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anemll

HN member
Joined
Wed, Feb 12, 2025, 3:35 PM UTC
HN karma
291
Public activity
23 items

About anemll

ANEMLL (pronounced like "animal") Artificial Neural Engine Machine Learning Library, Open Source Project

www.anemll.com https://github.com/anemll https://huggingface.co/anemll https://github.com/anemll

Recent public activity

  1. comment
    Comment #47605823

    Check it out, you might be able to speed it up using this https://github.com/Anemll/anemll-flash-mlx https://x.com/anemll/status/2038684375425200360

  2. comment
    Comment #47498363

    17B includes 10 expert plus one shared. So actual size of the expert is much smaller

  3. comment
    Comment #47493921

    Check my repo, I had added some support for GUFF/untloth, Q3,Q5/Q8 https://github.com/Anemll/flash-moe/blob/iOS-App/docs/gguf-h...

  4. comment
    Comment #47493564

    Thanks for posting this, that's how I first found out about Dan's experiment! SSD speed doubled in the M5P/M generation, that makes it usable! I think one paper under the radar is …

  5. comment
    Comment #47493458

    SSD streaming to compute units is new. M4 max can do 15 t/s with its 15GB/s drives

  6. comment
    Comment #47493446

    Yes, SSD speed is critical though. The repo has macOS builds for CLI and Desktop. It's early stages though. M4 Max gets 10-15 TPS on 400B depending on quantization. Compute is an i…

  7. comment
    Comment #47492835

    multiple NAND, and apple already used it in Mac Studio. Plus better cooling

  8. comment
    Comment #47492824

    both, tbh

  9. comment
    Comment #47492792

    Probably 2x speed for Mac Studio this year if they do double NAND ( or quad?)

  10. comment
    Comment #47490071

    [flagged]

  11. story
    iPhone 17 Pro Demonstrated Running a 400B LLM

    https://xcancel.com/anemll/status/2035901335984611412

  12. comment
    Comment #46251822

    Tensor Parallel test with RDMA last week https://x.com/anemll/status/1996349871260107102 Note fast sync workaround

  13. comment
    Comment #46048148

    In macOS 26.2 (Tahoe) beta, Apple introduced a low-latency Thunderbolt 5 RDMA driver, enabling up to 80 Gb/s bidirectional bandwidth for Mac clustering—ideal for distributed ML on …

  14. story
  15. comment
    Comment #45244563

    It’s also supported in Apple Neural Engine https://github.com/Anemll/Anemll

  16. comment
  17. story
  18. comment
    Comment #43917545

    We can ran 2000 or 4000 context with ANE

  19. comment
    Comment #43917532

    M4 max should work at 120GB for ANE and 500+ for GPU. So GPU will be 3-4 times faster for anything over 1-3B. ANE is likely as fast for prefill due to higher FLOPs

  20. comment
    Comment #43886921

    Right.I was thinking about it, you still need batch refill, however, Apple Core ML tools were failing for attention activations quantization. Long context, pre-fill is still comput…

  21. comment
    Comment #43886877

    Yes for GPU, however ANE only supports FP16 plus integers. M4/A17 added accelerated int8 that is twice faster than FP16

  22. comment
    Comment #43884485

    Memory bandwidth is the main bottleneck. It got better with M3/M4. ANE is really fast in FLOPS but low in memory bandwidth.

  23. comment
    Comment #43884470

    What hardware are you on? Most models are memory bandwidth limited. ANE was limited to 64GB/s prior to M3 Max or M4 pro. If you are on M1, GPU will be significantly faster for 3-8B…