Learn Distributed AI Training

What Is Distributed AI Training? A Plain-English Guide (2026)

Distributed AI training explained simply: data vs. model parallelism, FedAvg, communication-efficient methods (DiLoCo), and how a GPU pool trains models across many machines.

How to Train AI Models Cheaply Using Idle GPUs

Practical ways to cut AI training cost: idle/community GPUs, LoRA/QLoRA, spot instances, and distributed GPU pools. Train and fine-tune models for a fraction of cloud prices.

LoRA vs QLoRA: Which Fine-Tuning Method Should You Use?

LoRA vs QLoRA explained: how they work, VRAM needs, speed and quality trade-offs, and when to pick each for fine-tuning LLaMA, Mistral and other LLMs.

Federated Learning: Train AI Across Browsers, Phones and GPUs

How federated learning lets every device — browser, phone, CPU, GPU — help train the same model. What works, what does not, and how a universal small-model pool is built.

GPU Cloud Alternatives in 2026: Marketplaces & Distributed Pools Compared

Comparing ways to get GPUs for AI: hyperscaler clouds, GPU marketplaces (Vast.ai, RunPod), and distributed pools. Costs, trade-offs, and when a pool makes sense.

What Is a GPU Pool? Decentralized Compute for AI, Explained

A GPU pool aggregates many GPUs (and CPUs) into one shared compute network for AI. How it works, how contributors earn, verification, and how it differs from crypto mining pools.