SwiftLM: Pure-Swift Apple Silicon LLM inference server—no Python, runs big models on low-RAM Macs
Summary
SwiftLM is a Swift-native LLM inference server for Apple Silicon that runs large models without Python, using SSD streaming to load MoE weights and enabling 122B models on 64 GB Macs.
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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.
Optimizing On-Device Inference for Apple Silicon (20 minute read)
Apple's Lily engine optimizes on-device LLM inference for Apple silicon by leveraging unified memory and hardware, outperforming MLX-LM, and is tuned for the Qwen3.6-35B-A3B model's architecture.
New MLX LM Server From Apple
Apple's MLX team introduces MLX LM Server, a tool for running AI agent workflows fully locally on Mac, supporting continuous batching, distributed inference, and M5 neural acceleration, with no need for cloud or API keys.
@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.
@0xSero: Locally Part 1 - Apple Silicon Macs give you large pools of memory to run big models, but the token generation speed wi…
Apple Silicon Macs offer large memory pools for running big models but with slower token generation, performing best with large MoEs that have low active parameters.