Qwen Exposes Local AI’s Template Bottleneck

Local AI is fast enough now that compatibility—not compute—is becoming the real product challenge.

• –Qwen’s reasoning controls depend on template behavior, making `enable_thinking` and special-token handling deployment-critical.
• –MLX, Transformers, llama.cpp, and LM Studio can interpret Jinja templates differently, producing inconsistent behavior from identical weights.
• –Incorrect templates often fail silently, so a model may appear slow or unintelligent when the real issue is prompt serialization.
• –Model repositories should ship tested templates, runtime compatibility notes, and prompt-rendering fixtures.
• –Developers should pin model, tokenizer, and runtime versions, then inspect rendered prompts and token IDs before benchmarking.