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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

by NorwegianDude2 hours ago|

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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.

by flockonus2 hours ago|

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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.

by serf46 minutes ago|

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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.

by Ilaurens3 hours ago|

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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!

by jszymborski3 hours ago|

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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

by fhars2 hours ago|

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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).

by jszymborski2 hours ago|

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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

by 58 minutes ago|

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by mft_3 hours ago|

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Agree - I got 9.7 tok/s on an M1 Max with unsloth's gemma-4-31B-it-qat-UD-Q4_K_XL.

by 3 hours ago|

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by colordrops3 hours ago|

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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"?

by jszymborski3 hours ago|

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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)

by infecto4 hours ago|

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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.

by incognito1244 hours ago|

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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

by 3 hours ago|

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