Why harnesses matter for energy (and token) efficiency
Neuralwatt researchers are analyzing how different agent harnesses impact GPU energy efficiency and token processing costs. The study highlights that prompt caching is the most significant factor in reducing computational overhead for AI coding agents.
Why it matters
Optimizing AI agent interactions is critical for reducing the massive energy consumption and operational costs associated with large-scale language model deployment.
At Neuralwatt, we can see how the agent harness calling a model affects the energy it uses. I've been looking through a month of traffic on our cloud, and the harness seems to decide a surprising share of how much work our GPUs do. We usually talk about inference efficiency in terms of the model (how many active parameters, its attention mechanism) or the hardware (H200 vs B200 vs B300). The client deserves a closer look.
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