Qwen3.8 Flash Next

Headlines · 6

1. 3d
   Community post reports 5,500 tok/s prompt processing on one RTX 5080

   A r/LocalLLaMA post reported that Basalt achieved 5,500 tokens/s in prompt processing while running IQ3_S Qwen3.8 Flash Next on a single RTX 5080. The author also cited peak generation rates of 275 tokens/s for code and tool calls and 190 tokens/s for reasoning traces, using 64GB DDR5-6400 memory and a Core Ultra 9 285K; no independent measurement was supplied.

   r/LocalLLaMA · 🔥 0
2. 7d
   Paper reports coding gains from ledger-based self-orchestration

   A paper introduces ledger-based zero-shot self-orchestration, a training-free method in which fresh instances of the same model decompose problems and coordinate through a shared filesystem containing a plan, notes, and the current solution. The authors evaluated 11 open- and closed-weight models on the 100 latest hard LiveCodeBench problems and reported gains of up to 25.6 percentage points on pinned backends. They report that orchestrated Qwen3.8 Flash Next reached 93.0% pass@1 versus Fable 5's 90.4% at 9% of the cost, while locally served Qwen3.8-27B rose from 66.8% to 92.4%. The gains were not universal: some models were unchanged or performed worse.

   Hacker News 首页（100+ 分） · 🔥 0
3. Oct 24
   Local Qwen3.8 Flash Next Used to Create Remotion Motion Graphics

   Reddit user /u/rosie254 reported creating a motion-graphics video with locally run Qwen3.8 Flash Next and Remotion, the same animation framework associated with viral Claude Opus 5.5 motion-graphics posts. The creator shared the conversation history used for the project and said it required multiple prompts, refinement, and steering rather than a single prompt. The demonstration was not independently verified or benchmarked.

   r/LocalLLaMA · 🔥 0
4. Oct 22
   Developer claims unreleased ToRc engine runs ~100GB Qwen model on two £300 ARM mini PCs

   A developer posting on r/LocalLLaMA says an inference engine he is writing, codenamed ToRc and not yet released, reached 315 tok/s prompt read and 43 tok/s generation with Qwen3.6 35B A3B across two Snapdragon X mini PCs joined by a single USB4 cable, hardware he says cost about £600 in total. The same post reports 46 tok/s read and 20 tok/s generation for a roughly 95GB Qwen3.8 Flash Next IQ4_NL quant on the two boxes, versus 10.9/5.8 tok/s for llama.cpp over RPC, and 34.3/18.3 tok/s for a larger UD-Q4_K_XL quant. The author, who previously contributed ARM porting work to the Janas engine, says ToRc uses CPU cores plus the Hexagon NPU with no GPU involved; all figures are his own measurements and have not been independently verified.

   r/LocalLLaMA · 🔥 0
5. Oct 19
   prime-rl 0.10.0 releases with IPO default and unified evals

   Prime Intellect released prime-rl 0.10.0 and verifiers 0.4.0, making the new INTELLECT Policy Optimization (IPO) loss the default for asynchronous reinforcement learning. The release adds optimized model support, including DeepSeek V4 Flash, Qwen3.8 Flash Next and Nemotron-H, and consolidates evaluation into prime-rl. Prime Intellect reports that prime-rl was 1.15 times faster than torchtitan in its Qwen3-235B-A22B supervised fine-tuning comparison, though the benchmark details come from the vendor.

   Prime Intellect Blog · 🔥 0
6. Oct 17
   Five devices chained to run Qwen3.8 Flash Next at 37–39 tok/s on 60k context

   r/LocalLLaMA user StayLameBro says he chained two gaming PCs (an RTX 2060 Super and an RX 9060 XT), an M4 Pro MacBook and two iPhone 17 Pro Maxes over Ethernet and USB-C to serve Qwen3.8 Flash Next, which he describes as a 48-layer mixture-of-experts model with 512 experts per layer. The self-measured decode rates are about 42 tok/s at 16k context, 39–41 tok/s at 32k and 37–39 tok/s at 60k, with prompt reading around 850–900 tok/s on long prompts; he credits pipelined speculation for keeping the five devices from idling, landing about 2.2 tokens per step. The author says the current layout serves 69.6k context, capped by the second PC's 16GB, that a second layout read a single prompt past 164k tokens, and that a full 262k layout is still pending; the code is described as coming to a repo shortly.

   r/LocalLLaMA · 🔥 0

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