8 ms·

Which takes a $20k thunderbolt cluster of 2 512GB RAM Mac Studio Ultras to run at full quality…

by reilly3000 8mo ago

teaearlgraycold 8mo ago
Which while expensive is dirt cheap compared to a comparable NVidia or AMD system.
- blharr 8mo ago
  What speed are you getting at that level of hardware though?
- SchemaLoad 8mo ago
  It's still very expensive compared to using the hosted models which are currently massively subsidised. Have to wonder what the fair market price for these hosted models will be after the free money dries up.
  - cactusplant7374 8mo ago
    Inference is profitable. Maybe we hit a limit and we don't need as many expensive training runs in the future.
    - teaearlgraycold 8mo ago
      For sure Claude Code isn’t profitable
      - bdangubic 8mo ago
        Neither was Uber and … and …
        plagiarist 8mo ago
        Businesses will desire me for my insomnia once Anthropics starts charging congestion pricing.
        bdangubic 8mo ago
        that is coming for sure to replace the "500" errors
    - paxys 8mo ago
      Inference APIs are probably profitable, but I doubt the $20-$100 monthly plans are.
      - cactusplant7374 8mo ago
        I wouldn’t be so sure. Most users aren’t going to use up their quota every week.
  - whatsupdog 8mo ago
    I wonder if the "distributed AI computing" touted by some of the new crypto projects [0] works and is relatively cheaper. 0. https://www.daifi.ai/ https://www.daifi.ai/
PlatoIsADisease 8mo ago
[flagged]
- zozbot234 8mo ago
  70B dense models are way behind SOTA. Even the aforementioned Kimi 2.5 has fewer active parameters than that, and then quantized at int4. We're at a point where some near-frontier models may run out of the box on Mac Mini-grade hardware, with perhaps no real need to even upgrade to the Mac Studio.
  - PlatoIsADisease 8mo ago
    >may I'm completely over these hypotheticals and 'testing grade'. I know Nvidia VRAM works, not some marketing about 'integrated ram'. Heck look at /r/locallama/ There is a reason its entirely Nvidia.
    - K0balt 8mo ago
      Mmmm, not really. I have both a4x 3090 box and a Mac m1 with 64 gb. I find that the Mac performs about the same as a 2x 3090. That’s nothing stellar, but you can run 70b models at decent quants with moderate context windows. Definitely useful for a lot of stuff.
      - PlatoIsADisease 8mo ago
        >quants >moderate context windows Really had to modify the problem to make it seem equal? Not that quants are that bad, but the context windows thing is the difference between useful and not useful.
        K0balt 8mo ago
        Equal to the 2x3090? Yeah it’s about equal in every way, context windows included. As for useful at that scale? I use mine for coding a fair bit, and I don’t find it a detractor overall. It enforces proper API discipline, modularity, and hierarchal abstraction. Perhaps the field of application makes that more important though. (Writing firmware and hardware drivers). It also brings the advantage of focusing exclusively on the problems that are presented in the limited context, and not wandering off on side quests that it makes up. I find it works well up to about 1KLOC at a time. I wouldn’t imply they were equal to commercial models, but I would definitely say that local models are very useful tools. They are also stable, which is not something I can say for SOTA models. You cal learn how to get the best results from a model and the ground doesn’t move underneath you just when you’re on a roll.
bigyabai 8mo ago
"Full quality" being a relative assessment, here. You're still deeply compute constrained, that machine would crawl at longer contexts.
0xbadcafebee 8mo ago
Most benchmarks show very little improvement of "full quality" over a quantized lower-bit model. You can shrink the model to a fraction of its "full" size and get 92-95% same performance, with less VRAM use.
- MuffinFlavored 8mo ago
  > You can shrink the model to a fraction of its "full" size and get 92-95% same performance, with less VRAM use. Are there a lot of options how "how far" do you quantize? How much VRAM does it take to get the 92-95% you are speaking of?
  - bigyabai 8mo ago
    > Are there a lot of options how "how far" do you quantize? So many: https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/ https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overvie... > How much VRAM does it take to get the 92-95% you are speaking of? For inference, it's heavily dependent on the size of the weights (plus context). Quantizing an f32 or f16 model to q4/mxfp4 won't necessarily use 92-95% less VRAM, but it's pretty close for smaller contexts.
    - MuffinFlavored 8mo ago
      Thank you. Could you give a tl;dr on "the full model needs ____ this much VRAM and if you do _____ the most common quantization method it will run in ____ this much VRAM" rough estimate please?
      - omneity 8mo ago
        It’s a trivial calculation to make (+/- 10%). Number of params == “variables” in memory VRAM footprint ~= number of params * size of a param A 4B model at 8 bits will result in 4GB vram give or take, same as params. At 4 bits ~= 2GB and so on. Kimi is about 512GB at 4 bits.
deaux 8mo ago
And that's at unusable speeds - it takes about triple that amount to run it decently fast at int4. Now as the other replies say, you should very likely run a quantized version anyway.
deleted 8mo ago
[deleted]
polynomial 8mo ago
Depending on what your usage requirements are, Mac Minis running UMA over RDMA is becoming a feasible option. At roughly 1/10 of the cost you're getting much much more than 1/10 the performance. (YMMV) https://buildai.substack.com/i/181542049/the-mac-mini-moment https://buildai.substack.com/i/181542049/the-mac-mini-moment
- danw1979 8mo ago
  I did not expect this to be a limiting factor in the mac mini RDMA setup ! - > Thermal throttling: Thunderbolt 5 cables get hot under sustained 15GB/s load. After 10 minutes, bandwidth drops to 12GB/s. After 20 minutes, 10GB/s. Your 5.36 tokens/sec becomes 4.1 tokens/sec. Active cooling on cables helps but you’re fighting physics. Thermal throttling of network cables is a new thing to me…
  - cat_plus_plus 8mo ago
    I admire patience of anyone who runs dense models on unified memory. Personally, I would rather feed an entire programming book or code directory to a sparse model and get an answer in 30 seconds and then use cloud in rare cases it's not enough.
  - polynomial 8mo ago
    Luckily we're having a record cold winter and your setup can double as a personal space heater.

