metal-cpp VS cortex

My understanding is the proliferation of “XYZ-cpp” AI frameworks is due to the c++ support in Apple’s gpu library ‘Metal’, and the popularity of apple silicon for inference (and there are a few technical reasons for this): https://developer.apple.com/metal/cpp/

This is basically also what the "official" C++ API for Metal does (https://developer.apple.com/metal/cpp/), it's an automatically generated bindings wrapper which calls into ObjC runtime functions.

I also dabbled a bit with this idea by parsing clang AST-dumps of macOS system headers:

https://github.com/floooh/objc-ast-experiments

Unfortunately this is very brittle, and also broke on ARM CPUs, I guess the shim code needs some ABI adjustments (famously, objc_msgSend has multiple "ABI shapes": https://www.mikeash.com/pyblog/objc_msgsends-new-prototype.h...).

There's official C++-interface: https://developer.apple.com/metal/cpp/

It doesn't matter if the project driven by Microsoft or not, the cat (of automatically generated language bindings) is out of the bag. E.g. Zig is using the same approach without being an official MS project: https://github.com/marlersoft/zigwin32, and Apple has an automatically generated C++ API for Metal (https://developer.apple.com/metal/cpp/).

In the future, the question won't be "what language do I need to learn to code on this platform", but instead "are there language bindings for my favourite language".

this? https://developer.apple.com/metal/cpp/

Doesn't it just use objc/runtime.h and if anything is missing you can just add your custom api calls?

Metal has C++ bindings (which cover a full app lifecycle so you don’t have to touch Objective-C/Swift at all) but they’re based on the Objective-C memory model. There are some helper structs mimicking shared pointers, but you’ll still need to understand the basics of how an autorelease pool is used to avoid memory leaks and/or bad access crashes.

On https://developer.apple.com/metal/cpp/ check Foundation folder and all those nice Object::sendMessage().

Jan incorporates a lightweight, built-in inference server called Nitro. Nitro supports both llama.cpp and NVIDIA's TensorRT-LLM engines. This means many open LLMs in the GGUF format are supported. Jan's Model Hub is designed for easy installation of pre-configured models but it also allows you to install virtually any model from Hugging Face or even your own.

I'd like to see a comparison to nitro https://github.com/janhq/nitro which has been fantastic for running a local LLM.

Look... I appreciate a cool project, but this is probably not a good idea.

> Built on top of the cutting-edge inference library llama.cpp, modified to be production ready.

It's not. It's literally just llama.cpp -> https://github.com/janhq/nitro/blob/main/.gitmodules

Llama.cpp makes no pretense at being a robust safe network ready library; it's a high performance library.

You've made no changes to llama.cpp here; you're just calling the llama.cpp API directly from your drogon app.

Hm.

...

Look... that's interesting, but, honestly, I know there's this wave of "C++ is back!" stuff going on, but building network applications in C++ is very tricky to do right, and while this is cool, I'm not sure 'llama.cpp is in c++ because it needs to be fast' is a good reason to go 'so lets build a network server in c++ too!'.

I mean, I guess you could argue that since llama.cpp is a C++ application, it's fair for them to offer their own server example with an openai compatible API (which you can read about here: https://github.com/ggerganov/llama.cpp/issues/4216, https://github.com/ggerganov/llama.cpp/blob/master/examples/...).

...but a production ready application?

I wrote a rust binding to llama.cpp and my conclusion was that llama.cpp is pretty bleeding edge software, and bluntly, you should process isolate it from anything you really care about, if you want to avoid undefined behavior after long running inference sequences; because it updates very often, and often breaks. Those breaks are usually UB. It does not have a 'stable' version.

Further more, when you run large models and run out of memory, C++ applications are notoriously unreliable in their 'handle OOM' behaviour.

Soo.... I know there's something fun here, but really... unless you had a really really compelling reason to need to write your server software in c++ (and I see no compelling reason here), I'm curious why you would?

It seems enormously risky.

The quality of this code is 'fun', not 'production ready'.