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

aistack.imec-int.com

21–30 of 56 posts

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

#21
Right, so the 1.4 TB weight footprint straight away makes me wonder how much more important load/unload time becomes in terms of the neocloud product here. With 1.4TB of weights, a fast-enough cold start would basically be _the_ product; fast enough weight-swapping that can make K3 into something you could usefully rent by the hour/workday.

Interesting because again, the license Kimi shipped under [0] defines "Model as a Service" as

> giving a third party access to language model inference or fine-tuning (e.g., via API) in a manner that allows such third party to exercise meaningful control over the inputs, parameters, or training data

and then they have that clause around if you operate such a business above $20M aggregate revenue you need a separate agreement with Moonshot before commercial use, which presumably captures the majority of the larger neoclouds best placed to optimise this.

But where's the line? Say you offer infra optimised for GPU inference, warm pools, isolation per customer, exposed control plane, billed per GPU-hour rather than token, the invoice says compute rather than calls. The customer arguably 'self-hosts', you're probably fine? And if you as a provider run the serving stack and hand your customer an inference API, you're inside the definition regardless of whether you charge by the second or by the token. But what about if you give them direct hardware access, but have the weights cached on NVMe / ramdisk hyperlocal to the infra they're renting so that their hf cli pull only takes a few seconds? Sure, a managed warm pool of GPUs with K3 pre-loaded probably isn't ok, but a local hugging face lru cache holding 'whatever your customers pull down most often', superoptimised for fast weight swaps that the customer controls... is? Is it?

Again, where's the line? Is it materially different from a local docker registry mirror? What about safetensors checkpoints pre-sharded for the specific hardware topology you're offering? Does it matter whether you perform the checkpoint optimisation yourself and make it available, or merely cache one published on HF that happens to target exactly the hardware you rent out? What if you published that checkpoint yourself?

I'm definitely overthinking this, and I'm sure there's been conversation here about this already, but the other kimi threads[1] are enormous. And I am curious.

I'm also curious to know whether Moonshot would actually be against a setup like this. Guessing they would if it was AWS (not quite elastic but not that dissimilar), but what about others? Realistically I guess it'd be easier to just talk to them, especially if you were doing it in a way that targets a slice of the pie they never would have gotten anyway due to data residency requirements etc..

[0]: https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE [1]:https://news.ycombinator.com/item?id=49065752

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

#22

Earlier quoted context omitted.

On my RTX 5090, Qwen3.6-35B-A3B is my go to for coding, but Gemma-4-26b-a4b (Q4_0) is blazing fast and super high quality. I sometimes use the dense cousins of these models (Qwen3.6-27B QB_0 and Gemma4-31B-QAT Q4_0) but they can be very slow. Integrating web search and code calling via Open-WebUI makes it so that I usually don't need to reach for Claude. Here are the tok/s I get: - Gemma-4-26B-A4B (Q4_0) = 214 tok/s…

These token numbers look off for a 5090. For comparison, an rtx 5000 Blackwell SFF with just 470gb/s bandwidth gets me 30 tok/s on gemma4-31B-QAT. Almost 60 tok/s with MTP enabled. A 5090 should get you much more than that!

Thanks for flagging, this is on my local rig and it's driving my display too. I'm curious now, will take a closer look. These are the tok/s as reported by LMStudio.

EDIT: Updating llama.ccp gets me 58 tok/s on Gemma 31b

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

#24
post #9

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.

[flagged]

Noted. Making sure we have a cleanly readable version asap. We wanted to stand out a bit vs. our mother-brand (imec), but fully agree some of us like a more sober reading experience. Feedback much appreciated.

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

#25
post #5

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…

On my RTX 5090, Qwen3.6-35B-A3B is my go to for coding, but Gemma-4-26b-a4b (Q4_0) is blazing fast and super high quality. I sometimes use the dense cousins of these models (Qwen3.6-27B QB_0 and Gemma4-31B-QAT Q4_0) but they can be very slow. Integrating web search and code calling via Open-WebUI makes it so that I usually don't need to reach for Claude. Here are the tok/s I get: - Gemma-4-26B-A4B (Q4_0) = 214 tok/s…

I see a lot of conflicting info about whether Qwen3.6-35B-A3B or Qwen3.6-27B is more capable. Is it one of those things where "it depends"?

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

#26

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. Let us know your thoughts, we really value fee…

Like I said above, you should benchmark quantized versions. With quantization, the same models can be ran at much cheaper hardware, but quality loss is real and this kind of benchmark is an ideal place to put a finger on it.

Solid point. We debated multiple optimization methods and decided to do a more vanilla run first for the setups tested. We’re moving on to some more typical optimization methods for our next article… As soon as our teams comes out a well deserved summer break. Thx for the input.

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

#27

Earlier quoted context omitted.

On my RTX 5090, Qwen3.6-35B-A3B is my go to for coding, but Gemma-4-26b-a4b (Q4_0) is blazing fast and super high quality. I sometimes use the dense cousins of these models (Qwen3.6-27B QB_0 and Gemma4-31B-QAT Q4_0) but they can be very slow. Integrating web search and code calling via Open-WebUI makes it so that I usually don't need to reach for Claude. Here are the tok/s I get: - Gemma-4-26B-A4B (Q4_0) = 214 tok/s…

I see a lot of conflicting info about whether Qwen3.6-35B-A3B or Qwen3.6-27B is more capable. Is it one of those things where "it depends"?

I think sometimes the quantization is the source of the conflicting reports since those are often not included in discussions. Honestly, 27B is too slow for me to use very often, so I just do 35B-A3B and if it is underperforming I'll sometimes switch to 27B, and sometimes it does better, and other times it does not. That's when I reach for Kimi K2.6 or Claude (or just, y'know... use my noggin)

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

#28

Earlier quoted context omitted.

These token numbers look off for a 5090. For comparison, an rtx 5000 Blackwell SFF with just 470gb/s bandwidth gets me 30 tok/s on gemma4-31B-QAT. Almost 60 tok/s with MTP enabled. A 5090 should get you much more than that!

Thanks for flagging, this is on my local rig and it's driving my display too. I'm curious now, will take a closer look. These are the tok/s as reported by LMStudio. EDIT: Updating llama.ccp gets me 58 tok/s on Gemma 31b

The Qwen-35B-A3B numbers are even weirder, did you drop a digit? I get half of that speed on a AMD Radeon RX 5500 XT (RADV NAVI14) (8192 MiB) (unsloth/Qwen3.6-35B-A3B-MTP-GGUF:Q6_K with q8_0 context).

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

#29

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. Let us know your thoughts, we really value fee…

> Aggregate token throughput is about 30% lower (122 vs 170 tok/s at 16 users)

Is that actually the aggregate throughput? 8xB300 (with 4TB/s/GPU bandwidth) is only pushing 8 tg/s/session? That seems… incredibly low, even for an A100B model.

Is it actually 122 tg/s per session? (1952 tg/s aggregate throughout)?

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

#30
post #5

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…

On my RTX 5090, Qwen3.6-35B-A3B is my go to for coding, but Gemma-4-26b-a4b (Q4_0) is blazing fast and super high quality. I sometimes use the dense cousins of these models (Qwen3.6-27B QB_0 and Gemma4-31B-QAT Q4_0) but they can be very slow. Integrating web search and code calling via Open-WebUI makes it so that I usually don't need to reach for Claude. Here are the tok/s I get: - Gemma-4-26B-A4B (Q4_0) = 214 tok/s…

To the models you say for coding, can you give an example of what you code?

I find them insufficient for my projects (mid sized), but curious what people see working.

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