Which takes a $20k thunderbolt cluster of 2 512GB RAM Mac Studio Ultras to run at full quality…

teaearlgraycold 8mo ago

Which while expensive is dirt cheap compared to a comparable NVidia or AMD system.

blharr 8mo ago
What speed are you getting at that level of hardware though?
SchemaLoad 8mo ago
It's still very expensive compared to using the hosted models which are currently massively subsidised. Have to wonder what the fair market price for these hosted models will be after the free money dries up.
- cactusplant7374 8mo ago
  Inference is profitable. Maybe we hit a limit and we don't need as many expensive training runs in the future.
  - teaearlgraycold 8mo ago
    For sure Claude Code isn’t profitable
    - bdangubic 8mo ago
      Neither was Uber and … and …
      - plagiarist 8mo ago
        Businesses will desire me for my insomnia once Anthropics starts charging congestion pricing.
        bdangubic 8mo ago
        that is coming for sure to replace the "500" errors
  - paxys 8mo ago
    Inference APIs are probably profitable, but I doubt the $20-$100 monthly plans are.
    - cactusplant7374 8mo ago
      I wouldn’t be so sure. Most users aren’t going to use up their quota every week.
- whatsupdog 8mo ago
  I wonder if the "distributed AI computing" touted by some of the new crypto projects [0] works and is relatively cheaper. 0. https://www.daifi.ai/ https://www.daifi.ai/

