AI & LLM Index
Glossary · Hardware and infrastructure

What is VRAM?

VRAM 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

VRAM 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, vram 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 DeepSeek-Coder 8B with another model for a production task. VRAM is one of the factors behind the numbers you see: DeepSeek-Coder 8B lists a 32,768-token context window, costs $1.26 per million input tokens and scores 51.2 on AIME 2025. Understanding the term helps you decide which of those differences actually matter for your workload.

How to evaluate it

  • Check whether vram 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

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

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FAQ

What does VRAM mean?

VRAM 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 VRAM 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 VRAM used in practice?

Suppose you are comparing DeepSeek-Coder 8B with another model for a production task. VRAM is one of the factors behind the numbers you see: DeepSeek-Coder 8B lists a 32,768-token context window, costs $1.26 per million input tokens and scores 51.2 on AIME 2025. Understanding the term helps you decide which of those differences actually matter for your workload.