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Reddit user runs Qwen 3.8 Flash Next locally on an 8GB GPU under CachyOS
The poster reports about 30 tokens per second on average and bursts up to 48 tokens per second, using a 3-bit Unsloth quant with a 42k context that nearly exhausts VRAM.
Intel Arc Pro B65 four-GPU rig posts long-context throughput sweeps for Qwen3.8 models
8 Flash-Next and 27B models on 1x to 4x Intel B65 setups with vLLM, reporting output throughput across context lengths up to 200K. Results include queueing effects and are not model-quality scores.
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.
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.
Community post shares a Qwen prompt that makes the model draft and refine iteratively
A Reddit user reports that adding a fractal draft-test-refine prompt to Qwen 3.8 Flash Next made it faster and more decisive in their own projects. The claims are anecdotal and no benchmarks are given.
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.
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.