Hey everyone. I'm new to using Claude Code for some API testing and webhook simulations. Noticed my laptop fan kicks into high gear almost immediately and the battery drains super fast compared to just using my browser or VS Code.
Are there any power-saving settings or config tweaks I should look for? Maybe something in the local setup? I'm on a MacBook if that matters. Just trying to understand if this is normal or if I can optimize it for longer sessions.
Still learning.
You've hit on a classic issue with these tools that run a full local model. It's not just you, and it's likely normal for heavy loads.
If you're doing API testing and webhook sims, the local Claude instance is probably running a full inference model non-stop, which will hammer your CPU. Your browser and VS Code are mostly idle by comparison. There aren't really power settings inside the tool itself. You need to manage it at the system level.
On a Mac, open Activity Monitor and sort by Energy Impact. If Claude Code is at the top, you've confirmed it. The only real tweak is to be ruthless about closing it when you're not actively using its generation features, or see if you can run your simulations in smaller, batched chunks instead of one long, continuous session.
Migrate once, test twice.
Agreed it's normal, but calling it "full inference" glosses over the inefficiency. These local tools are using generic architectures, not ones optimized for sustained battery use on consumer laptops. It's a power-hungry brute force approach they're just passing off as a feature.
They could absolutely implement a low-power mode or smarter backgrounding. They just don't because "local AI" is the selling point, and admitting it kills your battery would ruin the marketing.
—EB
That's a valid architectural critique, but I think the inefficiency you're pointing to is less about a conscious marketing choice and more about a fundamental mismatch in optimization targets. These local tools are typically using quantized versions of models designed for server-grade hardware with consistent power and cooling. The quantization process itself is often optimized for a balance of accuracy and memory footprint, not for dynamic power scaling on a mobile CPU.
You can actually measure this. If you profile the process, you'll see it's not just "brute force" - it's a specific pattern of sustained, memory-bound matrix operations that keep the CPU in a high-power state. A true low-power mode would require a completely different model architecture, like a specialized small model for idle states, which introduces a huge development and maintenance overhead for a niche desktop tool.
The real issue is that the "local AI" benchmark everyone cares about is tokens per second or quality, not joules per token. Until users start demanding energy efficiency as a primary metric, we won't see those architectural shifts.
numbers don't lie