A Reddit user reviews agentionai's GYRO quants of Qwen3.8-Flash-Next, claiming they match or beat ISTA-lab's q3-level quants in KLD and loop resistance while being significantly smaller — praising the work of a single contributor.
Whenever it comes to Strata and low bit QFN discussion, everyone is talking about Unsloth, ISTA-lab and nobody mentions Agantai with his GYRO series of quants - https://huggingface.co/agentionai/Qwen3.8-Flash-Next-Gyro-GGUF The quants allegedly have simultaniously better KLD, top 1 and lesser size than the ones from ISTA. Also enhanced loop resitance is advertised. Bold claims and signle contributor were the things why I was initially reluctunt to check the claims myselfm hovewer that potential space savings were impossible to resist so I downloaded GYRO-M and played around with it for a little while. My expersience with it: Before moving further, I'd like to share my serttings: Inference engine: GYRO fork of the Strata Quant used: GYRO-M Vision: yes, took it from ISTA lab IQ3_XXS quant Context: 200k KV cache quantisation: k8v4 Agent: Pi agent Samplings: temperature: 1.0, top_p: 0.95, top_k: 20, min_p: 0.05 Thinking: always xhigh Looping It never looped once during my 3 days of testing, longest autonomous cycle - 2-3 hours, author's claims seems to hold up Tool calls Generally stable, however it sometimes meeses up edits on Cyryllic texts(Russian, Ukrainian languages). Subjective impression - more stable than ISTA q2 quants, less stable than ISTA q3 because of Cyrillics problem Context retention No signs of braindeadness recorded, it never loses initial goal, almost always remembers all the nuances, instruction following is stable Performance As fast as ISTA 2 bit quants Example Prompt: "create an animated voxel 3d world of a pelican riding a bike as good as you can" First photo is ISTA lab IQ3XXS Second one is ISTA lab IQ2XS Third one is GYRO-M https://preview.redd.it/slx0eci7osvh1.png?width=1911&format=png&auto=webp&s=6fd3042b5770850c00bcf4f9450401d418ae51e7 https://preview.redd.it/nwkbc5ahosvh1.png?width=1917&format=png&auto=webp&s=cb5965017ef49df6003427179769cd6cabde8964 https://preview.redd.it/o2lgfothosvh1.png?width=1916&format=png&auto=webp&s=880bab363dc169fc76de8b2ea79a899e361b0778 Conclusion This quant is 100% alive and well and there are strong signals that it's indeed at ISTA lab q3 level while being much smaller, huge thanks for the author for his incredible job, he's a monster because this impeccable job was done by a !single man. It looks like sometimes a solo with skill, perseverance and purpose can go against the dedicated team and win
A user shares their positive experience running Qwen 3.8 Flash Next in IQ4 xs quantization via strata, comparing its quality favorably to models they used professionally last year.
The post announces new quantized versions of the Qwen3.8 Flash model that outperform other high-quality quants through modified KLD measurement and optimized prefill performance.
Qwen team has released an open-weight model, Qwen 3.8 Flash Next, which surpasses DS V4 Flash in performance with half the parameters and is stronger than Opus 4.6, offering a preview of the Qwen 4 architecture.
A developer compares self-hosted Qwen3.8-27B-GGUF (Q4_K_XL via llama.cpp) against cloud Qwen3.8 Flash Next, arguing that despite worse benchmarks, the dense 27B delivers better code quality, fewer rework iterations, more concise reviews, and far lower token consumption for their spec-to-PR workflow.
A user shows that a locally-run, IQ3_S-quantized Qwen3.8-Flash-Next served via Strata and driven by the pi harness on an RTX 5090 matched (and slightly beat in speed) Claude Code Opus 5.5 on the AirBench everyday-tasks benchmark, with full reproduction steps.