Let's look at the physical footprints at a 4-bit (Q4) quantization. A 744B parameter model sits at roughly 370 GB cold on the NVMe drive. Because it’s a sparse Mixture of Experts, the dense backbone—the attention mechanisms and shared weights that every single token must touch—is surprisingly light: about 17B parameters, or roughly 9.9 GB.
If we draw up the memory allocation inside our 48GB pool, the board looks like this:
48 GB (Total) - 9.9 GB (Dense Backbone) - 4 GB (OS & Apps) - 2 GB (KV Cache) ≈ 32 GB Remaining
That leaves us a 32GB sandbox to allocate entirely to the lock-free expert ring buffer.
When this model executes, it activates roughly 40B parameters per token. At Q4 precision, that requires pulling ~11 GB of active experts into memory per step. Because our ring buffer is 32GB, it has enough structural breathing room to hold 2 to 3 slots of speculatively loaded future tokens simultaneously. The memory geometry fits.
Calculating the Bandwidth Reality
Now we look at the unyielding bottleneck: the internal Apple Silicon SSD bus, which reliably pushes a sustained read bandwidth of about 6.5 GB/s.
If the engine had to blindly pull a completely fresh set of 11 GB of experts for every single token, the raw physical limit is basic arithmetic:
Raw I/O Time Per Token = 11 GB / 6.5 GB/s ≈ 1.69 seconds ⇒ 0.59 tokens/second
But we know neural routing isn't completely chaotic. Tokens traveling through a sentence exhibit temporal expert locality—the token handling a specific technical context will likely trigger some of the exact same experts used by the token right before it.
If the speculative lookahead successfully exploits this, allowing us to reuse just 20% of the active experts already sitting warm in the ring buffer, the actual disk read demand drops from 11 GB down to ~8.8 GB:
Optimized I/O Time = 8.8 GB / 6.5 GB/s ≈ 1.35 seconds ⇒ 0.74 tokens/second
This lands us squarely in that 0.6 to 0.8 tokens per second window.
Perspectives on Speed
Not that we are calling this fast by modern server standards, but it completely rewrites the expectations for what a local machine can do. It’s all about perspective.
We used to wait hours just to download a single movie;
Waiting 30 or 40 seconds for a frontier-class AI to generate a deeply reasoned paragraph locally on a laptop isn't a penalty—it's a miracle.
Will it hold up when the code actually executes? The ultimate variable is the "Scout Fan-Out." With hundreds of potential experts per layer, the speculative engine's branch prediction has to be incredibly precise. If it misguesses too often, the pipeline will stall, waiting for synchronous disk reads.
But looking at the numbers on the board right now, the physics check out. We aren't just daydreaming—this approach stands a very real chance of turning an impossible 744B paperweight into a highly capable, private asynchronous workhorse.