PlatoIsADisease 8mo ago

[flagged]

zozbot234 8mo ago
70B dense models are way behind SOTA. Even the aforementioned Kimi 2.5 has fewer active parameters than that, and then quantized at int4. We're at a point where some near-frontier models may run out of the box on Mac Mini-grade hardware, with perhaps no real need to even upgrade to the Mac Studio.
- PlatoIsADisease 8mo ago
  >may I'm completely over these hypotheticals and 'testing grade'. I know Nvidia VRAM works, not some marketing about 'integrated ram'. Heck look at /r/locallama/ There is a reason its entirely Nvidia.
  - K0balt 8mo ago
    Mmmm, not really. I have both a4x 3090 box and a Mac m1 with 64 gb. I find that the Mac performs about the same as a 2x 3090. That’s nothing stellar, but you can run 70b models at decent quants with moderate context windows. Definitely useful for a lot of stuff.
    - PlatoIsADisease 8mo ago
      >quants >moderate context windows Really had to modify the problem to make it seem equal? Not that quants are that bad, but the context windows thing is the difference between useful and not useful.
      - K0balt 8mo ago
        Equal to the 2x3090? Yeah it’s about equal in every way, context windows included. As for useful at that scale? I use mine for coding a fair bit, and I don’t find it a detractor overall. It enforces proper API discipline, modularity, and hierarchal abstraction. Perhaps the field of application makes that more important though. (Writing firmware and hardware drivers). It also brings the advantage of focusing exclusively on the problems that are presented in the limited context, and not wandering off on side quests that it makes up. I find it works well up to about 1KLOC at a time. I wouldn’t imply they were equal to commercial models, but I would definitely say that local models are very useful tools. They are also stable, which is not something I can say for SOTA models. You cal learn how to get the best results from a model and the ground doesn’t move underneath you just when you’re on a roll.

bigyabai 8mo ago

"Full quality" being a relative assessment, here. You're still deeply compute constrained, that machine would crawl at longer contexts.

0xbadcafebee 8mo ago

Most benchmarks show very little improvement of "full quality" over a quantized lower-bit model. You can shrink the model to a fraction of its "full" size and get 92-95% same performance, with less VRAM use.

MuffinFlavored 8mo ago
> You can shrink the model to a fraction of its "full" size and get 92-95% same performance, with less VRAM use. Are there a lot of options how "how far" do you quantize? How much VRAM does it take to get the 92-95% you are speaking of?
- bigyabai 8mo ago
  > Are there a lot of options how "how far" do you quantize? So many: https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/ https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overvie... > How much VRAM does it take to get the 92-95% you are speaking of? For inference, it's heavily dependent on the size of the weights (plus context). Quantizing an f32 or f16 model to q4/mxfp4 won't necessarily use 92-95% less VRAM, but it's pretty close for smaller contexts.
  - MuffinFlavored 8mo ago
    Thank you. Could you give a tl;dr on "the full model needs ____ this much VRAM and if you do _____ the most common quantization method it will run in ____ this much VRAM" rough estimate please?
    - omneity 8mo ago
      It’s a trivial calculation to make (+/- 10%). Number of params == “variables” in memory VRAM footprint ~= number of params * size of a param A 4B model at 8 bits will result in 4GB vram give or take, same as params. At 4 bits ~= 2GB and so on. Kimi is about 512GB at 4 bits.

deaux 8mo ago

And that's at unusable speeds - it takes about triple that amount to run it decently fast at int4. Now as the other replies say, you should very likely run a quantized version anyway.

deleted 8mo ago

[deleted]

polynomial 8mo ago

Depending on what your usage requirements are, Mac Minis running UMA over RDMA is becoming a feasible option. At roughly 1/10 of the cost you're getting much much more than 1/10 the performance. (YMMV) https://buildai.substack.com/i/181542049/the-mac-mini-moment https://buildai.substack.com/i/181542049/the-mac-mini-moment

danw1979 8mo ago
I did not expect this to be a limiting factor in the mac mini RDMA setup ! - > Thermal throttling: Thunderbolt 5 cables get hot under sustained 15GB/s load. After 10 minutes, bandwidth drops to 12GB/s. After 20 minutes, 10GB/s. Your 5.36 tokens/sec becomes 4.1 tokens/sec. Active cooling on cables helps but you’re fighting physics. Thermal throttling of network cables is a new thing to me…
- cat_plus_plus 8mo ago
  I admire patience of anyone who runs dense models on unified memory. Personally, I would rather feed an entire programming book or code directory to a sparse model and get an answer in 30 seconds and then use cloud in rare cases it's not enough.
- polynomial 8mo ago
  Luckily we're having a record cold winter and your setup can double as a personal space heater.