AI & LLM Index
Glossary · Hardware and infrastructure

What is GPU?

GPU is a hardware or infrastructure concept in AI systems. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.

Definition

GPU is a hardware or infrastructure concept in AI systems. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system. The term is used in hardware and infrastructure discussions, where precision matters: two teams can use the same word to mean slightly different things. In this glossary, gpu is treated as a practical concept rather than a formal specification.

Why it matters

Infrastructure terms describe the memory, compute and serving choices behind a model. They explain why the same model can have very different cost and latency profiles across providers.

Example

Suppose you are comparing Llama 3 DeepInfra Base with another model for a production task. GPU is one of the factors behind the numbers you see: Llama 3 DeepInfra Base lists a 16,384-token context window, costs $2.95 per million input tokens and scores 77.0 on TruthfulQA. Understanding the term helps you decide which of those differences actually matter for your workload.

How to evaluate it

  • Check whether gpu is defined the same way in the documentation you are reading.
  • Ask which measurement or trade-off the term is describing before comparing numbers.
  • Separate vendor claims from independently observed behaviour on your own data.
  • Write down the definition your team uses so reviews and evaluations stay consistent.

Related terms

TPUHardware and infrastructure NPUHardware and infrastructure LPUHardware and infrastructure HBMHardware and infrastructure VRAMHardware and infrastructure Memory bandwidthHardware and infrastructure

Related guides

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FAQ

What does GPU mean?

GPU is a hardware or infrastructure concept in AI systems. It is part of the vocabulary teams use when they compare models, design prompts or review an AI system.

Why does GPU matter?

Infrastructure terms describe the memory, compute and serving choices behind a model. They explain why the same model can have very different cost and latency profiles across providers.

How is GPU used in practice?

Suppose you are comparing Llama 3 DeepInfra Base with another model for a production task. GPU is one of the factors behind the numbers you see: Llama 3 DeepInfra Base lists a 16,384-token context window, costs $2.95 per million input tokens and scores 77.0 on TruthfulQA. Understanding the term helps you decide which of those differences actually matter for your workload.