parent
yohannparis 4 days ago | parent
Maybe their chip team is already on the work, or already made, a server chip for AI purposes used internally, and doesn't have to talk about it yet.
alt227 4 days ago | parent
kolinko 4 days ago | parent
curt15 4 days ago | parent
nxobject 3 days ago | parent
(As much as conglomerates are fun…)
gempir 4 days ago | parent
brandonpelfrey 4 days ago | parent
pjmlp 4 days ago | parent
skhameneh 4 days ago | parent
Apple has released some small models previously that seemed genuinely great, along with some quality research/efforts. I’m just not seeing the same level of depth here, am I missing something?
tw1984 4 days ago | parent
nothing new here.
djoldman 4 days ago | parent
https://arxiv.org/pdf/2610.12970
Shortest promoted paper I've seen from a FAANG.
sehw 4 days ago | parent
lynndotpy 4 days ago | parent
botanrice 4 days ago | parent
lynndotpy 4 days ago | parent
twosdai 4 days ago | parent
lynndotpy 4 days ago | parent
willquack 4 days ago | parent
reachableceo 3 days ago | parent
chasil 4 days ago | parent
I would definitely opt out.
Is Apple going to compensate the owners?
bee_rider 3 days ago | parent
elcritch 3 days ago | parent
There's plenty of devices operating in giant supercomputer networks accessible for cheap to consumers! Unfortunately the consumers in question are spam farms, bot networks, ddos rings, and other mostly criminal types.
It would be cool if similar networks could be used for good instead. Earning credits providing compute that could redeem for spurts of fast parallel compute.
eikenberry 4 days ago | parent
magicmicah85 4 days ago | parent
nico 4 days ago | parent
Building the classification part now :) https://jeffyclassify.com/
cmdrk 4 days ago | parent
jacquesm 4 days ago | parent
MomsAVoxell 4 days ago | parent
jacquesm 4 days ago | parent
I wished they had a bit more memory bandwidth though, this is quite annoying.
d--b 4 days ago | parent
mcculley 4 days ago | parent
Yes, we can distribute tasks to idle machines in homes and on office desktops, but it is often much more expensive than sending it to a data center where power is cheaper and the hardware is more efficient.
testfrequency 4 days ago | parent
I’m very curious if that project evolved to this……..going to message a few friends now heh
buildbot 4 days ago | parent
willquack 4 days ago | parent
Then universities and business could reduce their need for cloud and have a reason to put that spending towards higher end Macs...
yardie 4 days ago | parent
splittydev 4 days ago | parent
janandonly 4 days ago | parent
nsagent 4 days ago | parent
Future work will extend Minions to support distributed model training in addition to inference
That could allow enthusiasts to pool compute resources on devices like Mac Studios, Mac Minis, etc to perform large scale federated training. While it'll be massively inefficient compared to running in a data center, it could democratize open weights models even more.It would be especially useful for RL post training of existing models, which is where much of the agentic abilities arise from.
curt15 4 days ago | parent
ElijahLynn 3 days ago | parent
1-6 3 days ago | parent
bigyabai 3 days ago | parent
Look at it like this: a maxed-out M5 Ultra Mac Studio starts at $6,799, and the iGPU benchmarks similar to an $800 RTX 4070 Ti SUPER. That's a 3nm SOC getting outperformed by a 5nm dGPU; Apple Silicon is years behind Nvidia's consumer offerings, and that's before we even compare with datacenter-grade DGX/Instinct GPUs.
sleepybrett 3 days ago | parent
badc0ffee 3 days ago | parent
It has 6x the RAM, 2x the memory bandwidth and looks like it totally outclasses the RTX 4070 Ti SUPER. I did some searching and it looks like a close comparison would be the older M3 Ultra Mac Studio, although again, with a lot less RAM.
bigyabai 3 days ago | parent
Don't take my word for it; go look at the number of datacenters that offer MLX or MPS inference. It isn't a market, nobody wants these GPUs even during a RAM shortage.
luizfelberti 3 days ago | parent