How to Train LLMs on Apple Silicon with MLX-LM-LoRA: A Step-by-Step Guide
14 hours ago 高效码农Deep Dive into MLX-LM-LoRA: Training Large Language Models on Apple Silicon Introduction In the rapidly evolving landscape of artificial intelligence, training Large Language Models (LLMs) has become a focal point for both research and industry. However, the high computational costs and resource-intensive nature of LLM training often pose significant barriers. Enter MLX-LM-LoRA, a groundbreaking solution that enables local training of LLMs on Apple Silicon devices. This comprehensive guide explores the technical principles, real-world applications, and step-by-step implementation of MLX-LM-LoRA, tailored to meet the needs of developers, researchers, and enthusiasts alike. Understanding the Core Technology: MLX and LoRA 2.1 The Foundations …
Unlocking 3x Faster LLM Inference on MacBooks: The KVSplit Quantization Breakthrough
1 days ago 高效码农Efficient LLM Inference on Apple Silicon: The KVSplit Breakthrough Introduction: Redefining Memory Constraints with Smart Quantization KV Cache Memory Comparison Running large language models (LLMs) on consumer MacBooks has long faced two critical challenges: memory limitations for long contexts and sluggish inference speeds. Traditional solutions forced trade-offs between precision and performance – until KVSplit introduced differentiated key-value quantization. This groundbreaking approach achieves: • 72% memory reduction • 3x longer context handling • 8% faster inference • <1% quality loss This deep dive explores the technical implementation, empirical results, and practical applications of this paradigm-shifting technology. Core Innovation: Why Treat Keys …
Unlocking 128K Context AI Models on Apple Silicon Macs: A Developer’s Guide
20 days ago 高效码农Ultimate Guide to Running 128K Context AI Models on Apple Silicon Macs Introduction: Unlocking Long-Context AI Potential Modern AI models like Gemma-3 27B now support 128K-token contexts—enough to process entire books or codebases in one session. This guide walks through hardware requirements, optimized configurations, and real-world performance benchmarks for Apple Silicon users. Hardware Requirements & Performance Benchmarks Memory Specifications Mac Configuration Practical Context Limit 64GB RAM 8K-16K tokens 128GB RAM Up to 32K tokens 192GB+ RAM (M2 Ultra/M3 Ultra) Full 128K support Empirical RAM usage for Gemma-3 27B: 8K context: ~48GB 32K context: ~68GB 128K context: ~124GB Processing Speed Insights …