Updated: 2026-07-07

Apple launched the M3 chip in October 2023 and the M4 in May 2024, consolidating the trajectory started with M1 in 2020. What is interesting is not raw speed (which also improved), but consolidation of a structural advantage: large unified memory, dramatic energy efficiency, and native AI workload support. For serious developers, the platform has become a differential working option worth considering.

Key takeaways

  • Unified memory eliminates copies between CPU, GPU and Neural Engine, transformative for local language models.

  • M3 Max reaches up to 128 GB; future M4 Ultra configurations will go further.

  • A quantised Llama 3.1 70B fits comfortably on a 64 GB MacBook Pro M3 Max.

  • The M4 Neural Engine declares 38 TOPS specifically for inference, freeing CPU/GPU for other tasks.

  • For local AI workloads, mobile development and long battery sessions, Apple Silicon is the natural choice in 2024.

The unified memory leap

M3 Max extended unified memory to 128 GB in maximum configuration, and the Ultra of subsequent generations reaches 192 GB. The architecture is what matters: CPU, GPU, and Neural Engine share the same memory without inter-space copies. For AI workloads, this detail is transformative.

A quantised Llama 3.1 70B model occupies approximately 40 GB and fits comfortably on a 64 GB MacBook Pro M3 Max. A Llama 3.1 405B quantised to four bits occupies about 220 GB and fits in a 192 GB Mac Studio M2 Ultra. On a standard laptop with discrete GPU, loading these models involves shuffling between system memory and GPU memory that penalises performance and limits useful size.

Real energy efficiency

Battery-life figures are concrete. A MacBook Pro M3 with typical developer workload (IDE, browser with dozens of tabs, Docker containers, a couple of local services) lasts between 12 and 16 hours unplugged. Under intense loads, the fan rarely activates and heat is manageable.

This efficiency is not accidental. Apple designs chips for the specific use patterns of its devices, can afford to prioritise performance per watt, and the silicon-software integration means macOS exploits the architecture fully. Windows manufacturers with ARM chips (Snapdragon X Elite) are getting closer but still trail behind.

Local AI workloads

The most visible practical consequence for developers is running large models on the machine itself. With Ollama, LM Studio, or llama.cpp directly, a MacBook Pro M3 Max can run models that on a typical Windows laptop would require a desktop with workstation GPU.

For experimentation, developing applications with private AI, proofs of concept, and working with sensitive data that cannot leave to cloud, the advantage is tangible. Iteration time changes when the model responds on the local machine rather than depending on API latency and rate-limiting quotas.

General development

Beyond AI, advantages for standard development are real but less dramatic:

  • Compiling Rust, Go, TypeScript, or Swift projects is notably fast.

  • Docker Desktop runs reasonably: the ARM ecosystem covers most popular images.

  • Java, Node.js, Python, all run natively with competitive performance.

Where friction remains is on very specific x86-legacy-linked workloads. Some proprietary libraries, certain enterprise drivers, and specific emulators may require Rosetta 2 with a performance penalty. For most developers on a modern stack, the impact is minimal.

M4 and its Neural Engine

M4, launched in iPad Pro and then Mac, stands out especially for its Neural Engine, the accelerator dedicated to inference operations. Apple claims 38 TOPS specifically for AI. Tasks like Whisper transcription, Stable Diffusion image generation, or small model execution use the Neural Engine, freeing CPU and GPU for other things.

Apple Intelligence (the suite of AI features built into macOS and iOS) relies heavily on this Neural Engine to process requests on the device without sending data to the cloud. For privacy-conscious users, the proposition has real value. This contrasts with the Windows PC NPU situation, where runtime fragmentation across hardware vendors remains a challenge.

Against competition

Honest comparison requires nuance:

  • PC laptops with Snapdragon X Elite approach in energy efficiency but remain behind in GPU performance and native software ecosystem maturity.

  • Laptops with Intel Core Ultra or AMD Ryzen AI processors offer NPUs but with approximately a third of Apple Neural Engine TOPS.

  • On workstations, NVIDIA with GPUs like the RTX 4090 remains competitive for model training and gaming.

For mobile developer work and mixed workloads, the Apple Silicon combination is hard to beat in 2024.

Honest limitations

Apple Silicon is not perfect for everything:

  • Linux runs on M1/M2 via Asahi Linux with growing but still incomplete functionality.

  • Certain specific drivers (studio audio interfaces, professional video capturers, certain industrial peripherals) lack ARM macOS support.

  • Gaming remains a limited field compared to PC.

  • Cost: a MacBook Pro M3 Max configured for serious AI workloads (with 64 or 128 GB of unified memory) runs between three and five thousand euros.

For startups on a budget, the cost matters; for established professionals, productivity usually justifies the investment.

Buying considerations

For developers evaluating a purchase, the key question is actual workload. If day-to-day work includes frequent local AI tasks, choosing enough unified memory (at least 36 GB, ideally 64 or more) transforms the experience. If the work is primarily standard development without AI, the base MacBook Air model is the pragmatic choice.

For desktops, the Mac Studio with M2 Ultra at 192 GB is the option for extreme local AI workloads, including the ability to run a quantised Llama 3.1 405B. The cost is high but there is no direct equivalent in the PC world for that usage range.

Conclusion

Apple Silicon M3 and M4 have consolidated a platform advantage in portable computing for serious developers. The combination of large unified memory, energy efficiency, powerful Neural Engine, and polished software is hard to replicate. For local AI workloads, mobile development, and quiet work with long battery sessions, it is the natural choice. Not a universal replacement (x86 workstations with NVIDIA still dominate certain niches), but a platform where developer productivity has notably increased. Those who can afford the investment get tangible return in daily workflow.