GPU Selection

Picking a GPU is the first decision that locks in your cost curve for months. This cluster covers every angle of that decision: NVIDIA generation-to-generation comparisons (Hopper vs Blackwell vs Rubin), workload-specific rankings for LLM inference, image generation, video generation, and computer vision training, and the non-NVIDIA options worth knowing about, from AMD's MI300X to Google's TPU line to newer inference ASICs like Groq's LPU and Etched's Sohu.

The guides here answer a narrow question well rather than a broad one badly. If you're choosing between an H200 and a B200 for a 70B inference workload, or trying to figure out whether an RTX 5090 has enough VRAM for your fine-tuning job, you'll find a spec table and a real benchmark number, not marketing copy. The pillar post, Best GPU for AI Inference in 2026, is the place to start if you don't know which comparison you actually need: it walks through L40S, H100, H200, and B200 with tokens-per-second-per-dollar numbers and a decision framework you can apply to your own workload.

This is for anyone about to sign a GPU rental agreement who wants to know, concretely, why one card costs more than another and whether that premium buys anything for their specific job.