User reports character encoding issues with LM Studio, Zoo, and Qwen 3.6 pipeline
A user running an Unsloth quant of Qwen3.6-35B-A3B on a Legion 7i laptop with a 5080 GPU reports two issues in the LM Studio to Zoo (formerly Roo) pipeline: character encoding mismatches causing em-dashes to appear as random characters, and a second unspecified problem. The user achieves ~80 tk/s by offloading all experts to CPU.
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User benchmarks 4x RTX 5060 Ti with SGLang for Qwen3.6 27B, finds better concurrency than vLLM
A user shared benchmark results showing that SGLang handles higher concurrency better than vLLM when running Qwen3.6 27B (INT8 with bf16 KV cache) on a 4x RTX 5060 Ti (64GB VRAM) setup. The test achieved 200 successful requests at 8 concurrency over 348.87 seconds, processing 61,870 input tokens and generating 44,525 tokens. This provides a practical reference for others considering multi-GPU configurations with these consumer cards.
Users report tool-call failures and looping in Qwen3.6-27B
A Reddit user reports persistent tool-call failures and looping behavior when using Qwen3.6-27B as a local model. The user describes needing constant monitoring to prevent the model from entering infinite loops or hallucinating tool calls, and has developed an extensible workaround to mitigate these issues.
Voodoo Quant claims 95% KLD improvement over Unsloth Dynamic 2.0 on Qwen3.5 models
A developer released two new GGUF quantizations of Qwen3.5 0.8B and 2B using a technique called Voodoo Quant, which optimizes mixed precision by assigning higher precision to more important parts of the model. The author claims Voodoo Quant beats Unsloth Dynamic 2.0 by 95% in Kullback-Leibler divergence (KLD). The quantized models are available on Hugging Face.
User benchmarks show 2x throughput gain from parallel agents on RTX 5090 with Qwen 3.6 35B
A Reddit user benchmarked LM Studio with Qwen 3.6 35B on an RTX 5090, finding that running at least 4 parallel agents doubles tokens/second compared to a single agent. The test with 8 configured parallel tasks showed significant throughput improvement, suggesting developers using agent frameworks like Open Code should parallelize to maximize performance.
User seeks to extend Qwen 3.6 27B context window beyond 100k tokens
A user reports running Qwen 3.6 27B (Q8_0) at 100k context length but finds reliability insufficient. They ask the community for techniques beyond KV cache quantization to improve stability at longer contexts.