Viewing profile — anemll
anemll
HN member- Joined
- Wed, Feb 12, 2025, 3:35 PM UTC
- HN karma
- 291
- Public activity
- 23 items
- HN profile
- View on Hacker News ↗
About anemll
www.anemll.com https://github.com/anemll https://huggingface.co/anemll https://github.com/anemll
Recent public activity
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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
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Comment #47498363
17B includes 10 expert plus one shared. So actual size of the expert is much smaller
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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...
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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 …
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Comment #47493458
SSD streaming to compute units is new. M4 max can do 15 t/s with its 15GB/s drives
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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…
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Comment #47492835
multiple NAND, and apple already used it in Mac Studio. Plus better cooling
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Comment #47492824
both, tbh
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Comment #47492792
Probably 2x speed for Mac Studio this year if they do double NAND ( or quad?)
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Comment #47490071
[flagged]
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iPhone 17 Pro Demonstrated Running a 400B LLM
https://xcancel.com/anemll/status/2035901335984611412
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Comment #46251822
Tensor Parallel test with RDMA last week https://x.com/anemll/status/1996349871260107102 Note fast sync workaround
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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 …
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Comment #45244563
It’s also supported in Apple Neural Engine https://github.com/Anemll/Anemll
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Comment #43917545
We can ran 2000 or 4000 context with ANE
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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
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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…
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Comment #43886877
Yes for GPU, however ANE only supports FP16 plus integers. M4/A17 added accelerated int8 that is twice faster than FP16
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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.
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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…