Run LLMs on Apple Neural Engine (ANE)
131 comments
·May 3, 2025behnamoh
kamranjon
Whisper.cpp has a coreml option which gives 3x speed up over cpu only according to the docs: https://github.com/ggml-org/whisper.cpp?tab=readme-ov-file#c...
zozbot234
Some outdated information about bare-metal use of the ANE is available from the Whisper.cpp pull req: https://github.com/ggml-org/whisper.cpp/pull/1021 Even more outdated information at: https://github.com/eiln/ane/tree/33a61249d773f8f50c02ab0b9fe... In short, the early (M1/M2) versions of ANE are unlikely to be useful for modern LLM inference due to their seemingly exclusive focus on statically scheduled FP16 and INT8 MADDs.
More extensive information at https://github.com/tinygrad/tinygrad/tree/master/extra/accel... (from the Tinygrad folks, note that this is also similarly outdated) seems to basically confirm the above.
(The jury is still out for M3/M4 which currently have no Asahi support - thus, no current prospects for driving the ANE bare-metal. Note however that the M3/Pro/Max ANE reported performance numbers are quite close to the M2 version, so there may not be a real improvement there either. M3 Ultra and especially the M4 series may be a different story.)
kamranjon
I wouldn't say that they aren't useful for inference (there are pretty clear performance improvements even from the asahi effort you linked) - it's just that you have to convert the model ahead of time to be compatible with the ANE which is explained in the readme docs for whisper.cpp that I linked above.
I would say though that this likely excludes them from being useful for training purposes.
echelon
> coreml option which gives 3x speed up over cpu
Which is still painfully slow. CoreML is not a real ML platform.
jorvi
.. who is running LLMs on CPU instead of GPU or TPU/NPU
kamranjon
Actually that's a really good question, I hadn't considered that the comparison here is just CPU vs using Metal (CPU+GPU).
To answer the question though - I think this would be used for cases where you are building an app that wants to utilize a small AI model while at the same time having the GPU free to do graphics related things, which I'm guessing is why Apple stuck these into their hardware in the first place.
Here is an interesting comparison between the two from a whisper.cpp thread - ignoring startup times - the CPU+ANE seems about on par with CPU+GPU: https://github.com/ggml-org/whisper.cpp/pull/566#issuecommen...
fc417fc802
Depends on the size of the model and how much VRAM you have (and how long you're willing to wait).
yjftsjthsd-h
Not all of us own GPUs worth using. Now, among people using macs... Maybe if you had a hardware failure?
thot_experiment
[flagged]
ks2048
One update from Apple Research since that one was "Deploying Attention-Based Vision Transformers to Apple Neural Engine" (but not clear the relationship. It doesn't build on ane_transformers, but maybe a sister project for vision?)
blog: https://machinelearning.apple.com/research/vision-transforme...
smpanaro
Not a public follow-up but the iOS 17 speech-to-text model has a clever approach to KV caching that works within the ANE’s constraints (fixed size inputs).
I wrote about it here[0] but the gist is you can have a fixed size cache and slide it in chunks with each inference. Not as efficient as a cache that grows by one each time of course.
[0]: https://stephenpanaro.com/blog/inside-apples-2023-transforme...
kamranjon
Hey I just wanted to say that this is an amazing write up and I'm bookmarking your blog cause there isn't a ton of information out there about this stuff as it related to Apple hardware and you do a really great job of explaining many of the concepts that I'm wasn't already familiar with. Thank you!
noahbp
Onnxruntime supports CoreML, though if my experience with converting an embedding model to CoreML using Apple's CoreML conversion tool is similar to the ORT maintainers', I can see why it would be unmaintained.
It took multiple tries to get the model to convert at all to the mlpackage format, and then a lot of experimenting to get it to run on the ANE instead of the GPU, only to discover that constant reshaping was killing any performance benefit (either you have a fixed multiplication size or don't bother), and even at a fixed size and using the attention mask, its operations were slower than saturating the GPU with large batches.
