{ "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. Review regressions before expanding the test." ], "measure": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.07904", "title": "ApexQuant: Data-Free Elastic Quantization by Residual Re-Isotropization", "summary": "ApexQuant replaces calibration-data-dependent post-training quantizers with a calibration-free residual re-quantization scheme that stacks scalar, E8, or trellis stages so one artifact serves multiple bit-widths. It costs extra passes of encoding and decoding (latency and storage of multiple residual codes per layer), adds implementation complexity on top of an existing quantizer, and trades a small quality gap versus full precision. 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