What is MLX? A Beginner's Guide To Apple’s Machine Learning

Are you tired of compatibility issues when running machine learning on your Mac? MLX, a powerful, native machine learning framework designed specifically for macOS and Apple Silicon, is changing how Mac users approach machine learning. This guide unpacks everything beginners need to know about this exciting framework.

From its innovative unified memory model to its impressive speed on Apple Silicon chips, we'll walk through why MLX matters and how you can start using it today. Keep reading to see why Mac is now becoming a serious option for machine learning work and how you can join this growing community.

What is MLX?

MLX is an open-source machine learning framework developed by Apple specifically for Mac computers. Released in December 2023, MLX is designed to leverage Apple's hardware to its fullest potential, particularly the powerful Apple Silicon chips (M1, M2, M3, M4 series). Think of MLX as Apple's answer to frameworks like PyTorch and TensorFlow, but built from the ground up for Mac users.

Why MLX Matters for Mac Users?

For years, serious machine learning work meant using Linux or dealing with compatibility issues on a Mac. MLX changes that by offering a native, high-performance ML framework that feels right at home on macOS. It's designed to make your Mac not just capable of running ML workloads, but exceptionally good at it.

6 Key Features of MLX

1. Unified Memory Model

What it means: MLX uses the same memory space for both CPU and GPU operations.

Why it matters: No more tedious copying of data between CPU and GPU memory! When your model needs to switch between using the CPU and GPU, there's zero data transfer overhead.

Example: In traditional frameworks, moving a large dataset from CPU to GPU might look like:

# Traditional approach
x_cpu = load_large_dataset()  # Data on CPU
x_gpu = x_cpu.to('gpu')       # Expensive copy operation

With MLX, it's simply:

x = load_large_dataset()  # Data is accessible by both CPU and GPU

2. Lazy Computation

What it means: MLX doesn't execute operations immediately; it builds a computation graph first and runs it only when needed.

Why it matters: This allows MLX to optimize the entire sequence of operations before running anything, eliminating unnecessary steps and finding the most efficient execution path.

Example:

# These operations aren't computed yet
a = mlx.ones((1000, 1000))
b = mlx.ones((1000, 1000))
c = a + b
d = c * 2

# Computation happens only when you evaluate d
result = d.item()

3. Array-based API (NumPy-like)

What it means: If you know NumPy, you'll feel right at home with MLX.

Why it matters: Gentle learning curve for Python users already familiar with data science tools.

Example:

import mlx.core as mx

# Create arrays
a = mx.array([1, 2, 3, 4])
b = mx.array([5, 6, 7, 8])

# Familiar operations
c = mx.dot(a, b)  # Dot product
d = mx.sum(a)     # Sum all elements

4. Dynamic Graph Construction

What it means: You can change your model's structure on the fly during execution.

Why it matters: Great flexibility for research and experimentation without performance penalties.

Example:

def conditional_model(x, use_feature=True):
    if use_feature:
        return complex_path(x)
    else:
        return simple_path(x)
    
# The computation graph adapts based on the condition

5. Composable Function Transformations

What it means: MLX allows you to transform functions with decorators that add behavior like automatic differentiation, vectorization, and just-in-time compilation.

Why it matters: You can easily add powerful capabilities to your functions without rewriting them.

Example:

import mlx.core as mx

# Add automatic differentiation to any function
@mx.grad
def squared_loss(params, x, y):
    y_pred = model(params, x)
    return mx.mean((y_pred - y) ** 2)

# Get the gradient function automatically
gradient_fn = squared_loss

6. Multi-device Support

What it means: MLX can utilize CPU, GPU, and Neural Engine in Apple Silicon chips.

Why it matters: Your ML workloads can leverage all the computational power available in your Mac.

Why is MLX Fast?

MLX's speed comes from three main ingredients:

1. Hardware-specific optimizations: MLX is written specifically for Apple Silicon, taking advantage of its unique architecture.
2. Optimized primitive operations: The core operations are implemented in high-performance C++ with processor-specific optimizations.
3. Intelligent compilation: MLX uses compilation techniques to transform high-level Python code into highly optimized machine code.

The combination of lazy evaluation and unified memory allows MLX to find global optimizations that other frameworks might miss. It's like having a smart assistant that rearranges your workload to eliminate wasteful steps.

