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The post consolidates a set of paper-backed, tiered mitigations that, if implemented in runtimes like `llama.cpp` or `vLLM`, could close the gap between DiffusionGemma's naive inference quality and autoregressive models like Qwen without waiting for official tooling support.
Developers running local models should evaluate whether their agent scaffold — not just the model itself — is the bottleneck, as `little-coder` demonstrates that the right harness can close much of the gap between local and cloud model coding performance.