Abstract: Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives rise to ...

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...

Introduces a low-rank-based approach to KV cache compression, one of the key bottlenecks in long-context AISpeeds up attention computation by up to 6.9x and overall generation throughput by up to 3.1x ...

KV, a low-rank KV cache compression method achieving up to 20x reduction, with the paper selected as a Spotlight at ICML 2026 ...

SEOUL, South Korea, June 11, 2026 /PRNewswire/ -- Nota AI, a company specializing in AI model compression and optimization, announced that two of its papers on MoE-specific quantization algorithms ...

Image courtesy by QUE.com As we navigate the landscape of 2026, we find ourselves no longer merely using Machine Learning (ML) but ...

Abstract: Modern datasets often exhibit heavy-tailed behavior, while quantization is inevitable in digital signal processing and many machine learning problems. This paper studies the quantization of ...

Two papers on MoE-specific quantization algorithms accepted at a workshop held in conjunction with ICML 2026 Recognition follows Nota AI's overall win at the NVIDIA Nemotron Hackathon Strengthening ...

Two papers on MoE-specific quantization algorithms accepted at a workshop held in conjunction with ICML 2026 Recognition follows Nota AI’s overall win at the NVIDIA Nemotron Hackathon Strengthening ...