Qwen3.8-Flash-Next Benchmarks Show Wide Performance Range Across Hardware
This story is from 2026-08-27. It is preserved in the archive; the latest stories are on the live feed.
Community benchmarks for Qwen3.8-Flash-Next show speeds from 33 to 203 tok/s depending on hardware and engine, with vLLM outperforming llama.cpp at long context and DGX Spark setups reaching 181-203 tok/s aggregate.
Read the full story at r/LocalLLM ↗
Timeline · 7 reports
- 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 22:00 · r/LocalLLaMA
Today I hit 181 toks/s (aggregate) on Qwen3.8-Flash-Next on 2x DGX Sparks - 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 - 2026-08-27 20:56 · r/LocalLLM
Qwen3.8-Flash-Next on 2x RTX 3090s, 33-43 tok/s - 2026-08-27 18:10 · r/LocalLLM
unsloth/Qwen3.8-Flash-Next-GGUF on NVIDIA GeForce RTX 5090 — 43.4 tok/s — llm-bench.io - 2026-08-27 15:32 · r/LocalLLM
Qwen3.8-Flash-Next NVFP4 running on vLLM across 2x DGX Spark — 63 tok/s single stream, 203 tok/s at 8 concurrent. Needed a 3-line patch, repo inside