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Qwen3.8

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Headlines · 6

  1. Temperature Sweep Links Low Decoding Settings to Worse Coding

    A Reddit author tested Qwen3.8 in IQ4_XS-M quantization on 100 hard LiveCodeBench v6 problems while varying only the decoding temperature. Pooled coding accuracy rose from 70.0% at temperature 0 to 82.7% at 1.0, while the loop rate fell from 9% to 0.3%. The author also reported that lower temperatures did not reduce performance on GPQA Diamond knowledge questions.

    r/LocalLLaMA · 🔥 2
  2. Cohere releases Qwen3.8 investigation artifacts for independent testing

    Cohere released investigation traces, raw tokens, and a minimal reproduction script for strange links reportedly generated by Qwen3.8. Cohere said it also observed the behavior without prompt prefilling, but described that occurrence as rare, and said developers could use prompt prefilling to reproduce it. Developers running Qwen3.8 locally first spotted the links, while the open weights allowed Cohere to investigate the behavior in depth.

    cohere (X) · 🔥 0
  3. Coding-Agent Harness Changes Raise Costs Without Reliable Gains

    Shandong University researchers Liu and Han compared Claude Code, OpenCode, and mini-SWE-agent while holding the language model constant. Most performance differences were similar to rerun noise, while the harnesses consumed two to three times as many tokens as the leanest option; OpenCode showed a significant deficit on the larger task pool. A small cross-check on Terminal-Bench 2.1 showed the same pattern.

    The Decoder · 🔥 0
  4. Zaggita GPU unlock boosts prefill on Chinese RTX 3080 20GB

    A Reddit user reports that Zaggita’s GPU performance unlock works on Chinese RTX 3080 20GB cards. In the poster’s vLLM test on two GPUs, prefill throughput rose about 20.8%–28.6% while decode steps per second showed no apparent change; the characterization of the unlock as removing an NVIDIA AI-compute restriction was not independently confirmed.

    r/LocalLLaMA · 🔥 0
  5. Surface Laptop Ultra hands-on: RTX Spark N1X, 128GB unified memory and local LLM tests

    After Microsoft's Windows and Surface event in San Francisco on October 7, ifanr obtained the Surface Laptop Ultra and tested it. The machine runs Nvidia's RTX Spark N1X platform, which Nvidia says offers up to a 20-core Grace CPU, a Blackwell GPU with 6,144 CUDA cores and up to 128GB of unified memory, with a claimed 1 PFLOPS of sparse FP4 AI compute; the Surface Laptop Ultra and the first wave of RTX Spark OEM machines opened for preorder and go on sale October 16. Running Qwen3.5 35B-A3B at Q4 quantization locally via Ollama and Open WebUI, the reviewer measured about 59.65 tokens per second of sustained generation, and about 20.98 tokens per second for the denser Qwen3.8 27B after warm-up; a local version of WorkBuddy can use on-device compute for agent tasks, some of them offline, but it does not yet switch automatically between local and cloud models. The laptop has a 15-inch 3:2 Mini LED touchscreen with 2000-nit peak HDR brightness, is about 18mm thick and 2.0kg, and starts at 21,988 yuan for 24GB of memory and 512GB of storage, rising to 48,388 yuan for the 128GB top configuration; Asus, Lenovo, MSI, Dell and HP also announced RTX Spark models.

    爱范儿 · 🔥 0
  6. Ollama v0.40.0 defaults to MLX on Apple Silicon

    Ollama v0.40.0 makes MLX the default runtime for supported model architectures on Apple Silicon. The release also adds MLX support for decision models and the EmbeddingGemma 2 embedding model, with additional model families listed in the release notes.

    Ollama Releases · 🔥 0

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