{ "schema_version": 1, "builder_plan": { "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.", "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.", "baseline": "The same model at its original precision, on the same GPU.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. 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This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.39223", "title": "QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs", "summary": "The method replaces post-training quantization (PTQ) with quantization-aware distillation that simulates the deployment format while computing in BF16, so no FP4-native training hardware is needed and checkpoints export directly to vLLM or llama.cpp. The cost is a training run with a teacher model, a token budget that favors long sequences, and format-dependent hyperparameter choices (weights-only fake quantization for NVFP4 versus weights-plus-activations for MXFP4), plus engineering complexity beyond a one-shot PTQ pass. 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