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Qwen 3.8 Flash Next Gyro Coder 🚢 - Optimized for (agentic) coding workflows - Fits 24 GB VRAM - Python KLD 0.165 - 21 GB total size - 50% of experts kept

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Laurent Zuijdwijk

1,561 subscribers

13,922 Aufrufe • vor 4 Tagen •via X (Twitter)

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19 Kommentare

Profilbild von Kyle Hessling
Kyle Hesslingvor 4 Tagen

Speed is looking awesome on a 5090! Great work! Looking forward to doing some more tests!

Profilbild von Marko
Markovor 4 Tagen

21 GB of a 24 GB card is gone before the conversation starts, so the agent loop's real limit is the little that is left.

Profilbild von nardoblanc
nardoblancvor 3 Tagen

fitv in 24gb is just wow bro Congrats

Profilbild von HelloHello
HelloHellovor 3 Tagen

gyro M is my everyday used, I have dual 3060 + P100 (gen 2x1) with 24 gb system Ram, 30-40 token per sec. satisfied for me with the model intelligence.

Profilbild von Laurent Zuijdwijk
Laurent Zuijdwijkvor 3 Tagen

That is a great result! Would love to hear your opinion about gyro coder.

Profilbild von Mark Murphy
Mark Murphyvor 3 Tagen

Agentic coding tune that fits in 24GB is a nice one. I'm on Qwen 3.8 27B at home, so this is going on my list to try.

Profilbild von JuggyBob
JuggyBobvor 4 Tagen

@grok is this better than the GSQ RCO quant that strata uses?

Profilbild von 🤖Sergio
🤖Sergiovor 3 Tagen

How does it compare with Qwen 3.8 27B Q4?

Profilbild von El Minero Sudaka
El Minero Sudakavor 4 Tagen

Make a 4 bit please

Profilbild von Laurent Zuijdwijk
Laurent Zuijdwijkvor 3 Tagen

Why? Just run the ful Gyro M model.

Profilbild von 君子中庸
君子中庸vor 3 Tagen

WHAT? 50% of experts kept it means...?

Profilbild von TolkienWindow
TolkienWindowvor 4 Tagen

look pretty cool, how does it do with spatial reasoning with all those experts trimmed?

Profilbild von Laurent Zuijdwijk
Laurent Zuijdwijkvor 3 Tagen

Look at the examples in the video. I paid extra care to get the spatial aspect right and keep world knowledge intact.

Profilbild von TolkienWindow
TolkienWindowvor 3 Tagen

good deal, thank you for taking the time to get that tuned! <3

Profilbild von Latent Node
Latent Nodevor 3 Tagen

Curious about the calibration set: if experts were pruned by routing frequency on mostly Python, TypeScript and Rust prompts may lean on the dropped ones. What does the KLD look like there?

Profilbild von Laurent Zuijdwijk
Laurent Zuijdwijkvor 3 Tagen

Experts weren't just dropped by routing frequency. I spent a lot of time keeping world and spatial knowledge intact while keeping the coding KLD as low as possible. KLD was measured at every step across Python, web, C++, Rust and shell. Routing frequency was just the starting point.

Profilbild von Latent Node
Latent Nodevor 3 Tagen

Thanks for the detail. Measuring KLD across five languages at every step is more careful than most pruned releases. A per-language KLD table on the model card would help people pick the right build for their stack.

Profilbild von Laurent Zuijdwijk
Laurent Zuijdwijkvor 3 Tagen

I am adding this to the model card as well.

Profilbild von Alfred
Alfredvor 3 Tagen

Great work, as always. How does it compare to Gyro-M? Did it have to sacrifice a lot of world knowledge?

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