Qwen3.8-Flash-Next Benchmarks Show Engine and Quantization Tradeoffs
This story is from 2026-08-28. It is preserved in the archive; the latest stories are on the live feed.
Community tests of Qwen3.8-Flash-Next (qwen4exp) show llama.cpp prefill speeds vary from 36 to 135 tps depending on split mode, with vLLM roughly 4x faster at long context. Low-VRAM users report success with IQ1_S quant on a 12GB GPU.
Read the full story at r/LocalLLaMA ↗
Timeline · 5 reports
- 2026-08-30 11:32 · r/LocalLLM
Qwen3.8-Flash-Next on single RTX PRO 6000 96GB + 64g RAM, full 262K context with NVMe offloading recipe - 2026-08-30 00:09 · r/LocalLLM
I hit 310 t/s running Qwen/Qwen3.8-Flash-Next-FP8 on 4x RTX PRO 6000 - 2026-08-29 14:47 · r/LocalLLM
Qwen3.8-Flash-Next IQ1_S on a single 5070 (12GB VRAM) - 2026-08-29 08:25 · r/LocalLLM
Qwen3.8-Flash-Next (qwen4exp): llama.cpp isn't ready for agentic work, vLLM is ~4x faster at long context (RTX PRO 6000, full numbers) - 2026-08-28 10:55 · r/LocalLLaMA
Qwen3.8-Flash-Next (UD-IQ4_XS) on 2x RTX 3060 + 7800X3D, from initial 36 tps prefill to 400 tps and other benchmarks (-sm tensor trap) + VRAM/RAM usage