Self-Supervised Image Representation Learning using Masked Autoencoders (MAE)

2026-03-10 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

A user successfully transformed a 16GB Mac Mini into an AI powerhouse capable of running 70B parameter models locally, bypassing cloud services and API keys. This was achieved by utilizing LM Studio Link, a feature that enables distributed inference across multiple local machines. The setup involved connecting two Macs via an encrypted tunnel, allowing the Mac Mini to offload computational tasks to a more powerful Mac Studio. This method effectively overcomes the memory and processing limitations of a single consumer-grade machine, demonstrating a cost-effective approach to running large language models (LLMs) without external dependencies.

Key takeaway

For AI enthusiasts or developers with multiple Apple Silicon Macs, LM Studio Link offers a compelling way to run large language models locally without cloud expenses. You can combine the resources of your machines, such as a Mac Mini and a Mac Studio, to handle models that would otherwise exceed a single device's capacity. This approach provides greater control and privacy for your AI workloads.

Key insights

LM Studio Link enables distributed LLM inference across local machines, overcoming individual hardware limitations.

Principles

• Distributed inference scales local AI capabilities.
• Offloading computation enhances resource-constrained devices.

Method

Connect two Macs via an encrypted tunnel, then use LM Studio Link to distribute LLM inference tasks, allowing a less powerful machine to utilize resources from a stronger one.

In practice

• Run 70B models on 16GB Mac Mini.
• Avoid cloud costs for LLM inference.

Topics

• LM Studio Link
• Local AI Inference
• Large Language Models
• Edge AI
• Distributed Inference

Best for: Machine Learning Engineer, AI Student, Software Engineer

Related on AIssential

• Fine-tuning a Large Language Model (LLM) usually feels like a battle against CUDA out-of-memory errors and broken environments. Unsloth AI Releases Studio: A Local No-Code Interface For High-Performance LLM Fine-Tuning With 70% Less VRAM Usage..... — Unsloth AI has released Studio, an open-source, local no-code Web UI designed to simplify and accelerate high-performance Large Language Model (LLM) fine-tuning.
• Local AI Agents on macOS: Building an Ollama Home Lab — Local AI inference on consumer hardware offers privacy, cost savings, and practical automation capabilities.
• Optimizing LLM latency — MLC, CTranslate2, and vLLM offer significant latency improvements for LLM inference, with varying trade-offs.
• Run Claude Code Locally on Apple Silicon Using LM Studio and LiteLLM (Zero Cost) — Bridge Anthropic's API expectations with local OpenAI-compatible LLM runtimes for cost-free, offline agentic coding.
• Welcome to Open Source AI: Run Your Own Models Locally — Running open-weight models locally offers privacy, cost savings, and performance comparable to cloud APIs through quantization and optimized inference engines.
• Build a Powerful AI Research Pipeline with LM Studio and NotebookLM — Combining local LLMs with source-grounded knowledge tools enhances research and content creation workflows.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.