What is Kernel?
Kernel 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
Kernel 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, kernel 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 Yi-Large with another model for a production task. Kernel is one of the factors behind the numbers you see: Yi-Large lists a 32,768-token context window, costs $3.40 per million input tokens and scores 66.2 on AIME 2025. Understanding the term helps you decide which of those differences actually matter for your workload.
How to evaluate it
- Check whether kernel 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
Related guides
FAQ
What does Kernel mean?
Kernel 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 Kernel 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 Kernel used in practice?
Suppose you are comparing Yi-Large with another model for a production task. Kernel is one of the factors behind the numbers you see: Yi-Large lists a 32,768-token context window, costs $3.40 per million input tokens and scores 66.2 on AIME 2025. Understanding the term helps you decide which of those differences actually matter for your workload.