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Get startedCactus vs MLX: Cross-Platform AI vs Apple Silicon ML Framework
MLX is Apple's open-source ML framework optimized exclusively for Apple Silicon Macs, with a NumPy-like Python API and unified memory architecture. Cactus is a cross-platform hybrid AI engine for mobile, desktop, and edge devices. MLX excels at Mac-based ML workflows; Cactus excels at shipping AI features in production apps across all platforms.
Cactus
Cactus is a hybrid AI inference engine for mobile, desktop, and edge hardware. It runs LLMs, transcription, vision, and embeddings on-device with automatic cloud fallback. Cactus provides native SDKs for Swift, Kotlin, Flutter, React Native, Python, C++, and Rust with sub-120ms latency and NPU acceleration.
MLX
MLX is Apple's open-source machine learning framework built specifically for Apple Silicon. It provides a NumPy-like Python API with unified CPU/GPU memory, supporting both inference and fine-tuning. The MLX ecosystem includes mlx-lm for language models, mlx-whisper for transcription, and mlx-vlm for vision-language models.
Feature comparison
Performance & Latency
MLX leverages Apple Silicon's unified memory architecture to eliminate data copies between CPU and GPU, enabling very fast inference and training on Macs. Cactus achieves sub-120ms latency with zero-copy memory mapping and Apple NPU acceleration. MLX is arguably the best framework for Mac ML workloads. Cactus focuses on production mobile deployment rather than ML experimentation.
Model Support
MLX has a rich ecosystem: mlx-lm supports LLM inference and fine-tuning, mlx-whisper handles transcription, and mlx-vlm covers vision-language models. Cactus supports Gemma, Qwen, LFM2, Whisper, Moonshine, Parakeet, and Nomic Embed. Both cover LLMs, transcription, and vision, but MLX also supports fine-tuning while Cactus adds hybrid cloud routing.
Platform Coverage
This is the decisive difference. MLX runs only on macOS with Apple Silicon. No iOS, no Android, no Linux, no Windows. Cactus runs on iOS, Android, macOS, Linux, watchOS, and tvOS. If you need mobile deployment or any non-Mac platform, MLX simply cannot be used. MLX is a Mac development tool, not a deployment framework.
Pricing & Licensing
Both are MIT licensed by their respective organizations and fully free to use. MLX has no commercial components. Cactus has an optional cloud API. For Mac-only workflows, both are cost-equivalent. MLX requires no additional infrastructure since it runs entirely on Apple Silicon.
Developer Experience
MLX offers a familiar NumPy-like API that ML researchers and data scientists love. It supports both inference and training, making it ideal for experimentation. Cactus is designed for app developers with native SDKs and a unified API across modalities. MLX targets ML practitioners; Cactus targets software engineers building production apps.
Strengths & limitations
Cactus
Strengths
- Hybrid routing automatically falls back to cloud when on-device confidence is low
- Single unified API across LLM, transcription, vision, and embeddings
- Sub-120ms on-device latency with zero-copy memory mapping
- Cross-platform SDKs for Swift, Kotlin, Flutter, React Native, Python, C++, and Rust
- NPU acceleration on Apple devices for significantly faster inference
- Up to 5x cost savings on hybrid inference compared to cloud-only
Limitations
- Newer project compared to established frameworks like TensorFlow Lite
- Qualcomm and MediaTek NPU support still in development
- Cloud fallback requires API key configuration
MLX
Strengths
- Best performance on Apple Silicon with unified memory
- NumPy-like API makes it easy for ML practitioners
- Supports both inference and fine-tuning
- Growing ecosystem with mlx-lm, mlx-whisper, mlx-vlm
Limitations
- Apple Silicon only — no mobile, no Linux, no Windows
- No on-device mobile deployment
- No hybrid cloud routing
- Limited to macOS development workflows
The Verdict
Choose MLX if you are an ML practitioner working exclusively on Apple Silicon Macs for inference, fine-tuning, and research. It is the best ML framework for Mac development workflows. Choose Cactus if you need to deploy AI features in production apps across iOS, Android, or any non-Mac platform. For most production deployment scenarios, Cactus is the practical choice. For Mac ML research, MLX is unbeatable.
Frequently asked questions
Can MLX run on iPhone or iPad?+
No. MLX is macOS-only and does not support iOS or iPadOS. For on-device AI on iPhones and iPads, you need a mobile framework like Cactus, Core ML, or ExecuTorch.
Is MLX good for fine-tuning models?+
Yes. MLX supports both inference and fine-tuning on Apple Silicon, with LoRA and QLoRA support in mlx-lm. Cactus focuses on inference rather than training. MLX is a better choice for local fine-tuning workflows.
Does MLX support Android?+
No. MLX is exclusively for Apple Silicon Macs. It does not support Android, Linux, or Windows. For Android deployment, use Cactus, ExecuTorch, or another cross-platform framework.
Which is better for prototyping AI features?+
MLX with its NumPy-like Python API is excellent for rapid prototyping on a Mac. Cactus is better for deploying prototypes into production mobile apps. Many teams prototype in MLX and deploy via Cactus.
Can Cactus use models trained with MLX?+
Models fine-tuned with MLX can often be exported to formats compatible with Cactus. The MLX ecosystem produces standard model weights that can be converted for deployment in other runtimes.
Does either support NPU acceleration?+
Cactus supports Apple Neural Engine (NPU) acceleration. MLX uses Metal GPU acceleration on Apple Silicon but does not directly target the Neural Engine. Cactus may achieve better efficiency on NPU-heavy workloads.
Try Cactus today
On-device AI inference with automatic cloud fallback. One unified API for LLMs, transcription, vision, and embeddings across every platform.