# Nota AI Has Two MoE Quantization Papers Accepted at ICML 2026 Workshop, Demonstrating Global Competitiveness in Large-Scale AI Optimization

- Link: https://www.thailand-business-news.com/pr-news/nota-ai-has-two-moe-quantization-papers-accepted-at-icml-2026-workshop-demonstrating-global-competitiveness-in-large-scale-ai-optimization
- Published: 2026-06-11T19:00:00+07:00
- Author: PR Newswire

◦ 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 core optimization technologies to make large-scale AI models smaller
   and more efficient to run

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 have been accepted to the Resource-Adaptive Foundation
Model Inference (AdaptFM) Workshop at ICML 2026, one of the world’s leading machine
learning conferences.

 [ ⌊Nota AI Has Two MoE Quantization Papers Accepted at ICML 2026 Workshop, Demonstrating Global Competitiveness in Large-Scale AI Optimization⌉ ](https://mma.prnasia.com/media2/2996619/NotaAI_ICML_Accept_PR.html)

Nota AI Has Two MoE Quantization Papers Accepted at ICML 2026 Workshop, Demonstrating
Global Competitiveness in Large-Scale AI Optimization

ICML is widely recognized as one of the premier global conferences in machine learning
and artificial intelligence, bringing together the latest research from global technology
companies, leading universities, and major research institutions. The AdaptFM Workshop
focuses on technologies that enable large-scale AI models to run efficiently under
limited computing resources. Researchers from global companies and research institutions,
including Amazon and Meta, serve on the organizing committee, while researchers 
from leading AI companies such as NVIDIA, Qualcomm AI Research, OpenAI, Apple, and
Microsoft are also participating as members of the program committee.

This achievement is significant as it recognizes Nota AI’s accumulated technical
expertise in optimizing Mixture-of-Experts (MoE) models, an architecture increasingly
regarded as a core structure for large language models (LLMs). MoE models improve
both performance and efficiency by activating only a subset of expert models as 
needed. However, their complex structure requires a different approach to quantization,
the process of making models smaller and more efficient, compared to conventional
model architectures.

Nota AI previously won both its track and the overall competition at the NVIDIA 
Nemotron Hackathon with a data-driven MoE quantization method. With the acceptance
of these two papers, Nota AI will once again present research outcomes specifically
designed for MoE architectures on a global research stage.

The first accepted paper, "DREAM-MoE," proposes a method to reduce changes in a 
model’s decision flow that can occur when large-scale AI models are quantized across
multiple segments. The method focuses on the fact that even a small error in an 
earlier segment can affect expert selection in later segments. DREAM-MoE helps the
quantized model select experts in a way that remains closer to the original model.

The second paper, "SRA-MoE," proposes a method that identifies and prioritizes important
inputs that have a greater impact on the model’s final output. Rather than treating
all inputs equally, SRA-MoE is designed to prevent expert selection from being significantly
disrupted for these key inputs, helping maintain model quality more effectively 
under limited resources.

Both studies demonstrated higher performance compared to the latest MoE-specific
quantization methods. This shows that large-scale AI models can be executed with
less memory and fewer computing resources while reducing quality degradation. As
the cost, power consumption, and hardware burden of running large AI models continue
to increase, MoE-specific quantization technologies are becoming increasingly important.

Nota AI has been proactively focusing its R&D efforts on optimizing large AI models
that require substantial memory and computing resources. The company is advancing
large-scale model optimization, including Solar MoE, as part of the sovereign foundation
model project led by the Upstage consortium. It is also expanding its experience
in quantizing NVIDIA Nemotron 3 Nano to newer large models such as Nemotron Ultra,
further broadening the scope of its optimization technologies.

"This paper acceptance reflects Nota AI’s continued advancement of MoE-specific 
quantization technologies," said Myungsu Chae, CEO of Nota AI. "Following our overall
win at the NVIDIA Nemotron Hackathon, we are pleased to present our research at 
the ICML 2026 AdaptFM Workshop. We will continue developing optimization technologies
that enable large-scale AI models to be used more efficiently and practically."

In addition, Nota AI will host "Nota AI – Korea Efficient Days" during ICML 2026
at COEX in Seoul. The event will bring together global researchers, engineers, and
business leaders visiting Korea to share research trends and industrial applications
of Efficient AI. Through the event, Nota AI plans to introduce its research achievements
in large-scale AI model optimization and expand opportunities for technical collaboration
and business engagement.

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