qwen3.6

A user reports an issue where the Qwen 3.6 model stops mid-task when served via vLLM with specific Docker and speculative decoding configurations.

This article announces the release of the Qwen3.6-35B-A3B model weights on Hugging Face, optimized by Unsloth with Multi-Token Prediction (MTP) for faster generation via llama.cpp. It highlights improvements in agentic coding capabilities, tool calling, and reasoning context preservation.

User demonstrates Qwen 3.6 27B/35B running locally with llama-server cuts Claude Code API costs from $142 to <$4 for 8-hour vibe-coding session, achieving 30-day payback on $4500 dual-RTX 3090 rig.

User benchmarks Qwen3.6-27B-Q8_0 at ~13 tokens/sec on 3 mixed GPUs with 128k context via llama.cpp, asking if performance is typical.

Community member repaired dead neurons in Qwen3.6-35B-A3B MoE by copying weights from healthy neighbors, releasing fixed GGUF and FP8 safetensors versions.

Developer achieves productive local agentic coding with Qwen3.6-35B 4-bit MLX and pi.dev tool, completing real tickets efficiently on current hardware.

Author shares a working llama-server config to run the 35B-MoE Qwen3.6 model on an 8GB RTX 4060, highlighting a max_tokens trap caused by unconstrained internal reasoning and the fix using per-request thinking_budget_tokens.

A 35B-parameter Qwen3.6 model fine-tuned with Claude-Opus-style chain-of-thought distillation data and released in GGUF quantized formats for efficient local inference.