Qwen3.5-122B hits performance ceiling on Apple Silicon

Qwen3.5-122B-A10B is the new heavyweight champion for local inference, but it demands specific software stacks to shine.

• –10 tok/s on llama.cpp is the expected floor for a model of this scale; MLX is required to hit the 40+ tok/s ceiling on M4 Max.
• –Performance degradation at 50k+ context points to KV cache overhead and memory pressure, common in MoE models with large context windows.
• –128GB of Unified Memory is the minimum requirement for 4-bit quants; any higher precision or context quickly triggers memory swap.
• –Users seeking interactive coding speeds should pivot to the 27B dense variant or prioritize the MLX framework over traditional GGUF backends.