Per-Axis Weight Deltas for Frequent Model Updates

• URL: http://arxiv.org/abs/2512.19720v1
• Date: Tue, 16 Dec 2025 16:46:28 GMT
• Title: Per-Axis Weight Deltas for Frequent Model Updates
• Authors: Stefan Kuyumdzhiev, Radostin Cholakov,
• Abstract summary: We propose a simple 1-bit delta scheme that stores only the sign of the weight difference together with lightweight per-axis (row/column) FP16 scaling factors.<n>This design preserves the compactness of 1-bit deltas while more accurately capturing variation across weight dimensions.
• Score: 0.4552848064814397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Serving many task-specialized LLM variants is often limited by the large size of fine-tuned checkpoints and the resulting cold-start latency. Since fine-tuned weights differ from their base model by relatively small structured residuals, a natural approach is to represent them as compressed deltas. We propose a simple 1-bit delta scheme that stores only the sign of the weight difference together with lightweight per-axis (row/column) FP16 scaling factors, learned from a small calibration set. This design preserves the compactness of 1-bit deltas while more accurately capturing variation across weight dimensions, leading to improved reconstruction quality over scalar alternatives. From a systems perspective, a streamlined loader that transfers packed deltas in a single operation per module reduces cold-start latency and storage overhead, with artifacts several times smaller than a full FP16 checkpoint. The method is drop-in, requires minimal calibration data, and maintains inference efficiency by avoiding dense reconstruction. Our experimental setup and source code are available at https://github.com/kuiumdjiev/Per-Axis-Weight-Deltas-for-Frequent-Model-Updates.

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