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  1. r/LocalLLaMACommunity

    Six-way benchmark compares Qwen3.8-Flash-Next locally against Claude Opus 5.5 on a Rust feature

    A community test of 12 PRs on a LlamaStash feature found Opus 5.5 averaged 82% against 68% for Claude Code with Flash-Next and 66% for Pi with Flash-Next. Flash-Next ran locally at about 4.16 bpw, so the author paid only electricity, and results are a single developer's unverified experiment.

  2. r/LocalLLaMACommunity

    Ornith-1.5-122B, a Qwen-based finetune, released in GGUF and NVFP4 formats

    Ornith AI introduces Ornith-1.5, which continuously generates its own training tasks and improves through reinforcement learning, extending Ornith-1.0 built on Qwen3.5 and Gemma4. GGUF and NVFP4 builds are on Hugging Face, with more details in the company blog.

  3. r/LocalLLaMACommunity

    ISTA-DASLab releases a Qwen3.8-27B prefiller model that streams from SSD for low-quant accuracy

    The demo recovers accuracy for low-bit quantized Qwen3.8-27B while keeping VRAM usage low and decoding fast. It reportedly needs an Nvidia Blackwell GPU, and the poster has not tested it. For context longer than about 4k tokens, the poster expects no speed penalty.

  4. r/LocalLLaMACommunity

    Tess-4.5-27B, a Qwen finetune, claims 25% fewer tokens at the same score

    Community finetune by migtissera built on Qwen3.8-27B, with GGUF files on Hugging Face. The author reports 25% fewer tokens across 150 BenchLocal scenarios and 43% fewer on data extraction, with the same scores claimed.