InfoWorld

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale. High inference latency and ...

What sudoku reveals about the limits of LLMs

The world's most advanced AI models can't solve Sudoku. That matters.
MarketWatch

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Through a combination of the Memory Sparse Attention mechanism, Document-wise RoPE for extreme context extrapolation, KV Cache Compression with Memory Parallelism, and a Memory Interleave mechanism ...
VentureBeat

Bigger isn't always better: Examining the business case for multi-million token LLMs

The race to expand large language models (LLMs) beyond the million-token threshold has ignited a fierce debate in the AI community. Models like MiniMax-Text-01 boast 4-million-token capacity, and ...
Morningstar

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

The research introduces a novel memory architecture called MSA (Memory Sparse Attention). Through a combination of the Memory Sparse Attention mechanism, Document-wise RoPE for extreme context ...