What are the Limitations of MLX?

Despite its strengths, MLX has some limitations to be aware of:

1. Apple exclusivity: MLX only works on Apple hardware, making code portability a challenge.
2. Ecosystem maturity: As a newer framework, MLX has fewer pre-built models, tutorials, and community solutions compared to PyTorch or TensorFlow.
3. Limited deployment options: Production deployment options are currently more limited than established frameworks.
4. Library support: Not all popular ML libraries have MLX equivalents or integrations yet.

Getting Started with MLX

Installation

Install ffmpeg as it is a dependency for mlx-whisper to handle audio.

MLX is easy to install using pip:

brew install ffmpeg 

pip install mlx-whisper

Whisper Example for Beginners

Input audio 

import mlx_whisper
text = mlx_whisper.transcribe("<path_to_your_audio>")["text"]
print(text)

Output:

improving your speaking skills in English. When you learn a language and phrases, it's a lot easier to speak it. Write down all the phrases into your phone, have them in notes, and use them when you speak. When you pause the movie or when you pause a video, try to paraphrase it. Try to use some different words. Just imagine that you're the character and try to pronounce that phrase in the same manner, replacing some words. The more you surround yourself with English speech every single day, the more you start thinking in English. That's just the way it works.

The default model is whisper-tiny. You can specify the models available in Whisper MLX Community using the syntax below.

result = mlx_whisper.transcribe("<path_to_your_audio>", path_or_hf_repo="
mlx-community/whisper-large-v3-mlx")

Check GPU Usage on live while running the model using the below command in another terminal. You can watch the GPU idle residency go down, and GPU Power increases.

sudo powermetrics | grep -i gpu

Output:

Code Helper (GPU)                  3308   27.33     62.61  0.99    0.40               193.48  0.79              
Google Chrome Helper (GPU)         738    1.05      76.15  0.00    0.00               0.99    0.00              
Slack Helper (GPU)                 725    0.04      47.34  0.00    0.00               0.60    0.00              
GPU Power: 5878 mW
Combined Power (CPU + GPU + ANE): 9120 mW
**** GPU usage ****
GPU HW active frequency: 1444 MHz
GPU HW active residency:  51.59% (338 MHz: 5.5% 618 MHz:   0% 796 MHz:   0% 924 MHz: .08% 952 MHz:   0% 1056 MHz: .09% 1062 MHz:   0% 1182 MHz:   0% 1182 MHz:   0% 1312 MHz:   0% 1242 MHz:   0% 1380 MHz:   0% 1326 MHz:   0% 1470 MHz:   0% 1578 MHz:  46%)
GPU SW requested state: (P1 :  11% P2 :   0% P3 :   0% P4 : .08% P5 : .24% P6 :   0% P7 :   0% P8 :   0% P9 :   0% P10 :  89% P11 :   0% P12 :   0% P13 :   0% P14 :   0% P15 :   0%)
GPU SW state: (SW_P1 : 5.5% SW_P2 :   0% SW_P3 :   0% SW_P4 : .08% SW_P5 : .09% SW_P6 :   0% SW_P7 :   0% SW_P8 :   0% SW_P9 :   0% SW_P10 :  46% SW_P11 :   0% SW_P12 :   0% SW_P13 :   0% SW_P14 :   0% SW_P15 :   0%)
GPU idle residency:  48.41%
GPU Power: 5889 mW
Second underflow occured.

Learn More About MLX

To dive deeper into MLX and explore all its capabilities, check out these official resources:

• Official Documentation: MLX Documentation
• GitHub Repository: github.com/ml-explore/mlx
• Examples Repository: github.com/ml-explore/mlx-examples

Conclusion

MLX represents a significant step forward for machine learning on Mac. By building a framework specifically optimized for Apple Silicon, Apple has created a tool that makes Mac a serious contender for ML development.

While MLX is still maturing, it already offers compelling advantages for Mac users who want to run machine learning workloads without switching platforms. The combination of performance, Python-friendly API, and tight integration with Apple hardware makes it an exciting option for both beginners and experienced ML practitioners.

As the ecosystem grows and more pre-built models become available, MLX is likely to become an increasingly important tool in the machine learning landscape, especially for the growing community of developers using Mac as their primary platform.