I am trying to make a platform decision for a professional laptop that will be used for both ordinary software development and AI/data-science work over several years.
The two approaches I am comparing are:
M5 Pro/Max MacBook Pro with 64 GB unified memory and 2 TB SSD, possibly 128 GB if that is more valuable.
High-end NVIDIA laptop with CUDA but much less GPU memory, more heat/noise and usually worse battery life.
Typical work includes Docker-based web development, Python/Jupyter/Conda, dataset work, ML experiments and local inference. Large training jobs can use cloud GPUs, but I want the laptop to remain useful offline and for private/local models.
The full laptop-and-monitor budget is €6,000, with roughly €5,000 available for the laptop. I am in Croatia/EU and will buy only brand-new, factory-sealed hardware—no refurbished, used, returned, display or open-box units.
I am interested in the architectural tradeoff rather than a brand argument:
- For local inference, when does a 64–128 GB unified-memory pool outweigh CUDA's faster and broader software ecosystem?
- Which real development workflows still make a local NVIDIA GPU essential?
- How much friction is involved in developing on MPS/MLX locally and moving training to remote CUDA?
- Does a mobile NVIDIA GPU provide enough VRAM and sustained performance to justify its battery, noise and thermal compromises?
- Is a strong daily-driver laptop plus rented/cloud CUDA more flexible than trying to put all compute in one portable machine?
- Which platform is likely to retain more practical usefulness as local models and agent workflows evolve?
I would especially value answers from people who actively use both Apple silicon and CUDA systems.
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