Mlx Alternatives

mlx reviews and mentions

• Ollama v0.1.33 with Llama 3, Phi 3, and Qwen 110B
  11 projects | news.ycombinator.com | 28 Apr 2024

  Yes, we are also looking at integrating MLX [1] which is optimized for Apple Silicon and built by an incredible team of individuals, a few of which were behind the original Torch [2] project. There's also TensorRT-LLM [3] by Nvidia optimized for their recent hardware.

  All of this of course acknowledging that llama.cpp is an incredible project with competitive performance and support for almost any platform.

  [1] https://github.com/ml-explore/mlx

  [2] https://en.wikipedia.org/wiki/Torch_(machine_learning)

  [3] https://github.com/NVIDIA/TensorRT-LLM

• Ask HN: What is the current (Apr. 2024) gold standard of running an LLM locally?
  11 projects | news.ycombinator.com | 1 Apr 2024

  If you're able to purchase a separate GPU, the most popular option is to get an NVIDIA RTX3090 or RTX4090.

  Apple Mac M2 or M3's are becoming a viable option because of MLX https://github.com/ml-explore/mlx . If you are getting an M series Mac for LLMs, I'd recommend getting something with 24GB or more of RAM.

• MLX Community Projects
  1 project | news.ycombinator.com | 8 Feb 2024
• FLaNK 15 Jan 2024
  21 projects | dev.to | 15 Jan 2024
• Why the M2 is more advanced that it seemed
  5 projects | news.ycombinator.com | 15 Jan 2024
• I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros
  12 projects | news.ycombinator.com | 7 Jan 2024

  3) Not Enough Benefit (For the Cost... Yet!)

  This is my best understanding based on my own work and research for a local LLM iOS app. Read on for more in-depth justifications of each point!

  -—-

  1) No Neural Engine API

  - There is no developer API to use the Neural Engine programmatically, so CoreML is the only way to be able to use it.

  2) CoreML has challenges modeling LLMs efficiently right now.

  - Its most-optimized use cases seem tailored for image models, as it works best with fixed input lengths[1][2], which are fairly limiting for general language modeling (are all prompts, sentences and paragraphs, the same number of tokens? do you want to pad all your inputs?).

  - CoreML features limited support for the leading approaches for compressing LLMs (quantization, whether weights-only or activation-aware). Falcon-7b-instruct (fp32) in CoreML is 27.7GB [3], Llama-2-chat (fp16) is 13.5GB [4] — neither will fit in memory on any currently shipping iPhone. They'd only barely fit on the newest, highest-end iPad Pros.

  - HuggingFace‘s swift-transformers[5] is a CoreML-focused library under active development to eventually help developers with many of these problems, in addition to an `exporters` cli tool[6] that wraps Apple's `coremltools` for converting PyTorch or other models to CoreML.

  3) Not Enough Benefit (For the Cost... Yet!)

  - ANE & GPU (Metal) have access to the same unified memory. They are both subject to the same restrictions on background execution (you simply can't use them in the background, or your app is killed[7]).

  - So the main benefit from unlocking the ANE would be multitasking: running an ML task in parallel with non-ML tasks that might also require the GPU: e.g. SwiftUI Metal Shaders, background audio processing (shoutout Overcast!), screen recording/sharing, etc. Absolutely worthwhile to achieve, but for the significant work required and the lack of ecosystem currently around CoreML for LLMs specifically, the benefits become less clear.

  - Apple's hot new ML library, MLX, only uses Metal for GPU[8], just like Llama.cpp. More nuanced differences arise on closer inspection related to MLX's focus on unified memory optimizations. So perhaps we can squeeze out some performance from unified memory in Llama.cpp, but CoreML will be the only way to unlock ANE, which is lower priority according to lead maintainer Georgi Gerganov as of late this past summer[9], likely for many of the reasons enumerated above.

  I've learned most of this while working on my own private LLM inference app, cnvrs[10] — would love to hear your feedback or thoughts!

  Britt

  ---

  [1] https://github.com/huggingface/exporters/pull/37

  [2] https://apple.github.io/coremltools/docs-guides/source/flexi...

  [3] https://huggingface.co/tiiuae/falcon-7b-instruct/tree/main/c...

  [4] https://huggingface.co/coreml-projects/Llama-2-7b-chat-corem...

  [5] https://github.com/huggingface/swift-transformers

  [6] https://github.com/huggingface/exporters

  [7] https://developer.apple.com/documentation/metal/gpu_devices_...

  [8] https://github.com/ml-explore/mlx/issues/18

  [9] https://github.com/ggerganov/llama.cpp/issues/1714#issuecomm...

  [10] https://testflight.apple.com/join/ERFxInZg

• Ferret: An End-to-End MLLM by Apple
  5 projects | news.ycombinator.com | 23 Dec 2023

  Maybe MLX is meant to fill this gap?

  https://github.com/ml-explore/mlx

• PowerInfer: Fast Large Language Model Serving with a Consumer-Grade GPU [pdf]
  3 projects | news.ycombinator.com | 19 Dec 2023

  This is basically fork of llama.cpp. I created a PR to see the diff and added my comments on it: https://github.com/ggerganov/llama.cpp/pull/4543

  One thing that caught my interest is this line from their readme:

  > PowerInfer exploits such an insight to design a GPU-CPU hybrid inference engine: hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons are computed on the CPU, thus significantly reducing GPU memory demands and CPU-GPU data transfers.

  Apple's Metal/M3 is perfect for this because CPU and GPU share memory. No need to do any data transfers. Checkout mlx from apple: https://github.com/ml-explore/mlx

• Whisper: Nvidia RTX 4090 vs. M1 Pro with MLX
  10 projects | news.ycombinator.com | 13 Dec 2023

  How does this compare to insanely-fast-whisper though? https://github.com/Vaibhavs10/insanely-fast-whisper

  I think that not using optimizations allows this to be a 1:1 comparison, but if the optimizations are not ported to MLX, then it would still be better to use a 4090.

  Having looked at MLX recently, I think it's definitely going to get traction on Macs - and iOS when Swift bindings are released https://github.com/ml-explore/mlx/issues/15 (although there might be some C++20 compilation issue blocking right now).

• [D] M3 MAX 64GB VS RTX 3080
  1 project | /r/MachineLearning | 8 Dec 2023

  software is already there, check the new ml framework from Apple https://github.com/ml-explore/mlx

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