---
title: "Apple Silicon"
description: "2 posts about Apple Silicon, summarised, each linking to the original."
---

# Apple Silicon
> 2 posts about Apple Silicon, summarised, each linking to the original.

## Articles

### [Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio](https://yomu.fyi/post/local-and-distributed-large-llm-inference-with-mlx-and-rdma-on-mac-stu.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Oct 15, 2026
- Type: Benchmark

MLX enables running large language models locally on Apple silicon by exploiting the unified memory architecture of Mac Studio. The latest MLX release supports distributed inference across multiple Macs using RDMA over Thunderbolt, with pipeline or tensor parallelization schemes, and uses the jaccl communication collective. Benchmarks compare M3 Ultra and M5 Ultra across three models: Qwen 3.8 (27B) and Mistral Medium 3.5 (128B) fit on a single 512GB Mac, while the newly released Muse Spark Open (816B) requires parallelization across up to four machines at its 4-bit NVFP4 quantization. The M5 Ultra’s GPU Neural Accelerator delivers up to 4.6 times faster time-to-first-token than the M3 Ultra, while its 1.2 TB/s memory bandwidth improves generation throughput by 1.6 times for dense models. On four M5 Ultra Mac Studios with tensor parallelism, Muse Spark Open achieves over 36 tokens per second.


### [Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices](https://yomu.fyi/post/minions-a-dynamic-and-distributed-virtual-gpu-cloud-built-on-idle-appl.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Oct 12, 2026
- Type: Problem & solution

Minions is a dynamic, distributed virtual GPU cloud that aggregates idle Apple Silicon GPUs across macOS devices to run large-scale machine learning inference. The work responds to limits in centralized GPU infrastructure, including high cost, limited elasticity, and long wait times when resources are constrained. Demand for GPU-accelerated machine learning inference keeps rising, while modern Apple Silicon macOS devices carry powerful GPUs that stay largely idle during normal usage. Minions offers flexible GPU inference task execution and provides fault tolerance and efficient resource utilization, letting teams use existing hardware in place of additional GPU procurement. The authors report that idle macOS devices can become a practical distributed GPU inference platform in environments where centralized GPU resources are constrained.