GPU guides
Plain-English, in-depth explainers on GPU cloud pricing, hardware choices, and cost planning for AI workloads. All figures are illustrative examples for education.
How GPU Cloud Pricing Works: On-Demand vs Spot vs Reserved
The three pricing models every GPU cloud uses, when each one makes sense, and how billing actually works — with illustrative examples.
Read guide →A100 vs H100 vs L40S, Explained for Beginners
What separates NVIDIA's most popular data-center GPUs, and which one fits training, fine-tuning, or inference.
Read guide →How to Estimate GPU Hours for Training and Fine-Tuning
A practical framework for turning model size, dataset size, and epochs into a GPU-hour budget, with a worked example.
Read guide →Multi-GPU Setups: NVLink, Scaling, and When You Need More Than One GPU
Data parallelism, NVLink vs PCIe, scaling efficiency, and how to tell when a single GPU is enough.
Read guide →Cost-Saving Strategies for Indie AI Developers
Spot instances, right-sizing, quantization, efficient fine-tuning, and other ways to stretch a small GPU budget.
Read guide →Glossary of GPU Terms: VRAM, TFLOPS, Tensor Cores, and More
Plain-English definitions of the jargon you'll meet when shopping for GPU compute.
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