I got tired of Apple's configurator, so I built one for local AI
Summary
作者因不满 Apple 官方配置器,开发了 modelfit.io——一个专为购买 Mac 运行本地 LLM 的用户设计的配置工具,可实时显示所选 Mac 配置能运行哪些模型及预计推理速度。
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@sitinme: There's a pretty interesting open-source project called Cider, specifically designed to accelerate local AI inference on Macs with Apple Silicon chips. Many people buy a Mac mini or MacBook Pro and want to run models locally, but often encounter issues like insufficient speed and high memory usage. Actually...
Cider is an open-source project designed for Apple Silicon Macs, accelerating local AI inference by fully leveraging the computing power of M-series chips. It is compatible with the MLX ecosystem, supports models like Qwen and Llama, and is easy to install.
@ai_suxiaole: https://x.com/ai_suxiaole/status/2101298139034792256
本文提供了2026年Mac电脑配置指南,专注于如何选择硬件以本地运行大型AI模型,包括内存、带宽和芯片的考量。
@cevenif: For those running local LLMs on Macs, here's a tool worth watching — Rapid-MLX. It delivers 2-4x faster inference on M-series chips than Ollama, thanks to being built directly on Apple's MLX framework for more thorough utilization of the chip architecture. Key highlights: KV cache pruning plus…
Rapid-MLX is a local LLM inference tool optimized for Apple M-series chips. Built on the MLX framework, it achieves 2 to 4 times faster inference than Ollama, supports multiple models, tool calling, and an OpenAI API-compatible interface.
I built an app to efficiently run decision models on the Apple Neural Engine with Shortcuts actions for OS automations
Tinkerbox 是一个可将 LiquidAI d1-3B 等决策模型高效运行在 Apple Neural Engine 上的应用,支持通过 macOS Shortcuts 触发器实现无代码自动化,延迟低至约 40ms,并计划集成 vLLM/Qwen3.5 系列模型。
My local model setup on an M4 Pro Mac Mini
The author describes their local AI model setup on an M4 Pro Mac mini, using models like Qwen and Gemma with tools such as oMLX and Tailscale to achieve data privacy, cost predictability, and offline capability.