I discovered an issue where using the newer iOS 18 standard would cause the model conversion to break, and put an issue in on their GitHub, including an example repository for easy replication. I got a response quickly, but almost a year later, the bug is still unfixed.
Even when George Hotz attempted to hack it to use it without Apple's really bad and unmaintained CoreML library, he gave up because it was impossible without breaking some pretty core OS features (certificate signing IIRC).
The ANE/CoreML is just not serious at all about making their hardware usable at all. Even Apple's internal MLX team can't crack that nut.
imtringued
ONNX is horrible for anything that has variable input shapes and that is why nobody uses it for LLMs. It fundamentally is poorly designed for anything that doesn't take a fixed size image.
zozbot234
ANE itself is also limited to fixed computation "shapes" so I'm not sure how much that would matter practically.
1W6MIC49CYX9GAP
Based on the graphs "up to 10 times faster" compares before/after flash attention
ljosifov
This more than anything feels emblematic to me, that Apple executives are brain dead when it comes to software. AI seemingly being a step too far. (in the s/w direction.) While they could at some level grok classical s/w, NN-s are that Terra Incognita where Apple executives can not possibly follow in. It's just too strange and mysterious world for them to be able to effectively decide or execute on anything. Worked (20yrs ago) in an industrial R&D lab of a mostly h/w manufacturer for 3 years. It looked to me that the worlds of h/w and s/w, the mindsets, diverged pretty quickly on all important considerations of "what should happen next".
anon373839
Huh? Apple ships heaps of deep learning-based products/features, and they’ve done so for years. But I’ll agree with you, they’re currently behind in generative AI.
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cowmix
This sorta reminds me of the lie that was pushed when the Snapdragon X laptops were being released last year. Qualcomm implied the NPU would be used for LLMs — and I bought into the BS without looking into it. I still use a Snapdragon laptop as my daily driver (it's fine) but for running models locally, it's still a joke. Despite Qualcomm's claims about running 13B parameter models, software like LM Studio only runs on CPU with NPU support merely "planned for future updates." XDA The NPU isn't even faster than the CPU for LLMs — it's just more power-efficient for small models, not the big ones people actually want to run. Their GPUs aren't much better for this purpose either. The only hope for LLMs is the Vulkan support on the Snapdragon X — which still is half-baked.
wmf
AFAIK Windows 11 does use the NPU to run Phi Silica language models and this is available to any app through some API. The models are quite small as you said though.
eyeris
Agreed. Same with intel’s NPUs. I’ve been testing with my intel core evo 155x. The npu only runs int8 as well. At least, Intel has put in a decent amount of effort into the ecosystem
There are a couple ways to interface — DirectML by MS and Intel’s native api (they provide OpenVINO model conversion to convert normal Python ml models) I’ve tried ONNXRuntime conversions for both backends to little success. Additionally the OpenVINO model conversion seems to break the model if the model small enough.
OpenVINO model server seems pretty polished and has openapi compatible endpoints.
nullpoint420
If you don’t mind me asking, what OS do you use on it?
cowmix
I use Windows 11. Podman/WLS2 works way better than I thought it would. And when Gitbash was finally ported (officially) - that filled other gaps I was missing in my workflow. Windows ARM Python still is lacking all sorts of stuff, but overall I'm pretty productive on it.
I pre/ordered the Snapdragon X Dev kit from Qualcomm - but they ended up delivering a few units -- to only cancel the whole program. The whole thing turned out to be a hot-mess express saga. THAT computer was going to be my Debian rig.
captainregex
AnythingLLM uses NPU
mikaraento
Could you provide a pointer to docs for this? It wasn't obvious from an initial read of their docs.
tough
could find some about it on the 1.7.2 changelog here https://docs.anythingllm.com/changelog/v1.7.2
htk
I always felt that the neural engine was wasted silicon, they could add more gpu cores in that die space and redirect the neural processing api to the gpu as needed. But I'm no expert, so if anyone here has a different opinion I'd love to learn from it.
