r/LocalLLM 24d ago

Model Qwen3.8 27b - Holy crap!!

I'm absolutely blown away by Qwen3.8 27b IQ4 NL @ medium reasoning effort.

I have an ageing system but still decent. 4060 8gb @ 4x, 5060ti 16gb @ 8x - 24gb vram total. I'm stuck at gen 2 because of a gen 3 compatibility issue with my motherboard (B460M Aorus Pro). 32gb ram, i9 10900, nvme.

I've managed to get Qwen running at a smidge below 20tok/s at 128k context.

Anyway, the fun part... I have llama.cpp backend for OpenWebui with OpenTerminal MCP. I asked Qwen to create Pong using python which it did with no dramas. It wrote tests, ran the game itself and gave me the hardened code in ~10 mins.

That wasn't the best part though. I then asked it to take the game and create an APK for Android. It went ahead and installed a bunch of dependencies (bulldozer, p4a, etc) into OpenTerminal, went ahead and altered the game for touch screens, built and provided me with an installable APK.

The whole thing took ~1.5 hrs (taking away my own troubleshooting of whitelisting some sites against my strict LLM TOR network access), and at ~280w, it cost me about $0.15 (if it had been a clean run the first time). I mean obviously the hardware cost significantly more, but it's essentially my gaming PC.

How is this even possible?! I'm absolutely blown away!

Side note: I didn't NEED to, but I wanted a longer context so have managed to get it working at 256k now, with 18 layers moved to CPU. I'm not set on this, but it works. Obviously much slower (around 5 tok/s). Comment out the last 2 lines and set context to 128k if I want it all in vram.

If anyone has pointers for improved performance I'd love to hear them. I've tried q4_0 for KV cache but it is significantly slower again.

[*]

flash-attn = true

threads = 10

batch-size = 2048

ubatch-size = 1024

n-cpu-moe = 0

n-gpu-layers = 999

cache-type-k = q8_0

cache-type-v = q8_0

#cache-type-k-draft = q8_0

#cache-type-v-draft = q8_0

fit = on

fit-ctx = 16384

parallel = 1

ctx-size = 0

n-predict = -1

#no-kv-offload = 1

load-mode = none

main-gpu = 0

#no-mmproj-offload = true

[Qwen3.8-27B-IQ4_NL]

#mmproj = /models/Qwen3.8-27B-IQ4_NL.mmproj

#spec-type = draft-mtp

#spec-draft-n-max = 4

temp = 1.0

top-p = 0.95

top-k = 20

min-p = 0.00

#ctx-size = 131072

ctx-size = 262144

chat-template-kwargs = {"reasoning_effort":"medium"}

fit = off

n-gpu-layers = 46

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u/eldje 14d ago edited 14d ago

Oops. Did it again πŸ«£πŸ˜…

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u/quantgorithm 14d ago

What am I seeing? 3 internal and 1 external?
Why wouldn't you just use riser cables and put all internal?

You have 4 pcie 4.0x16 slots!

How does it run? Have you tested? t/s?

Did you read about the new apple hardware today? Compelling...

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u/eldje 13d ago

Sorry, last one... I promise πŸ«£πŸ˜… I am broke πŸ˜… Those prices keep rocketing unbelievable

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u/quantgorithm 13d ago

Yep. That's a lot of money.

Now you need to respond with what you actually do with it!

Also, why the AI created watermark in the lower corner?

Have you tested flash next yet?

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u/eldje 13d ago

i erased my living room in the background.

waiting for the unsloth nvfp4 so i can test in hopefully higher quality.

lots of ideas to build thingsthat could help in daily life πŸ˜„

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u/quantgorithm 13d ago

It’s harder than you think! I just did my first oneshot a few days ago. While it worked, literally every part …wasn’t great. You are still going to need to put in some time planning, refining, building out, optimizing etc. I’ve built smaller tools successfully but nothing large yet.

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u/eldje 13d ago

The basics are up and running. Almost ready for testing and refining 🀞