So you subscribe to the belief we won't in future find mentalism in other galaxies or solar systems which operate on mechanisms we don't understand and think v e r y s l o w w w w w w w l y ?
(note. I am not a believer in AGI)
"useful" is highly contextual. The clock of the long "now" is not useful in the sense you mean, to synchronise your wristwatch. I'm still glad it exists.
Need to justify buying an expensive rig that doesn't do what you expected.
Specifically thinking the people they could do something AI with cpu, and realizing it isn't feasible. Happened at my fortune 20 company. They had to get approvals and ofc it was useless. Plenty people tried to explain, but they were the principle engineer, and out ranked everyone.
"It's not going to work", the topic changed, and we never spoke about it again.
I’ve wondered for a while: given the lower cost of SSD per GB could you build a very wide RAID0 style striped array of SSDs (maybe one per slot) to get almost RAM like read speeds?
To really go fast you’d probably have to do PCB layout and do like 256 or 1024 chips in parallel with a fast SRAM aggregation buffer feeding a GPU or TPU rig.
Or could you do the same with custom layout of cheap slower RAM?
I wonder if anyone is doing this? You would flash in a model and then just run it. It would need RAM for context but much less of it.
We've made some updates to deltafin since this was shared 5 days ago. I wonder if you'd be able to get better speeds now? https://github.com/gavamedia/deltafin
Anyone who knows the state of NVMe hardware more than me know if this would obliterate the lifespan of your drive? Seems like the biggest limitation to me (some people are probably fine with letting their Macs churn over the weekend).
No problem at all to read data over and over. In fact, LLM weights are a great candidate for low-quality flash that can't handle a lot of write cycles, and you want a large amount of storage cheaply...
The title should probably be edited to specify "M1 Max" instead of "M1 Mac". You aren't running K3 on a base M1 anytime soon. Either way, still a very impressive project.
Now set it up with an agent and a permanent `/goal` to say it cannot stop until it has solved for speed, then leave it on and livestream so we can all see when it becomes exponential. Could have the Eternal Jukebox playing in the background!
Does anyone else feel like the writing is on the wall for a future of local models? Spamming data centres everywhere, powering them, having to commit insane capital to hardware, all the effort to serve inference over a network reliably - when here we are with a frontier model nearly running on a laptop.
Local AI on your device seems like a much more likely future to me than datacenters in space. For inference at least, training is another story.
The point of these engineering tricks is to see the envelope of what's possible. You can use these tricks to both run a bigger model on smaller hardware or run a smaller model on smaller hardware.
Thanks for sharing, tito! I figured it was time I chimed in properly, as the lead on this project.
Quick mission statement, since it explains everything below: Deltafin runs the full, never-pruned, 2.8-trillion-parameter Kimi K3... all 16 experts, every token, no shortcuts, nothing re-encoded down to "close enough." Speed is not allowed to come from quality.
A few similar K3 projects have shown up claiming to be the same thing, just faster. But they all get there by shrinking the model - quantizing it down, trading fidelity for speed, calling the results close enough. Those are great projects, and have real value - but they're very different projects. Deltafin's purpose remains the real full K3, unaltered and uncut, as fast* as possible.
* it's not fast
Next, an update since this was posted five days ago. Thanks to everyone who filed issues, including a couple of you running M3 Ultra and M5 Max boxes, which helped a lot!
Since this was posted, throughput on the M1 Max laptop went from 0.0141 tok/s to 0.2847 tok/s - about 20x, and still climbing. Most of that came from routine hardening: fixing a storage race, a rust/c++ rewrite, and tolerating a float32 rounding drift that looked like a routing bug until it wasn't.
None of that is a moonshot (get it?). It's the boring, necessary work of making something this huge survive on consumer hardware.
Now one huge thing to get out of the way, because this is about half the comments: is this a viable business product, the kind of thing that makes people in suits happy because it helps them sell widgets? No. My interest in computing goes beyond that, and I suspect a lot of yours does too. I'd like to think that's still what hacker news is largely about.
A research project usually isn't the useful thing itself. It's what falls out of chasing it.
It is genuinely hard to squeeze even a small speed gain out of a model this size on a machine this small. Yes, an M1 Max is a good laptop. But Kimi's own recommended setup is 16 nodes at ~300GB of VRAM each. That's $2,000,000 of hardware. Next to that, this laptop is an ant lifting a car. And that is the point of this project - the insane challenge.
My hope is that if we push as hard as we can to get the largest public model ever released running on the smallest machine that can plausibly hold it, some of what falls out along the way could be useful elsewhere... maybe to smaller models, on smaller machines, running faster than they do today.
But first I wanted to find out whether this was even possible at all. K3 is the largest public model that exists. Nobody had run something this size on a laptop before. The closest comparable projects like colibri and DwarfStar ran quantized models at half this size. Turns out: yes, it's doable, but slow af.
