Three Models Doctrine
Home inference does not need one perfect machine. It needs a small fleet of imperfect machines with clear roles.
My current doctrine is built around three models of compute.
The first model is cheap VRAM: a Tesla P40 with 24GB. It is old, passive, loud, and power-hungry, but it still does one job extremely well: it provides a real 24GB CUDA tier at home. NVIDIA’s Tesla P40 datasheet lists 24GB GDDR5 and 250W max power, which tells you exactly what kind of machine it wants to be: a datacenter card, not a civilized desk companion.
The second model is modern CUDA: an RTX 5060 Ti 16GB. It gives up 8GB of memory compared with the P40, but in return it offers a much more practical everyday experience: current-generation NVIDIA support, better efficiency, easier cooling, and far higher useful speed for normal single-user local inference. NVIDIA’s official 5060 family specs confirm the 16GB model exists, and board-partner specs place it at 4608 CUDA cores and about 180W power.
The third model is portable unified memory: a MacBook Pro with M3 Max and 64GB unified memory. Apple’s official specs confirm that the M3 Max MacBook Pro can be configured with 64GB unified memory, which makes it a very different kind of local AI machine: quieter, more portable, and more flexible in memory fit than a 16GB gaming GPU, but generally not the fastest choice for raw local inference throughput.
That is the doctrine:
24GB but loud and power-hungry. 16GB but fast and modern. 64GB on a Mac: portable, quiet, and good enough.
These are not competing machines. They are different answers to different questions.
The P40 is for “does it fit?” The 5060 Ti is for “does it run well?” The MacBook Pro is for “can I carry it with me and still do useful local work?”
This matters because home inference is no longer a search for a single winner. It is a balancing act between memory capacity, speed, power, noise, and portability. A big old datacenter card can solve the VRAM problem. A modern midrange GeForce card can solve the usability problem. A high-memory laptop can solve the portability problem. None solves all three at once.
So my current conclusion is simple:
Do not build your home inference strategy around one ideal box. Build it around three roles.
Cheap VRAM. Fast CUDA. Portable unified memory.
That is the Three Models Doctrine.