In a landscape dominated by NVIDIA GPUs for AI workloads, Apple Silicon chips are carving out a niche as energy-efficient alternatives for running large language models locally. As developers explore the capabilities of MLX, Apple's machine learning framework optimized for their custom silicon, users are reporting impressive performance metrics that highlight the potential of these systems for AI applications.
MLX Framework Gaining Traction
MLX, Apple's machine learning framework specifically designed for Apple Silicon, has been steadily gaining attention in the developer community despite being just over a year old. Similar to NumPy and PyTorch but exclusively for Apple Silicon, MLX provides the foundation for running various AI models locally on Mac devices. The framework enables users to run LLMs (Large Language Models), vision models, and increasingly audio models without requiring expensive dedicated GPU hardware. Community members have noted that the ecosystem activity around MLX is impressive, with tools like mlx-lm emerging as llama.cpp equivalents built specifically for Apple's architecture.
Performance Metrics Show Promise
Performance reports from community members highlight the efficiency of Apple Silicon for running LLMs. A user running a 4-bit quantized DeepSeek-R1-Distill-Llama-70B on a MacBook Pro M4 Max reported achieving 10.2 tokens per second when plugged in and 4.2 tokens per second on battery power. For the smaller Gemma-3-27B-IT-QAT model, the same system achieved 26.37 tokens per second on power and 9.7 tokens per second in battery-saving mode. These metrics demonstrate that modern Macs can run substantial AI models with reasonable performance, making previously server-bound capabilities accessible on consumer hardware.
Performance Metrics on Apple Silicon
| Model | Device | Power Mode | Performance |
|---|---|---|---|
| DeepSeek-R1-Distill-Llama-70B (4-bit) | MacBook Pro M4 Max | Plugged in | 10.2 tok/sec |
| DeepSeek-R1-Distill-Llama-70B (4-bit) | MacBook Pro M4 Max | Battery/Low Power | 4.2 tok/sec |
| Gemma-3-27B-IT-QAT (4-bit) | MacBook Pro M4 Max | Plugged in | 26.37 tok/sec |
| Gemma-3-27B-IT-QAT (4-bit) | MacBook Pro M4 Max | Battery/Low Power | 9.7 tok/sec |
Energy Efficiency Comparison
| Hardware | OpenCL Benchmark Score | Power Consumption |
|---|---|---|
| NVIDIA GeForce RTX 5090 | 376,224 | 400-550W (GPU) + 250-500W (system) |
| Apple M3 Ultra | 131,247 | ~200W (total system) |
Energy Efficiency Comparison
When comparing energy efficiency between Apple Silicon and NVIDIA GPUs, community discussions suggest Apple may have an advantage in terms of performance per watt. While NVIDIA's high-end cards like the RTX 5090 achieve higher raw performance (scoring 376,224 in OpenCL benchmarks compared to the M3 Ultra's 131,247), they consume significantly more power—approximately 400-550W for the GPU alone plus additional system power requirements. In contrast, the M3 Ultra operates at around 200W total system power, potentially making it more energy-efficient for certain AI workloads despite lower absolute performance.
User Experience Challenges
Despite the performance benefits, Python dependency management remains a significant pain point for many users attempting to run MLX-based applications. Multiple commenters described frustrating experiences with Python environment setup, highlighting a common barrier to entry for non-Python developers who simply want to run applications that happen to be written in Python. One user's experience improved by specifying Python version 3.12 with the command parameter -p 3.12, suggesting that MLX may have binary wheels available only for specific Python versions.
Python is in a category of things you can't just use without being an expert in the minutiae. This is unfortunate because there are a lot of people who are not Python developers who would like to run programs which happen to be written in Python.
Practical Applications
Users report successfully employing various models through MLX for diverse tasks. Popular models include Mistral Small 3.1 (requiring about 20GB of RAM), Gemma3:12B for general tasks like story generation and light coding, Qwen2.5-coder:32B for programming assistance, and the surprisingly capable tiny Qwen2.5:0.5B model. The tiny-llm tutorial project highlighted in the original article aims to help developers understand the techniques behind efficiently serving LLM models, particularly focusing on Qwen2 models, by building model serving infrastructure from scratch using MLX's array/matrix APIs.
As the capabilities of consumer hardware continue to improve and frameworks like MLX mature, we're witnessing a democratization of AI technology that was previously confined to specialized data centers. While challenges remain, particularly around software dependencies and development workflows, Apple Silicon is emerging as a compelling platform for AI enthusiasts and professionals looking to run sophisticated models locally with reasonable performance and excellent energy efficiency.
Reference: tiny-llm - LLM Serving in a Week