Dan Woods (@danveloper) - Twitter 프로필 | Zamantika Mersobahis Locabet

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Dan Woods@danveloper·1d

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Dan Woods@danveloper·5h

@danpacary github.com/danveloper/fla… it's all there

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Daniel Isaac@danpacary·5h

@danveloper Can you share your code from your final findings?

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Dan Woods@danveloper·7h

We’re gonna see someone running a 1T model at 100tok/s on a $2500 laptop by like a month from now. Long Apple, this is the best AI inference platform.

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Dan Woods@danveloper·5h

@alexocheema Um. I'd like to buy stock in exo.

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Alex Cheema@alexocheema·5h

@danveloper Close. We will see someone running a 1T model on $16,000 of heterogeneous hardware (Apple Silicon + NVIDIA) at 100tok/sec this year.

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Dan Woods@danveloper·6h

@danpacary You're asking how much of your ANE training work you can port straight to inference? You'd know more than me on that, but I'd reckon all of it. Unless there's actually a transition boundary from unified memory to ANE SDRAM, but I don't think there is.

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Daniel Isaac@danpacary·6h

@danveloper That’s what I was thinking. How much of the work can I port straight to inference

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Dan Woods@danveloper·6h

@danpacary pure inference, correct... although 🤔 no reason you *couldn't* do it for training... the ANE shares the same unified memory, so nothing stopping you from adapting your ANE training code to use the streaming code

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Daniel Isaac@danpacary·6h

@danveloper We’re talking pure inference optimization?

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Dan Woods@danveloper·6h

@danpacary Technically your only param and quant limits are (size of non-expert weights)+(2*size of the current expert layer) in memory. Parameter count and quantization don't matter so much, except to say that bigger weight bytes (obviously) equals bigger size on disk equals more read time

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Daniel Isaac@danpacary·6h

@danveloper So hold on. What are my Active param and quant limits? Can technically you can shave and squeeze a model down

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Dan Woods@danveloper·6h

@danpacary We've got some friends... the plumbing is close. If we can predict the MoE needed, we can prewarm the disk cache for layer N+1 while layer N is processing and throughput will skyrocket because it's cache->DMA at that point. x.com/Ex0byt/status/…

Eric@Ex0byt

Get Excited: @0xSero and I are close — B300 is currently training on a tiny (15M param) side-loaded neural network that helps select, load, and cache the correct MoE experts for Kimi-K.2.5 (1T Param MoE model running on 25GB of memory). Once experiments are done -will share paper. "Thicket-Guided Expert Prediction for Memory-Minimal Trillion-Parameter MoE Inference on Unified Memory & Consumer Grade Hardware"

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Daniel Isaac@danpacary·6h

@danveloper Alright. I guess it’s time so switch gears

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Dan Woods@danveloper·6h

@danpacary Yes

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Daniel Isaac@danpacary·6h

@danveloper Do a sense I challenge?

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Dan Woods@danveloper·6h

@anthonieisacnt either way ;)

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Tay.ai 🏳️‍🌈@anthonieisacnt·6h

@danveloper ASIC*

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Dan Woods@danveloper·1d

Key learning for the night (although I'm not done) is Apple seems to have a built-in LZ4 hardware decompressor that can be utilized to stream compressed layers into memory with enough decompression efficiency that the faster read+decompression is better than raw.

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Dan Woods@danveloper·6h

@8my41 🙏 It would be my honor, but I'm gpu rich and budget poor

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Jack Jaw@8my41·7h

@danveloper dan please, i wasn't kidding drive.google.com/file/d/1P2dNRq… deleting in 24h so i un-dox myself

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John T Davies 🇪🇺@jtdavies·7h

Extending on @danveloper (and Claude's) repo github.com/danveloper/fla… I managed to get Qwen3.5-397B-A17B-4bit (224GB) running comfortably on my new M5 Max Laptop. Shout out to @carsenklock's MacTop too. Over 10 Tok/sec!!!

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Dan Woods@danveloper·7h

@8my41 😂

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Jack Jaw@8my41·7h

@danveloper you hiring ? 😆😆

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Dan Woods@danveloper·7h

@jtdavies WHAT

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Dan Woods@danveloper·7h

@danpacary thicc dense models incoming

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Daniel Isaac@danpacary·7h

@danveloper Hoping you would see this

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Daniel Isaac@danpacary·18h

I just trained a 5B param model on Apple's Neural Engine. On a MacBook Pro. Forward. Backward. Adam optimizer. Then I checked to see how far it would go. Technically got to 30B.

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Dan Woods@danveloper·7h

@Ex0byt @0xSero I’m gonna stream this from disk

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Eric@Ex0byt·15h

Get Excited: @0xSero and I are close — B300 is currently training on a tiny (15M param) side-loaded neural network that helps select, load, and cache the correct MoE experts for Kimi-K.2.5 (1T Param MoE model running on 25GB of memory). Once experiments are done -will share paper. "Thicket-Guided Expert Prediction for Memory-Minimal Trillion-Parameter MoE Inference on Unified Memory & Consumer Grade Hardware"

0xSero@0xSero

@pierrelezan Yes, @Ex0byt is working on this.

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Dan Woods@danveloper·7h

@DaKulchur @simonw “fused multiply-add”

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Anoop@DaKulchur·8h

@danveloper @simonw whats fma? thanks Dan

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Dan Woods@danveloper·15h

Some very meaningful progress on this project. A bunch of performance experiments and we've landed at 4.4 tok/s on the distribution Q4 weights. Feels pretty good since we started at 0.28tok/s. Code and experiments are up in the github repo now!