arXiv:2605.29843v1 Announce Type: new Abstract: Post-training quantization (PTQ) is essential for deploying LLMs under memory and bandwidth constraints. However, extreme low-bit quantization remains highly sensitive to activation outliers and anisotropic weight curvature. Existing incoherence-based PTQ methods mitigate this issue with fixed randomized Hadamard transforms (RHTs), which improve quantization robustness but cannot adapt the rotated basis to the layer, calibration distribution, or quantizer. We introduce HARP (Hadamard-preconditioned Adaptive Rotation Processor), a learnable struct
The increasing scale of LLMs necessitates more efficient deployment strategies, making post-training quantization a critical area of research for practical implementations.
This development allows for more efficient deployment of large language models on resource-constrained hardware, expanding their accessibility and applications without significant performance degradation.
Extreme low-bit quantization for LLMs becomes more robust and adaptable, potentially reducing the memory and computational footprint required for inference.
- · AI hardware manufacturers
- · Edge AI developers
- · Cloud providers offering LLM services
- · Developers of resource-constrained AI applications
- · Companies reliant solely on high-compute LLM deployment models
Widespread deployment of larger, more complex LLMs on consumer devices and edge infrastructure becomes more feasible.
Increased competition and innovation in the AI hardware and software optimization space as new deployment paradigms emerge.
The proliferation of more sophisticated AI applications embedded directly into everyday objects and local systems, reducing dependence on continuous cloud connectivity.
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Read at arXiv cs.LG