** "Qwen 3.6 MoE pushes 4GB VRAM limits" - Good headlinese.

Running a 35B MoE model on a 4GB laptop is a "triumph of software over hardware" that highlights the maturity of local LLM quantization and offloading.

• –The `--cpu-moe` flag in llama.cpp is the "secret sauce" here, allowing the 32B non-active experts to sit in system RAM while the GPU handles the 3B active parameters.
• –Context window management is the silent killer—a 60k context in 4-bit consumes more memory than the GPU has total, forcing immediate and severe performance degradation.
• –Importance Quantization (IQ4_NL) preserves reasoning capabilities better than standard 4-bit, but at 35B parameters, the IO overhead of moving data from DDR5 RAM to VRAM is the primary bottleneck, not compute.
• –Users with <8GB VRAM are better served by the dense Qwen 2.5-7B/9B models, which offer higher tokens-per-second and larger usable context windows on consumer laptops.