But I've spent a long time frustrated with the quality ceiling on local models, and quantization is a big part of that ceiling. The hallucinations. The reasoning that almost holds together, but then doesn't.
So I'm done compromising on that axis. This isn't going to be another quantized, slightly-worse copy of the real model wearing its name.
Maybe no techniques we’re using are groundbreaking at all in the end. Bummer. But, at least I tried. Try and fail at life, people. That’s okay. Just try, and at least you’ll have lived a little. It's a decent way to spend the time you get.
If people want to keep following this, I'll post updates here instead of just on Reddit like I have been.
34 comments
[ 2.4 ms ] story [ 49.4 ms ] thread(note. I am not a believer in AGI)
"useful" is highly contextual. The clock of the long "now" is not useful in the sense you mean, to synchronise your wristwatch. I'm still glad it exists.
Need to justify buying an expensive rig that doesn't do what you expected.
Specifically thinking the people they could do something AI with cpu, and realizing it isn't feasible. Happened at my fortune 20 company. They had to get approvals and ofc it was useless. Plenty people tried to explain, but they were the principle engineer, and out ranked everyone.
"It's not going to work", the topic changed, and we never spoke about it again.
- bunch of people only ~4 years ago
Consider this like if it were the first test
Soon decent speed across two Mac Studios with 512GB of RAM.
And it gets worse with every token.
To really go fast you’d probably have to do PCB layout and do like 256 or 1024 chips in parallel with a fast SRAM aggregation buffer feeding a GPU or TPU rig.
Or could you do the same with custom layout of cheap slower RAM?
I wonder if anyone is doing this? You would flash in a model and then just run it. It would need RAM for context but much less of it.
We've made some updates to deltafin since this was shared 5 days ago. I wonder if you'd be able to get better speeds now? https://github.com/gavamedia/deltafin
I don't know if I'd call this "running"
idk how people access (soldout) and even afford 512GB RAM MacStudio's. Isn't it $40k or so?
Local AI on your device seems like a much more likely future to me than datacenters in space. For inference at least, training is another story.
The point of these engineering tricks is to see the envelope of what's possible. You can use these tricks to both run a bigger model on smaller hardware or run a smaller model on smaller hardware.
Quick mission statement, since it explains everything below: Deltafin runs the full, never-pruned, 2.8-trillion-parameter Kimi K3... all 16 experts, every token, no shortcuts, nothing re-encoded down to "close enough." Speed is not allowed to come from quality.
A few similar K3 projects have shown up claiming to be the same thing, just faster. But they all get there by shrinking the model - quantizing it down, trading fidelity for speed, calling the results close enough. Those are great projects, and have real value - but they're very different projects. Deltafin's purpose remains the real full K3, unaltered and uncut, as fast* as possible.
Next, an update since this was posted five days ago. Thanks to everyone who filed issues, including a couple of you running M3 Ultra and M5 Max boxes, which helped a lot!Since this was posted, throughput on the M1 Max laptop went from 0.0141 tok/s to 0.2847 tok/s - about 20x, and still climbing. Most of that came from routine hardening: fixing a storage race, a rust/c++ rewrite, and tolerating a float32 rounding drift that looked like a routing bug until it wasn't.
None of that is a moonshot (get it?). It's the boring, necessary work of making something this huge survive on consumer hardware.
Now one huge thing to get out of the way, because this is about half the comments: is this a viable business product, the kind of thing that makes people in suits happy because it helps them sell widgets? No. My interest in computing goes beyond that, and I suspect a lot of yours does too. I'd like to think that's still what hacker news is largely about.
A research project usually isn't the useful thing itself. It's what falls out of chasing it.
It is genuinely hard to squeeze even a small speed gain out of a model this size on a machine this small. Yes, an M1 Max is a good laptop. But Kimi's own recommended setup is 16 nodes at ~300GB of VRAM each. That's $2,000,000 of hardware. Next to that, this laptop is an ant lifting a car. And that is the point of this project - the insane challenge.
My hope is that if we push as hard as we can to get the largest public model ever released running on the smallest machine that can plausibly hold it, some of what falls out along the way could be useful elsewhere... maybe to smaller models, on smaller machines, running faster than they do today.
But first I wanted to find out whether this was even possible at all. K3 is the largest public model that exists. Nobody had run something this size on a laptop before. The closest comparable projects like colibri and DwarfStar ran quantized models at half this size. Turns out: yes, it's doable, but slow af.
But I've spent a long time frustrated with the quality ceiling on local models, and quantization is a big part of that ceiling. The hallucinations. The reasoning that almost holds together, but then doesn't.
So I'm done compromising on that axis. This isn't going to be another quantized, slightly-worse copy of the real model wearing its name.
Maybe no techniques we’re using are groundbreaking at all in the end. Bummer. But, at least I tried. Try and fail at life, people. That’s okay. Just try, and at least you’ll have lived a little. It's a decent way to spend the time you get.
If people want to keep following this, I'll post updates here instead of just on Reddit like I have been.