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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 görüntüleme • 4 gün önce •via X (Twitter)

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Kyle Hessling profil fotoğrafı
Kyle Hessling4 gün önce

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

Marko profil fotoğrafı
Marko4 gün önce

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.

nardoblanc profil fotoğrafı
nardoblanc3 gün önce

fitv in 24gb is just wow bro Congrats

HelloHello profil fotoğrafı
HelloHello3 gün önce

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.

Laurent Zuijdwijk profil fotoğrafı
Laurent Zuijdwijk3 gün önce

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

Mark Murphy profil fotoğrafı
Mark Murphy3 gün önce

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.

JuggyBob profil fotoğrafı
JuggyBob4 gün önce

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

🤖Sergio profil fotoğrafı
🤖Sergio3 gün önce

How does it compare with Qwen 3.8 27B Q4?

El Minero Sudaka profil fotoğrafı
El Minero Sudaka4 gün önce

Make a 4 bit please

Laurent Zuijdwijk profil fotoğrafı
Laurent Zuijdwijk3 gün önce

Why? Just run the ful Gyro M model.

君子中庸 profil fotoğrafı
君子中庸3 gün önce

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

TolkienWindow profil fotoğrafı
TolkienWindow4 gün önce

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

Laurent Zuijdwijk profil fotoğrafı
Laurent Zuijdwijk3 gün önce

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

TolkienWindow profil fotoğrafı
TolkienWindow3 gün önce

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

Latent Node profil fotoğrafı
Latent Node3 gün önce

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?

Laurent Zuijdwijk profil fotoğrafı
Laurent Zuijdwijk3 gün önce

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.

Latent Node profil fotoğrafı
Latent Node3 gün önce

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.

Laurent Zuijdwijk profil fotoğrafı
Laurent Zuijdwijk3 gün önce

I am adding this to the model card as well.

