arXiv:2605.26628v1 Announce Type: new Abstract: This report describes Tail-Aware HiFloat4, our submission to the low-bit text-to-video generation quantization challenge. Our method adapts the public ViDiT-Q post-training quantization pipeline to Wan2.2 under the HiFloat4 numerical format. We quantize the main linear layers in both Wan2.2 transformer modules with W4A4 HiFloat4 fake quantization, keep numerically sensitive boundary modules in high precision, and introduce an activation-tail-aware percentile calibration module for channel-mask construction. Together with compact PTQ-state restora
This report details a new, more efficient quantization method for large language models, addressing the critical need for cost-effective AI deployment. The timing aligns with the industry-wide focus on optimizing AI models for broader accessibility and reduced operational overhead.
Advanced quantization techniques like HiFloat4 are crucial for democratizing access to powerful AI models by significantly reducing their computational and memory footprints. This enables deployment on a wider range of hardware, including edge devices, and lowers the economic barrier for AI development and application.
The ability to run sophisticated text-to-video generation models more efficiently through W4A4 quantization shifts the landscape towards more accessible and scalable AI applications. This potentially accelerates the adoption of these models in various sectors by making them cheaper to operate.
- · AI developers
- · Cloud providers
- · Edge device manufacturers
- · AI-driven content creators
- · Companies relying on high-cost, high-compute AI solutions
- · Legacy hardware manufacturers
Reduced inference costs and increased deployment flexibility for text-to-video AI models.
Accelerated innovation in AI applications due to lower barriers to entry and experimental costs.
New business models emerging around highly optimized, resource-efficient AI services.
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Read at arXiv cs.AI