[Release] GSQ-RCO GGUFs for Qwen3.8-Flash-Next, plus a 50% expert-pruned Coder build at ~1.89 bpw

We have released Qwen3.8-Flash-Next quantized with GSQ and RCO, together with a second, capability-targeted build in which half of the model's experts have been removed.

Flash-Next is a sparse mixture-of-experts model: 512 routed experts per layer across 48 layers, 176.9B parameters, 354 GB at BF16.

What's inside

• Four quantized GGUFs, 2.40 to 3.50 bpw (66.4 to 83.6 GB), and the BF16 vision projector
• Expert-pruned Coder GGUF, 58.4 GB in total, of which 29.6 GB must remain resident
• GSQ (Gumbel-Softmax Quantization): post-training scalar quantization that jointly learns grid assignments and group scales, closing most of the gap between scalar and vector quantization at low bit-widths while remaining deployable in standard GGUF types
• RCO (Riemannian Constrained Optimization): enforces exact budgets by gradient descent on the task loss, without per-constraint tuning. It serves two roles in this release: assigning a quantization type to every tensor, and selecting which experts to retain in the Coder build, where it enforces several exact budgets simultaneously, one per layer

Results:

At 3.50 bpw the model matches the BF16 base on every benchmark evaluated.

• IQ3_S (3.50 bpw, 83.6 GB): AIME25 100.00, GPQA-Diamond 92.93 against 91.92 for BF16, LiveCodeBench v6 86.86 against 87.43. Task average 93.26 against 93.12.
• IQ3_XXS (3.00 bpw, 75.8 GB): AIME25 100.00, GPQA-Diamond 91.41, LiveCodeBench v6 86.29
• Q2_0 (2.40 bpw, 66.4 GB): zero-shot average 78.00, above the BF16 value of 76.94, at approximately one fifth of the size

Coder (capability pruned model):

Instead of storing every parameter at lower precision, half of the routed experts are removed from the model: 256 of 512 per layer, selected by RCO optimising the KL divergence against the unpruned model. The retained weights remain at 3.5 bpw. Pruning and quantization compound, and the combined effect is an average of 1.89 bits per parameter of the original transformer. The averaged bitwidth amortises the removed experts over the original parameter count, and therefore expresses the joint effect of pruning and quantization. No individual weight is stored at 1.89 bits.

The practical consequence is that a 176.9B-parameter model has a resident working set of 29.6 GB, since the n-gram shard is a lookup table and may be served from disk. This is within the capacity of a single 32 GB accelerator.

• SWE-bench Verified: 75.60 against 82.80 for BF16, retaining 91.3%
• LiveCodeBench v6: 86.28 against 87.43, retaining 98.7%

Both measured at xhigh reasoning effort.

Links

• Quantized models: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF
• Coder (expert-pruned): https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF
• GSQ: paper https://arxiv.org/abs/2604.18556 | code https://github.com/IST-DASLab/GSQ
• RCO: paper https://arxiv.org/abs/2605.00649 | code https://github.com/IST-DASLab/RCO

Both repositories ship the complete per-tensor RCO allocation.

The Coder build is an experimental release and feedback is welcome, particularly on capabilities that were not represented in the calibration mixture. Requests for models to quantize or prune are also welcome.

From the ISTA Deep Algorithms and Systems Lab.