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)
That Gemma release is what opened my eyes. Even downloading the models on my iPhone was shocking. It reminded me when folks would put Wikipedia on a CD or something similar.
Obviously does not compare to a leading model but it’s impressive for something that was running on my phone. I could see thinking token output and it’s directionally interesting thought.
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.
You can get easily over 100 tok/s on the Gemma 4 31B QAT if you enable MTP. Same goes for the 27B. I'm getting 880 tok/s of throughout on a single RTX 5090 for batch tasks.
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).
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)
gemma 31b is my goto day-to-day model. It's adequately capable for coding and natural phenomena, and it follows instructions and skills reasonably well. My main negative trait is all the LLM-isms, but I can get past that for the value it brings
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.
The costs are so staggering it's becoming clear we're going to be priced out of owning computers altogether. Self-hosting frontier models is a corporation's choice, not an individual's.
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.
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.
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.
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.
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..
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 feedback!
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.
Kimi k3 is natively mxfp4 w/ mxfp8 activations. Since they talk about glm 5.2 running on a 8x b200s, that's likely fp8 since it wouldn't fit at bf16 (1.5TB weights alone)
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.
I second this. Anyone considering running their own hardware would also consider quantization since they would have full control over what quant to run.
The value proposition changes a lot if you can get 90% of the quality for 50% the price with a quant due to halving your hardware requirement.
Thank you for the article, though! Very informative.
> 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)?
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)
That Gemma release is what opened my eyes. Even downloading the models on my iPhone was shocking. It reminded me when folks would put Wikipedia on a CD or something similar.
Obviously does not compare to a leading model but it’s impressive for something that was running on my phone. I could see thinking token output and it’s directionally interesting thought.
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
- Gemma4-31B-QAT (Q4_0) = 58 tok/s
- Qwen3.6-35B-A3B (QB_0) = 30 tok/s
- Qwen3.6-27B (QB_0) = 9 tok/s
EDIT: Updated tok/s after updating llama.cpp
You can get easily over 100 tok/s on the Gemma 4 31B QAT if you enable MTP. Same goes for the 27B. I'm getting 880 tok/s of throughout on a single RTX 5090 for batch tasks.
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.
I use local models like qwen to do refactoring; the code and concept already exist, they need refining.
I use sota models for concept, implementation plans, and initial deployment.
I find that local models can perform at a very high level if given a very strict environment and the already bounded rules of an existing code base.
aye aye aye.
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).
No, it's what I get repeatedly. Recall that it's QB_0 though. I'll get back to you when the Q4_K_M quant finishes downloading.
EDIT: I get 259 tok/s with the Q4_K_M quant
Agree - I got 9.7 tok/s on an M1 Max with unsloth's gemma-4-31B-it-qat-UD-Q4_K_XL.
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)
gemma 31b is my goto day-to-day model. It's adequately capable for coding and natural phenomena, and it follows instructions and skills reasonably well. My main negative trait is all the LLM-isms, but I can get past that for the value it brings
>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.
The costs are so staggering it's becoming clear we're going to be priced out of owning computers altogether. Self-hosting frontier models is a corporation's choice, not an individual's.
Don't worry, you'll be able to rent your next computer: https://www.apple.com/newsroom/2026/07/apple-upgrade-launche...
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.
I found it trivial to consume. It should support reduced motion preference though for the more feeble among us.
> It should support reduced motion preference though for the more feeble among us.
What a strong start to a sentence before veering into a pretty gross equivalency.
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.
Same here. Stopped reading.
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.
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.
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
Why does the website have to be so trippy when scrolling? It fucks with my eyes.
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 feedback!
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.
Kimi k3 is natively mxfp4 w/ mxfp8 activations. Since they talk about glm 5.2 running on a 8x b200s, that's likely fp8 since it wouldn't fit at bf16 (1.5TB weights alone)
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.
I second this. Anyone considering running their own hardware would also consider quantization since they would have full control over what quant to run.
The value proposition changes a lot if you can get 90% of the quality for 50% the price with a quant due to halving your hardware requirement.
Thank you for the article, though! Very informative.
> 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)?