Choosing a GPU
The fastest card is rarely the right one. What matters is whether your workload fits in its memory, and whether the rest of the box can feed and cool it.
Memory first
For most GPU work — especially model inference and training — the first question is whether your model and batch fit in the card's memory. A model that spills out of VRAM is slow no matter how fast the card is. Size the memory to the workload before you look at throughput.
Then throughput, for the right precision
Training rewards raw compute and fast interconnect between cards; inference often runs happily at lower precision on a more modest card. A local LLM serving a team is a different spec from a research training rig. Tell us the model and the job, not just "a GPU".
Power, cooling and sharing
Big cards draw serious power and throw real heat, so the chassis, power and cooling have to suit them — this shapes the whole build, see GPU servers. Get the drivers right, and if several jobs will share one card, sharing a GPU covers how.
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