Qwen 3.8 Flash Next is a mixture-of-experts model in the Qwen family, discussed mainly in the local LLM community. One developer reported running a 68GB quantized version at about 21 tok/s on an RTX 3060 12GB with 16GB RAM using expert-routing prediction, while forks of llama.cpp and halogen-flash-server reached roughly 52 tok/s decode and 1300 tok/s prefill. Another user said Codex CLI configuration let it outperform GPT 5.6 Luna on a 3D Mario test.
Qwen 3.8 Flash runs at ~21 tok/s on RTX 3060A developer reports running a 68GB quantized Qwen 3.8 Flash Next MoE at about 21 tok/s on an RTX 3060 12GB with 16GB RAM, using expert-routing prediction to speed CPU/GPU offload with no gate pruning and bit-exact output. Warm cache reaches 24+ tok/s, versus roughly 1.4-2.1 tok/s with stock llama.cpp.
User says Codex CLI greatly boosts local LLM performanceA user reports that configuring Codex CLI with GPT's help let local Qwen 3.8 Flash Next outperform GPT 5.6 Luna on a 3D Mario test and a real project, after local models had long lagged in serious work. The user also ported a search tool into Codex as a skill.
User shares local LLM editing vLLM service configA local-LLM user posted on r/LocalLLaMA that they run Qwen 3.8 Flash Next as a driver for Hermes and Pi, asking it to add CUDA_DISABLE_PERF_BOOST=1 to their vLLM service to cut idle server power draw.
Local LLM community glimpses a golden eraA Reddit post says hardware shortages have pushed the local LLM community back into low-level optimization: forks of llama.cpp and Strix Halo's halogen-flash-server boosted Qwen 3.8 Flash Next (Q38FN) to about 52 tok/s decode and 1300 tok/s prefill. The author argues scarcity spurs learning and technical growth.
Defense of Artificial Analysis BenchmarksA r/LocalLLaMA post defends the Artificial Analysis aggregated benchmark, outlining its methodology and costs, and cites performance differences among Deepseek, Qwen, and GPT‑6 across subbenchmarks.
The A.I. Daily