Unverified50% confidenceBenchmarkExact time
VLCQ was experimented on three types of tasks: image classification, segmentation, and object detection; in per-channel quantization it surpassed FP32 precision and PWLQ on most models, achieving up to 6x compression compared to FP32 and up to 1.7x compression compared to PWLQ.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
UnverifiedWhen compression priority is higher and limited precision loss is tolerated, VLCQ can achieve up to 12.25x compression compared to FP32, with performance degradation kept within 4%.77% similarUnverifiedWhen processing every frame of a 30fps video with a VLM, the processing speed of a pure VLM solution may be 10 to 100 times slower than real-time requirements.65% similarVerifiedQuantization technology compresses model weights from FP32/FP16 to low-precision formats such as INT8/INT4, which can reduce model size by 50%–75%.65% similarUnverifiedQ3 quantization refers to compressing model weights from FP16 format to an integer representation averaging approximately 3 bits.64% similarUnverifiedMoRI（Mixture of Routed Inference）量化通信策略在 dispatch 阶段使用 FP4 精度，combine 阶段使用 FP8 精度，实现了 2.56 倍的带宽压缩62% similar
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