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SSD streaming throughput is too slow to be usable.

GLM-5.2 has 40B active parameters at a time. At Q4 that's 20GB. The best PCIe 5 SSDs can get 15GB/sec when everything goes well. Every expert load would take more than a second.

If you had enough RAM and enough SSDs in parallel you might get a couple tokens per second on a good day. If you left this machine running 24 hours straight, you might be able to get 200,000 tokens generated.

So it can be done, but only if you interact with your LLM like you're e-mailing someone back and forth and you're okay waiting until tomorrow for a response.

You would spend $50K to buy a machine that consumes 2000W and takes all day to produce as many tokens as I could buy on OpenRouter for $0.60. You would spend $5-15 on electricity depending on where you live.

If you have no other option but to process data locally and you must use a very large model and you aren't in a rush, this can do it. I would not recommend it unless you're desperate and operating inside of rigid constraints.

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You can improve that with speculative preload. I'm sure models could be designed and tuned around efficient SSD offloading to keep throughput pretty high.
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It would apply equally to GPU or RAM inference as those are also bandwidth constrained on decode, so people already try to optimize for it.
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surely the supply of unified memory will rise to meet demand before this is needed
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