lucasoshiro
I'm not a ML guy, but when I needed to train a NN I thought that the my Mac's ANE would help. But actually, despite it being way easier to setup tensorflow + metal + M1 on Mac than to setup tensorflow + cuda + nvidia on Linux, the neural engine cores are not used. Not even for classification, which are their main purpose. I wouldn't say they are wasted silicon, but they are way less useful than what we expect
mr_toad
Does Apple care about third party use of the ANE? There are many iOS/iPadOS features that use it.
sroussey
Not really. Apple software uses the neural engine all over the place, but rarely do others. Maybe this will change [1]
There was a guy using it for live video transformations and it almost caused the phones to “melt”. [2]
[1] https://machinelearning.apple.com/research/neural-engine-tra...
brigade
Eyeballing 3rd party annotated die shots [1], it’s about the size of two GPU cores, but achieves 15.8 tflops. Which is more than the reported 14.7 tflops of the 32-core GPU in the binned M4 Max.
[1] https://vengineer.hatenablog.com/entry/2024/10/13/080000
Archit3ch
Not really. That's 15.8 fp16 ops compared to 14.7 fp32 ops (that are actually useful outside AI). It would be interesting to see if you can configure the ANE to recover fp32 precision at lower throughput [1].
brigade
Apple GPUs run fp16 at the same rate as fp32 except on phones, so it is comparable for ML. No one runs inference from fp32 weights.
But the point was about area efficiency
xiphias2
I guess it's a hard choice as it's 5x more energy efficient than GPU because it uses systolic array.
For laptops, 2x GPU cores would make more sense, for phones/tablets, energy efficiency is everything.
1W6MIC49CYX9GAP
You're completely right, if you already have a GPU in a system adding tensor cores to it gives you better performance per area.
GPU + dedicated AI HW is virtually always the wrong approach compared to GPU+ tensor cores
ks2048
At least one link/benchmark I saw said the ANE can be 7x faster than GPU (Metal / MPS),
https://discuss.pytorch.org/t/apple-neural-engine-ane-instea...
It seems intuitive that if they design hardware very specifically for these applications (beyond just fast matmuls on a GPU), they could squeeze out more performance.
astrange
Performance doesn't matter. Nothing is ever about performance.
It's about performance/power ratios.
rz2k
I was trying to figure the same thing out a couple months ago, and didn't find much information.
It looked like even ANEMLL provides limited low level access to specifically direct processing toward the Apple Neural Engine, because Core ML still acts as the orchestrator. Instead, flags during conversion of a PyTorch or TensorFlow model can specify ANE-optimized operations, quantization, and parameters hinting at compute targets or optimization strategies. For example `MLModelConfiguration.computeUnits = .cpuAndNeuralEngine` during conversion would disfavor the GPU cores.
Anyway, I didn't actually experiment with this, but at the time I thought maybe there could be a strategy of creating a speculative execution framework, with a small ANE-compatible model to act as the draft model paired with a larger target model running on GPU cores. The idea being that the ANE's low latency and high efficiency could accelerate results.
However, I would be interested to hear the perspective of people who actually know something about the subject.
bigyabai
If you did that, you'd stumble into the Apple GPU's lack of tensor acceleration hardware. For an Nvidia-like experience you'd have to re-architecture the GPU to subsume the NPU's role, and if that was easy then everyone would have done it by now.
sroussey
M1/M2 shared a GPU design, same with M3/M4. So maybe M5 will have a new design that includes tensor cores in the GPU.
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antirez
The README lacks the most important thing: how many more tokens/sec at the same quantization, compared to llama.cpp / MLX? It is worth to switch default platforms only if there is a major improvement.
SparkyMcUnicorn
I ran R1-8B for both anemll[0] and mlx[1][2] models on an M4 Max.
Prompt: "Tell me a long story about the origins of 42 being the answer."
anemll: 9.3 tok/sec, ~500MB of memory used.
mlx 8bit: 31.33 tok/sec, ~8.5GB of memory used.
mlx bf16: 27.17 tok/sec, ~15.7GB of memory used.
Memory results are from activity monitor across any potentially involved processes, but I feel like I might missing something here...
[0] https://huggingface.co/anemll/anemll-DeepSeekR1-8B-ctx1024_0...
