Apple Silicon M3 and M4: The Silent Advance in Portable Computing
Actualizado: 2026-05-03
Apple launched the M3 chip in October 2023 and the M4 in May 2024, consolidating the trajectory started with M1 in 2020. What’s interesting isn’t 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 — frees 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. What matters isn’t just the figure but the architecture: 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.
Local AI Workloads
The most visible practical consequence for developers is running large models locally. 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 can’t leave to cloud, the advantage is tangible.
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 — 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.
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.
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’s Neural Engine TOPS.
- On workstations, NVIDIA with GPUs like the RTX 4090 remains competitive for model training and gaming.
Honest Limitations
Apple Silicon isn’t perfect for everything:
- Linux runs on M1/M2 via Asahi with growing but still incomplete functionality.
- Certain specific drivers — studio audio interfaces, professional video capturers — lack ARM macOS support.
- Gaming remains limited field vs PC.
- Cost: a MacBook Pro M3 Max configured for serious AI workloads is significant outlay, between three and five thousand euros.
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’s the natural choice. Not universal replacement — x86 workstations with NVIDIA still dominate certain niches — but a platform where developer productivity has notably increased in the last four years.
