Self-hosting Kimi K3: 20% more hardware cost, 20% better task resolution

Recently I started playing with LM Studio and local models and found out that `gemma-4-26b-a4b` is suprisingly capable. I don't need elaborate akin to "create complete app to do X" or "refactor the whole codebase of bazzilion of LOC" but rather "how to go about doing thing X" or for language study (explaining nuances of phrasal verbs or subtelties of vocabulary in other languagues) and darn -- the results are rather good (to the point that I use it mostly nowadays)

>which box to buy

…

> spark, costs less than a conference trip.

I know putting actual prices regionally localizes your article and temporally, with how prices are so unstable. But analysis of “what to buy” without actual prices is borderline meaningless.

Overall, good article, very interesting to see a real deployment that’s actually attainable and not just a subscription to a big 3 token plan.

This seems like a temporary situation. Utilization maximization is a matter of switching to the software factory unattended flow and intelligently routing tasks that will not require assistance to the unused time.

A decade and a half ago we used to run massive map reduce jobs overnight. Code will be handled like this.

I would love to see such comparisons but with quantized versions, because quantization allows running models on smaller hardware with some quality loss. I am running Qwen3.6-35B-A3B quantized to int4 on an A6000 card that was otherwise just sitting around idle. It works up to a degree, but I would love to see benchmarks comparing different quantizations of several models, especially in quality. This is an important dimension in deciding whether to buy a GPU is worth it, and it is missing from this (otherwise comprehensive) article.

I could not focus on the article with all the noise in the background. It was annoying in the header but then it continued down the page. If you want to do this on your marketing pages have at it but for a blog/news style page? Reader mode was the only way to restore sanity.

Author here(I'm on the team). We updated this post after Monday's Kimi K3 weights release: fitting the 2.8T model means going from 8×B200 to 8×B300, ~20% more hardware cost, and concurrency drops from 24 to 16 users vs GLM-5.2. Caveat we're upfront about in the post: our 64-task SWEBench Pro subset may be in Kimi's training set, so the 86% resolve rate is an upper bound.

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