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Deep Dive 2: The Technical Requirements for Liquid Training

What must be technically true for liquid training to work

The Economics of Liquid Training

In this piece, we lay out the shape of the market today, how different compute workloads create value, how the capital model for GPUs works, and how liquid training on IOTA will drive us toward better economic outcomes.

The Case for Liquid Training

Thesis Compute is the limiting reagent of modern intelligence.

“The Middle Point Between Researcher and Engineer” – Alan Aboudib on AI, Bittensor, IOTA, and the Future

An interview with Dr Alan Aboudib, AI Research Lead at Macrocosmos

Orion-100B: Distributed pretraining arrives at hundred-billion-parameter scale

Author: Dr. Steffen Cruz

The IOTA Simulator, on Apex

New SN1 Competitions Designed to Accelerate Distributed Training

When Does Compression Improve Transfer Latency?

Results from Apex's matrix compression competitions

How to Use Social Media Data in Your Marketing Strategy - Three Tips

Incorporating SN13, Data Universe, into your marketing stack

The Envelope-Weight Trick How a Matrix Compression Competition Turned Into a Mini Red-Team - and What We’ll Carry Into IOTA (SN9)

A community data story from Apex (Subnet 1)

Welcome to Train at Home

SN9’s Train at Home is available to all. Here’s what you need to know.