Performance engineering
Optimizing software speed, scalability, and resource use.
Performance engineering is about making computer programs run as fast and efficiently as possible. This means using less computer power, memory, and time to get the job done.
For example, imagine a website that loads instantly versus one that takes a minute to show up. Performance engineering is the work that makes the first website load so quickly, even if many people are using it at the same time.
Performance engineering is a software engineering discipline focused on ensuring systems meet their performance requirements under expected and peak loads. It involves analysis, tuning, and testing to optimize metrics such as latency, throughput, scalability, and resource utilization (CPU, memory, network). Tradeoffs often exist between performance, cost, and development time.
As AI models become more powerful, they also become more resource intensive. Performance engineering is crucial for making these models practical and affordable to run, both for developers and users. Labs are focused on optimizing AI systems to handle more complex tasks and reach more users efficiently.
- Designing AI resistant technical evaluationsAnthropic · Anthropic Engineering ·
- Anthropic Claude
- Anthropic is focused on performance engineering to ensure their AI models are efficient and scalable for a wide range of applications.
- Efficiency
- Efficiency is a broader concept that can include performance but als
- Optimization
- Optimization is a technique used within performance engineering to
The principles of performance engineering have evolved alongside software development, with formalization emerging as systems grew in complexity and scale, particularly in areas like high-performance computing and large-scale web services.
Try sending a very long prompt to an AI assistant and then a very short one. Notice if there's a difference in how quickly you get a response. You can also try asking the same question multiple times in quick succession to see if the response time changes.
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Questions people ask
- Why is performance engineering important for AI?
- AI models, especially large ones, require significant computing resources. Performance engineering makes them faster, cheaper, and more accessible to run.
- What are the main goals of performance engineering?
- The main goals are to make software run faster, handle more users or data (scalability), and use fewer computing resources.
First seen on Subvolts Jan 22, 2026. Updated Sep 14, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.