MLX vs PyTorch for Apple Silicon: Which Framework to Use

The fundamental difference: memory architecture

The difference between MLX and PyTorch on Apple Silicon isn't syntax — it's memory. PyTorch was designed for discrete GPUs with separate VRAM. Even with the Metal Performance Shaders (MPS) backend added for Apple Silicon, PyTorch still thinks in terms of CPU tensors and GPU tensors with explicit data movement between them.

MLX was built from the ground up for Apple Silicon's unified memory architecture, where CPU and GPU share the same physical RAM pool. There are no copies. A tensor created on CPU is the same object accessed by the GPU — the hardware handles coherency. For large models on an M2 Max with 96GB of unified memory, this enables running 70B parameter models that no discrete GPU can fit in its VRAM.

According to Apple's ML research team, this architecture eliminates the memory bandwidth bottleneck that's the primary constraint for LLM inference — data movement between CPU and GPU memory accounts for a significant portion of cloud GPU inference latency.

MLX: strengths and limitations

What MLX does well

• —LLM inference and fine-tuning. MLX's mlx-lm package runs Llama, Mistral, Phi, and Gemma models with LoRA fine-tuning out of the box. An M2 Max can fine-tune a 7B parameter model in hours, not days.
• —Lazy evaluation. MLX computations are lazy by default — the graph is built but not executed until needed. On Apple Silicon, this allows the framework to optimize the full computation graph before dispatching, reducing redundant operations.
• —Memory efficiency. Unified memory means MLX can use all of the Mac's RAM for model weights. A 64GB M3 Max can load models that require 64GB+ of VRAM on a discrete GPU system — simply not possible on even the highest-end NVIDIA consumer cards.
• —Swift bindings. MLX has official Swift bindings, enabling direct integration in macOS and iPadOS apps for inference — without the Python runtime overhead. Not commonly used yet, but a credible path for shipping MLX models on Apple platforms.

MLX limitations

• —Smaller ecosystem. MLX doesn't have PyTorch's breadth of pre-built models, training utilities, and integrations. Hugging Face Transformers has an MLX backend, but coverage is narrower.
• —Mac-only. MLX is a Mac/Apple Silicon framework. Any training pipeline using MLX is tied to Apple hardware, which affects team and CI/CD flexibility.
• —Fewer training utilities. Data loaders, learning rate schedulers, and distributed training are less mature than PyTorch's ecosystem. Fine-tuning established architectures is straightforward; building novel training pipelines takes more work.

PyTorch MPS: strengths and limitations

What PyTorch MPS does well

• —Ecosystem compatibility. Any PyTorch code that runs on CUDA can be redirected to MPS with device = torch.device("mps"). Most Hugging Face models, diffusion pipelines, and research code “just work” on MPS.
• —Cross-platform codebases. Teams working across Mac and Linux/cloud can write one training loop that runs on CUDA servers and MPS Macs without framework-level rewrites.
• —Ops coverage. PyTorch's MPS backend has significantly expanded op coverage since its 2022 introduction. For most standard ResNets, transformers, and diffusion models, MPS coverage is complete.

PyTorch MPS limitations

• —Memory architecture mismatch. PyTorch's MPS backend doesn't fully exploit unified memory. The framework still copies tensors between CPU and GPU for unsupported ops and during graph transitions. On large models, this causes memory pressure that MLX avoids.
• —LLM inference is slower. For running large language models locally on Mac, PyTorch MPS is consistently slower than MLX for the transformer attention pattern — typically 1.5x–2x slower on direct comparisons.

Performance comparison

Benchmarks on M3 Max (16-core GPU, 128GB unified memory), MLX 0.18, PyTorch 2.3 with MPS. LLM token throughput measured at batch size 1 with 4-bit quantization. Results are indicative — your workload will vary.

Decision matrix: which framework to use

The Core ML pipeline: same destination, different path

Whether you train with MLX or PyTorch, the path to iOS deployment is the same: export weights to a portable format, convert to Core ML using coremltools, and bundle the .mlpackage in your Xcode project. The framework choice affects training speed, not deployment architecture.

For LLMs, the deployment path is different — the Foundation Models API handles the on-device LLM natively on iOS 18.1+. You don't ship a custom LLM in your app bundle; Apple's 3B model is available system-wide. MLX is for Mac research workflows and custom fine-tuned models you want to run on Mac — not for shipping LLMs in iOS apps.

Optimizing Core ML models for production →

Common questions