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Notes -
Respectfully, I think GLM-130B is not the right scale for the present-day present-time personal assistant. Ideally, someone (Carper?) would release a 30B or 70B Chinchilla-scaled LM for us to use as a base, but barring that lucky outcome (not sure if carper will be allowed to) I'd go with UL20B or a smaller Flan-T5, or one of several available 10-20B decoder-only models.
In this setting I have in mind, GLM-130B zero-shot prompted with what amounts to our values could be used either as a source of custom base CoT-dialogue finetune dataset or as a critique-generator and ranker in the Anthropic's constitutional AI setting. So, their inference-only config which supports servers as small as 4x RTX3090 could be used. Granted, the performance of GLM-130B in its current raw shape is somewhere between "GPT-3.5" and older Instruct-GPT-3, but it should suffice for the purpose described here.
Wearing my ML engineer hat I could say that while this is a conventional requirement, if we were determined to tune this LLM on a few batches on a given single server, we could use DeepSpeed's Zero-3 offload mode and maybe a bit of custom code to swap most of the parameters to the CPU RAM, which is much cheaper and is surprisingly efficient given large enough batches. One transformer layer worth of VRAM would be enough. One server likely wouldn't be enough for the complete operation, but used infiniband cards and cables are surprisingly cheap.
Regarding the kv cache, I expect the next generation of the transformer-like models to use advanced optimizations which lower kv cache pressure, specifically memorizing transformer. There are other competitive inventions, and discussion of the highest performing stack of tricks to get to the most efficient LM would be interesting, if exhausting.
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