Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit

LCLMs compress LLM context before decode — 8.8x faster at 16x compression, beating every KV cache method tested. Open-sourced by NYU and Columbia.
Morning Overview on MSN

Google unveiled TurboQuant, a method that cuts the memory bottleneck slowing large AI models

Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during ...
The Manila Times

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

Two papers on MoE-specific quantization algorithms accepted at a workshop held in conjunction with ICML 2026Recognition ...
scmp.com

DeepSeek unveils multimodal AI model that uses visual perception to compress text input

DeepSeek on Monday released a new multimodal artificial intelligence model that can handle large and complex documents with significantly fewer tokens – the smallest unit of text that a model ...
Ars Technica

Running local models on Macs gets faster with Ollama’s MLX support

Ollama, a runtime system for operating large language models on a local computer, has introduced support for Apple’s open source MLX framework for machine learning. Additionally, Ollama says it has ...