For conversational purposes that may be too slow, but as a coding assistant this should work, especially if many tasks are batched, so that they may progress simultaneously through a single pass over the SSD data.
Like computing used to be. When I first compiled a Linux kernel it ran overnight on a Pentium-S. I had little idea what I was doing, probably compiled all the modules by mistake.
I remember that time, where compiling Linux kernels was measured in hours. Then multi-core computing arrived, and after a few years it was down to 10 minutes.
With LLMs it feels more like the old punchcards, though.
True, but this is not only a trade-off between opex and capex.
Local inference using open weight models provides guaranteed performance which will remain stable over time, and be available at any moment.
As many current HN threads show, depending on external AI inference providers is extremely risky, as their performance can be degraded unpredictably at any time or their prices can be raised at any time, equally unpredictably.
Being dependent on a subscription for your programming workflow is a huge bet, that you will gain more from a slightly higher quality of the proprietary models than you will lose if the service will be degraded in the future.
As the recent history has shown, many have already lost this bet.
I am not a gambler, so I have made my choice, which is local AI inference, using a variety of models depending on the task, i.e. both small models completely executable on relatively cheap GPUs (like the new Intel GPUs), medium models that need e.g. 128 GB on a CPU, and huge models that must be stored on fast SSDs (e.g. interleaved on multiple PCIe 5.0 SSDs).
Such a strategy is achievable with a modest capex, in the lower half of the 4-digit range.
Batching many disparate tasks together is good for compute efficiency, but makes it harder to keep the full KV-cache for each in RAM. You could handle this in an emergency by dumping some of that KV-cache to storage (this is how prompt caching works too, AIUI) and offloading loads for that too, but that adds a lot more overhead compared to just offloading sparsely-used experts, since KV-cache is far more heavily accessed.