[1] https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Lla...
[2] https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Lla...
antirez
Thank you. Strange. If the memory numbers are accurate, it is so slow because likely layers are loaded from disk before inference of each layer or something like that, otherwise it could not do the inference of such model in 500MB. But if that's what it does, 33% of the speed would be already too fast, likely.
ericboehs
Interesting. Does this mean larger models could be ran on less memory? It looks like it uses 15-20x less memory. Could a 671B DeepSeek R1 be ran in just ~40-50GB of memory? It sounds like it'd be 1/3 as fast though (<1tk/sec).
anemll
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 models due to memory bandwidth rather then ANE capabilities.
SparkyMcUnicorn
M4 Max with 128GB of memory.
srigi
Can you add a recent build of llama.cpp (arm64) to the results pool? I'm really interested in comparing mlx to llama.cpp, but setting up the mlx seems too difficult for me to do by myself.
SparkyMcUnicorn
I ran them again several times to make sure the results were fair. My previous runs also had a different 30B model loaded in the background that I forgot about.
LM Studio is an easy way to use both mlx and llama.cpp
anemll [0]: ~9.3 tok/sec
mlx [1]: ~50 tok/sec
gguf (llama.cpp b5219) [2]: ~41 tok/sec
[0] https://huggingface.co/anemll/anemll-DeepSeekR1-8B-ctx1024_0...
[1] https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Lla...
[2] (8bit) https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-...
loh
Yeah, I looked all over for a comparison and couldn't find anything in the repo, on their social media, etc. I saw some other comments here that said it's supposed to be "15.8 fp16 ops compared to 14.7 fp32 ops" but that isn't really enough to go on. Maybe when I have the time I'll install their TestFlight app and do some comparisons myself.
sunpazed
In my testing, tokens per sec is half the speed of the GPU, however the power usage is 10x less — 2 watts ANE vs 20 watts GPU on my M4 Pro.
simonw
I'm trying to figure out what the secret sauce for this is. It depends on https://github.com/apple/coremltools - is that the key trick or are there other important techniques going on here?
smpanaro
coremltools is the only way to run on ANE, so less of a trick and more of a requirement.
The tricks are more around optimizing for the hardware capabilities/constraints. For instance:
- conv2d is faster than linear (see Apple's post [0]) so you rewrite the model for that (example from the repo [1])
- inputs/outputs are static shapes, so KV cache requires some creativity (I wrote about that here [2])
- compute is float16 (not bfloat16) so occasionally you have to avoid activation overflows
[0]: https://machinelearning.apple.com/research/neural-engine-tra...
[1]: https://github.com/Anemll/Anemll/blob/4bfa0b08183a437e759798...
[2]: https://stephenpanaro.com/blog/kv-cache-for-neural-engine
thadk
Sounds like M2-era onward have bfloat16: https://eclecticlight.co/2024/01/13/how-m1-macs-may-lag-behi...
anemll
Yes for GPU, however ANE only supports FP16 plus integers. M4/A17 added accelerated int8 that is twice faster than FP16
sunpazed
The key benefit is significant lower power usage. Benchmarked llama3.2-1B on my machines; M1 Max (47t/s, ~1.8 watts), M4 Pro (62t/s, ~2.8 watts). The GPU is twice as fast (even faster on the Max), but draws much more power (~20 watts) vs the ANE.
Also the ANE models are limited to 512 tokens of context, so unlikely yet to use these in production.
anemll
We can ran 2000 or 4000 context with ANE
daedrdev
Apple is a competitive choice simply because their unified memory allows you to get enough ram that would take multiple Gpus to have enough space to run larger models.
fmajid
Yes, but their refusal to open up the ANE to third-party models negates that. You can get (or will be able to very soon) a Strix Halo Ryzen AI Max+ 395 able to access 96GB of unified RAM (on a 128GB system) for well under half what you'd pay for an equivalent M4 system from Apple.
wmf
Hobbyists don't use ANE on Apple Silicon and they won't use XDNA on Strix Halo either. In both cases the GPU is faster and can access most of system memory.
