I've been calling that the "streaming experts" trick, the key idea is to take advantage of Mixture of Expert models where only a subset of the weights are used for each round of calculations, then load those weights from SSD into RAM for each round.
As I understand it if DeepSeek v4 Pro is a 1.6T, 49B active that means you'd need just 49B in memory, so ~100GB at 16 bit or ~50GB at 8bit quantized.
v4 Flash is 284B, 13B active so might even fit in <32GB.
The "active" count is not very meaningful except as a broad measure of sparsity, since the experts in MoE models are chosen per layer. Once you're streaming experts from disk, there's nothing that inherently requires having 49B parameters in memory at once. Of course, the less caching memory does, the higher the performance overhead of fetching from disk.
> ~100GB at 16 bit or ~50GB at 8bit quantized.
V4 is natively mixed FP4 and FP8, so significantly less than that. 50 GB max unquantized.
Ahh, that actually makes more sense now. (As you can tell, I just skimmed through the READMEs and starred "for later".)
My Mac can fit almost 70B (Q3_K_M) in memory at once, so I really need to try this out soon at maybe Q5-ish.
Streaming weights from RAM to GPU for prefill makes sense due to batching and pcie5 x16 is fast enough to make it worthwhile.
Streaming weights from RAM to GPU for decode makes no sense at all because batching requires multiple parallel streams.
Streaming weights from SSD _never_ makes sense because the delta between SSD and RAM is too large. There is no situation where you would not be able to fit a model in RAM and also have useful speeds from SSD.
On Apple Silicon Macs, the RAM is shared. So while maybe not up to raw GPU VRAM speeds, it still manages over 450GB/s real world on M4 Pro/Max series, to any place that it is needed.
They all do have a limitation from the SSD, but the Apple SSDs can do over 17GB/s (on high end models, the more normal ones are around 8GB/s)
There have been some very interesting experiments with streaming from SSD recently: https://simonwillison.net/2026/Mar/18/llm-in-a-flash/