Alfred profil fotoğrafı
Alfred3 gün önce

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

Benzer Videolar

The VRAM barrier is officially dead. I just ran Qwen 3.8 Flash Next (MoE) 125B A6B with a 250,000 context window on a single 24GB RTX 4090. 21 tokens/sec decode. 364 t/s prefill. no mtp. no dflash. no kv cache quantization! We are running datacenter models on consumer hardware. Tested on Ubuntu 22 | CUDA 13.0 | PCIe 4.0 x16 | 110 GB DDR4 System RAM with a continuous 28k prompt across all runs. ### The Benchmarks & Scaling # 1. Hybrid Offload (-ncmoe 40 @ 80k Context) Offloaded 40 expert layers to the GPU, pushing VRAM to the ceiling. ./build/bin/llama-server -m Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf -c 80000 --port 8080 -v --fit off -b 4096 -ub 4096 -ncmoe 40 Prefill: 383.85 t/s | Decode: 22.52 t/s Footprint: 23.85 GB VRAM | 97 GB RAM # 2. Full CPU MoE Offload (-cmoe @ 80k Context) Pinned all 512 expert layers to DDR4 RAM (-cmoe), keeping attention on the 4090. llama.cpp flags: (Same as above, replace -ncmoe 40 with -cmoe) Prefill: 355.72 t/s | Decode: 20.84 t/s Footprint: 11.66 GB VRAM (12GB+ VRAM freed up!) | 110 GB RAM # 3. The 180,000 Context Run Prefill: 357.75 t/s | Decode: 20.98 t/s | VRAM: 15.6 GB | RAM: 110 GB # 4. The 250,000 Context Absolute Ceiling ./build/bin/llama-server -m Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf -c 250000 --port 8080 -v --fit off -b 4096 -ub 4096 -cmoe Prefill: 364.29 t/s | Decode: 20.97 t/s Footprint: 18.3 GB VRAM (Still ~5.7 GB of VRAM headroom!) | 110 GB RAM ### Key Insights: -b 4096 -ub 4096: doubles the prompt ingestion from ~150 to 364+ t/s. -cmoe Free Lunch: Shifting expert layers to DDR4 RAM slashes VRAM from 24GB to 11.6GB with virtually zero decode penalty (22.5 -> 20.9 t/s), enabling the 250k context ceiling. Qwen 3.8 Flash-Next (UD-Q4_K_XL) is a massive 111.4 GB model split across 4 shards. To run this architecture, you must build from the experimental PR branch (#27742) by Daniel Han: git clone && cd llama.cpp git fetch origin pull/27742/head:qwen-next && git checkout qwen-next cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native -DBUILD_SHARED_LIBS=OFF cmake --build build --config Release -j $(nproc) --target llama-server A single 4090 paired with 100 GB of cheap DDR4 RAM will comfortably serve production grade 125B inference. While Qwen 3.8 27B (dense) still holds the crown for single 3090/4090 rigs, Flash Next proves 125B hybrid models are officially viable on consumer hardware. Hugging Face GGUF link and complete performance telemetry graphs are dropped in the replies below. GLM 5.3 Flash VS Qwen 3.8 Flash Next, which one takes the open weights crown this week?
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The VRAM barrier is officially dead. I just ran Qwen 3.8 Flash Next (MoE) 125B A6B with a 250,000 context window on a single 24GB RTX 4090. 21 tokens/sec decode. 364 t/s prefill. no mtp. no dflash. no kv cache quantization! We are running datacenter models on consumer hardware. Tested on Ubuntu 22 | CUDA 13.0 | PCIe 4.0 x16 | 110 GB DDR4 System RAM with a continuous 28k prompt across all runs. ### The Benchmarks & Scaling # 1. Hybrid Offload (-ncmoe 40 @ 80k Context) Offloaded 40 expert layers to the GPU, pushing VRAM to the ceiling. ./build/bin/llama-server -m Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf -c 80000 --port 8080 -v --fit off -b 4096 -ub 4096 -ncmoe 40 Prefill: 383.85 t/s | Decode: 22.52 t/s Footprint: 23.85 GB VRAM | 97 GB RAM # 2. Full CPU MoE Offload (-cmoe @ 80k Context) Pinned all 512 expert layers to DDR4 RAM (-cmoe), keeping attention on the 4090. llama.cpp flags: (Same as above, replace -ncmoe 40 with -cmoe) Prefill: 355.72 t/s | Decode: 20.84 t/s Footprint: 11.66 GB VRAM (12GB+ VRAM freed up!) | 110 GB RAM # 3. The 180,000 Context Run Prefill: 357.75 t/s | Decode: 20.98 t/s | VRAM: 15.6 GB | RAM: 110 GB # 4. The 250,000 Context Absolute Ceiling ./build/bin/llama-server -m Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf -c 250000 --port 8080 -v --fit off -b 4096 -ub 4096 -cmoe Prefill: 364.29 t/s | Decode: 20.97 t/s Footprint: 18.3 GB VRAM (Still ~5.7 GB of VRAM headroom!) | 110 GB RAM ### Key Insights: -b 4096 -ub 4096: doubles the prompt ingestion from ~150 to 364+ t/s. -cmoe Free Lunch: Shifting expert layers to DDR4 RAM slashes VRAM from 24GB to 11.6GB with virtually zero decode penalty (22.5 -> 20.9 t/s), enabling the 250k context ceiling. Qwen 3.8 Flash-Next (UD-Q4_K_XL) is a massive 111.4 GB model split across 4 shards. To run this architecture, you must build from the experimental PR branch (#27742) by Daniel Han: git clone && cd llama.cpp git fetch origin pull/27742/head:qwen-next && git checkout qwen-next cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native -DBUILD_SHARED_LIBS=OFF cmake --build build --config Release -j $(nproc) --target llama-server A single 4090 paired with 100 GB of cheap DDR4 RAM will comfortably serve production grade 125B inference. While Qwen 3.8 27B (dense) still holds the crown for single 3090/4090 rigs, Flash Next proves 125B hybrid models are officially viable on consumer hardware. Hugging Face GGUF link and complete performance telemetry graphs are dropped in the replies below. GLM 5.3 Flash VS Qwen 3.8 Flash Next, which one takes the open weights crown this week?

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