$2,000 vs. $3,500 isn't well under half either.
ycomby321
Something to consider for deploying LLMs on the ANE is: https://github.com/pytorch/executorch/tree/main/examples/app...
The model does have some limitations (e.g., need for QAT for 4-bit quantization), lack of a C++ runner to execute the model, but parts of the model are promising.
If interested in further discussion, join the conversation on the ExecuTorch discord channel: https://discord.gg/xHxqsD5b
jvstokes
But coreml utilizes ANE, right? Is there some bottleneck in coreml that requires lower level access?
anemll
Memory bandwidth is the main bottleneck. It got better with M3/M4. ANE is really fast in FLOPS but low in memory bandwidth.
cwoolfe
Is there a performance benefit for inference speed on M-series MacBooks, or is the primary task here simply to get inference working on other platforms (like iOS)? If there is a performance benefit, it would be great to see tokens/s of this vs. Ollama.
SparkyMcUnicorn
See my other comment for results.
mlx is much faster, but anemll appeared to use only 500MB of memory compared to the 8GB mlx used.
kamranjon
I am curious if anyone knows if the neural cores in apple silicon based machines are at all useful in training? I’ve been using the MLX framework but haven’t seen them mentioned anywhere so I’m just wondering if they are only useful for inference? I know whisper.cpp takes advantage of them in the inference context.
Edit: I changed llama.cpp to whisper.cpp - I didn’t realize that llama.cpp doesn’t have a coreml option like whisper.cpp does.
lucasoshiro
Well, the TensorFlow port to metal was written by Apple, and it doens't use ANE. If even they have chosen to use only GPU probably the ANE wouldn't help in training. I also heard that the ANE is way less powerful than Apple Silicon's GPU, but I don't have numbers
486sx33
Maybe a quick side shift - What the heck are apples neural cores good for ? Used for ? Use cases ?
freehorse
In my understanding, they are intended to be utilized by various apps (and Apple intelligence) for performing different machine learning tasks (inference), in a manner that is unobtrusive to the user and the rest of the system. While a GPU would theoretically be more performant, it could potentially lead to excessive energy consumption, temperature rise, fan noise, and other factors that may not be desirable when performing basic tasks like OCR when viewing an image within an application.
Currently, it is for example used through the "Vision Framework" eg for OCR tasks (for instance, when previewing an image in macOS, it performs OCR in the background using the ANE). Additionally, they are utilized by certain apple intelligence features that are executed locally (eg when I asked writing tools to rewrite this comment, I saw a spike in ANE usage).
They can also be used for diffusion image models (through core ml, diffusers has a nice frontend for that) but my understanding is that they are primarily for "light" ML tasks within an application rather than running larger models (though that's also possible, but they are gonna probably run slower than in gpu).
giantrobot
They're used pretty extensively by stuff like the Photos app for various ML tasks. Not all AI is LLMs.
kamranjon
They are definitely good for local inference as evidenced by the pretty amazing performance increase on Apple silicon when used with whisper.cpp, maybe other frameworks that utilize coreml? I think they’re sorta purpose built for doing matrix math.
m3kw9
Even 1gb model is prohibitively big for phones if you want mass adoption.
SparkyMcUnicorn
The 1B model works on iPhones[0].
See my other comments. anemll appears to use less memory.
[0] https://huggingface.co/anemll/anemll-llama-3.2-1B-iOSv2.0
ukuina
We'll just have to await next week's oai-agi1-0.4b-a0.1b-iq1_xs.gguf
I wonder if Apple ever followed up with this: https://github.com/apple/ml-ane-transformers
They claim their ANE-optimized models achieve "up to 10 times faster and 14 times lower peak memory consumption compared to baseline implementations."
AFAIK, neither MLX nor llama.cpp support ANE. Though llama.cpp started exploring this idea [0].
What's weird is that MLX is made by Apple and yet, they can't support ANE given its closed-source API! [1]
[0]: https://github.com/ggml-org/llama.cpp/issues/10453
[1]: https://github.com/ml-explore/mlx/issues/18#issuecomment-184...