alt Hacker NewsExcuse me, could you elaborate how you came up with 192GB of RAM? I almost thought they downsized model, but it's still 2.8T tokens. Are we talking about not-yet-out 1-bit quant or assumption that something like ds4 (daskstar, not deepseek) will heavily optimize it?
Not a trolling question, genuine interest.
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It's not based on loading the full 1.5TB model into VRAM or system RAM, it's just streaming parameters on demand from SSD - which is worthwhile since the MoE portion of the model is quite sparsely accessed. This has been demoed on a 128GB M5 MacBook Pro with lousy performance (0.3 tok/s or so) because it had to load at least ~25GB from SSD per token (comprising the entirety of the sparsely routed active params) and potentially even reload some of the dense weights, depending on what exactly managed to fit on that platform. With 192GB you'd avoid any reloading for the dense weights and even cache a few layer-experts.
it's a mixture of experts so it's somewhat viable to stream weights from nvme
All these very large models are sparse mixtures of experts (SMoE), where typically only 2-3% of weights are active for a given forward pass.
Usually there are some static experts in each attention layer, plus a whole bunch of dynamically-routed experts. Typically some form of LRU cache is used for the dynamic experts.
So you can keep only the static expert(s) and routing layers in memory and then stream the dynamic experts from SSD, at a modest inference speed cost driven by the latency required to load layers from SSD when they aren’t already in the memory cache.
The LRU cache works reasonably well because a given task will often hit the same experts on each iteration of the forward pass.
For example, on a Mac Studio, Qwen3.8-flash-next (180b total parameters) with 50% of experts in memory and n-gram offloading has about 1/4 to 1/3 the inference speed of everything in memory. That requires 32GB of vRAM for the weights (plus a little extra for context, obviously)