Quantizing Qwen 3.6 35B MoE to Q4_K_M in Ollama does reduce memory and make local inference more accessible, but it does trim quality. Across coding, multilingual, and vision tests, the full precision model produced more accurate and complete outputs, while the quantized model delivered roughly about 85 percent of the quality at a fraction of … Read more
Here is the direct path to get a local agentic coder running with my stack: serve Qwen 3.6 27B with vLLM on Ubuntu, install OpenClaw, point OpenClaw to the same local endpoint, then prompt it to read your backend and frontend code, identify bugs, apply fixes, and output the exact run commands. I ran this … Read more
Qwen 3.6 27B runs locally on Ubuntu with a single NVIDIA A100 80 GB by logging in to Hugging Face, starting a vLLM OpenAI-compatible server with the right context length, and serving on port 8000. With default settings the full model occupies about 74 GB of VRAM, so keep the max context at 32k if … Read more