Go-CoreML – Go Bindings for Apple's CoreML with Neural Engine Support
Go bindings for CoreML that let you build and run ML models on Apple Silicon from Go, no Python required. You construct models programmatically using CoreML's MIL intermediate language, then run them on the Neural Engine, GPU, or CPU. Also works as a GoMLX backend.
Go-CoreML – Go Bindings for Apple's CoreML with Neural Engine Support launched on January 14, 2026 with 6 votes, #2,272 of 14,584 launches that month and more than 75% of that year's launches.
What it does
Go bindings to Apple's CoreML framework for high-performance machine learning inference on Apple Silicon. - gomlx/go-coreml
Does a similar job
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CLI for working with Apple Core ML modelsJan 2026 · github.com · ▲48A native command-line interface for working with Apple Core ML models on macOS - schappim/coreml-cli
Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series MacJul 2026 · github.com · ▲919Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
We made our own inference engine for Apple Silicon2025 · github.com · ▲186We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
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