You could easily fit a 1T parameter model on a MacBook if you radically altered the architecture of the AI system.
Consider something like a spiking neural network with weights & state stored on an SSD using lazy-evaluation as action potentials propagate. 4TB SSD = ~1 trillion 32-bit FP weights and potentials. There are MacBook options that support up to 8TB. The other advantage with SNN - Training & using are basically the same thing. You don't have to move any bytes around. They just get mutated in place over time.
The trick is to reorganize this damn thing so you don't have to access all of the parameters at the same time... You may also find the GPU becomes a problem in an approach that uses a latency-sensitive time domain and/or event-based execution. It gets to be pretty difficult to process hundreds of millions of serialized action potentials per second when your hot loop has to go outside of L1 and screw with GPU memory. GPU isn't that far away, but ~2 nanoseconds is a hell of a lot closer than 30-100+ nanoseconds.
Consider something like a spiking neural network with weights & state stored on an SSD using lazy-evaluation as action potentials propagate. 4TB SSD = ~1 trillion 32-bit FP weights and potentials. There are MacBook options that support up to 8TB. The other advantage with SNN - Training & using are basically the same thing. You don't have to move any bytes around. They just get mutated in place over time.
The trick is to reorganize this damn thing so you don't have to access all of the parameters at the same time... You may also find the GPU becomes a problem in an approach that uses a latency-sensitive time domain and/or event-based execution. It gets to be pretty difficult to process hundreds of millions of serialized action potentials per second when your hot loop has to go outside of L1 and screw with GPU memory. GPU isn't that far away, but ~2 nanoseconds is a hell of a lot closer than 30-100+ nanoseconds.
Edit: fixed my crappy math above.