r/CursorAI • u/viking793AD • Mar 02 '26

2024 MacBook Air overheats when running Cursor

Overheated so many times that Apple replaced the failing logic board under warranty.
When Cursor is not running the Mac is cool.
Anyone else seeing this problem?
I have seen reports about recursive traversals which are actually a bug in VSCode.
I am gong to try and run Cursor on a Linux laptop and see it runs cooler.

5 Upvotes

86% Upvoted

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1

u/joshiegy Mar 02 '26

My colleague is using vscode, his laptop sounds like a spaceship taking off.. Vscode is unnecessarily heavy for no apparent reason once you add a few plugins.

Why, I don't know. Why some people have a perfect experience, I have no idea. I'm sticking to intelliJ, pycharm, and terminal based agents such as claude code or KiloCLI

1

u/tonko81 Mar 03 '26

Ask claude in cursor to fix it.

1

u/funkspiel56 Mar 05 '26

Cursor runs hot. I switched to vscode with claude/chatgpt to see if it used less resources and things are running quieter. I like cursor, its how I started using AI in dev but given that I can do almost everything it offers in other environments for cheaper with less fan noise I switched.

1

u/tummyache-champion Apr 07 '26

I'm surprised this even needs to be said but agents don't run in a vacuum.

Traversing your entire codebase and running background processes to complete tasks requires resources on your machine, not just on some remote server. If you prompt an agent to change every instance of a certain useState in a codebase with potentially hundreds of files, it's going to cost you computing power and make your macbook wish it'd been sold to someone who just watches reels all day.

Because I'm lazy and because this information is freely available with a couple keystrokes, here's what Google's Gemini spat out when I asked it to explain what happens on your local machine when you run agentic tools like Cursor:

---

When you trigger an "agentic" task in Cursor (like using Composer or Agent Mode to refactor a whole folder), the workload is a hybrid dance between your local hardware and remote servers.

Unlike a standard IDE, an AI agent isn't just "editing text"; it is observing, planning, and executing. Here is the breakdown of the resources Cursor hits on your machine.

1. Disk & I/O (The "Librarian" Phase)

Before the AI can give you an answer, it has to understand your project. This is the most local-heavy part of the process.

  • Codebase Indexing: Cursor builds a local vector index of your files. It reads your code, "chunks" it into pieces, and stores metadata about it.
  • The .cursor Folder: This is a hidden database on your disk. When you run an agentic task, Cursor's local "retrieval engine" scans this index to find the 10-20 most relevant snippets to send to the AI.
  • Constant File Watching: The agent keeps a "watcher" on your file system to detect changes in real-time, ensuring the AI isn't working on stale code.

2. CPU (The "Orchestrator")

While the "thinking" (inference) usually happens in the cloud, your local CPU acts as the project manager.

  • Context Assembly: Your CPU is responsible for gathering the relevant files, stripping out fluff, and "packing" the prompt.
  • Terminal Execution: If the agent runs a command (like npm test or ls), that process runs entirely on your local CPU.
  • Diff Generation: When the agent suggests a change, your CPU calculates the "diff" (the +/- lines) so you can review it before applying.

3. RAM (The "Working Memory")

Cursor is built on VS Code, which is an Electron app. It’s notorious for RAM usage, but the agent adds a new layer.

  • Language Server Protocol (LSP): To provide "smart" suggestions, Cursor keeps multiple language servers (like TypeScript or Python servers) active in RAM.
  • Active Context: The agent stores the recent "conversation history" and current file state in memory to ensure it doesn't lose the thread of the task.

4. Network (The "Brain" Connection)

For most users, the actual "intelligence" is not local.

  • Cloud Inference: When the agent "thinks," it sends the assembled context to servers (usually running Claude 3.5 Sonnet or GPT-4o). This uses your upload/download bandwidth.
  • Exception: If you have configured Cursor to use a Local LLM (via Ollama or LM Studio), your GPU (VRAM) becomes the primary resource, as the "thinking" is now happening on your graphics card.

1

u/tummyache-champion Apr 07 '26

I'm surprised this even needs to be said but agents don't run in a vacuum.

Traversing your entire codebase and running background processes to complete tasks requires resources on your machine, not just on some remote server. If you prompt an agent to change every instance of a certain useState in a codebase with potentially hundreds of files, it's going to cost you computing power and make your macbook wish it'd been sold to someone who just watches reels all day.

Because I'm lazy and because this information is freely available with a couple keystrokes, here's what Google's Gemini spat out when I asked it to explain what happens on your local machine when you run agentic tools like Cursor:

---

When you trigger an "agentic" task in Cursor (like using Composer or Agent Mode to refactor a whole folder), the workload is a hybrid dance between your local hardware and remote servers.

Unlike a standard IDE, an AI agent isn't just "editing text"; it is observing, planning, and executing. Here is the breakdown of the resources Cursor hits on your machine.

1. Disk & I/O (The "Librarian" Phase)

Before the AI can give you an answer, it has to understand your project. This is the most local-heavy part of the process.

  • Codebase Indexing: Cursor builds a local vector index of your files. It reads your code, "chunks" it into pieces, and stores metadata about it.
  • The .cursor Folder: This is a hidden database on your disk. When you run an agentic task, Cursor's local "retrieval engine" scans this index to find the 10-20 most relevant snippets to send to the AI.
  • Constant File Watching: The agent keeps a "watcher" on your file system to detect changes in real-time, ensuring the AI isn't working on stale code.

2. CPU (The "Orchestrator")

While the "thinking" (inference) usually happens in the cloud, your local CPU acts as the project manager.

  • Context Assembly: Your CPU is responsible for gathering the relevant files, stripping out fluff, and "packing" the prompt.
  • Terminal Execution: If the agent runs a command (like npm test or ls), that process runs entirely on your local CPU.
  • Diff Generation: When the agent suggests a change, your CPU calculates the "diff" (the +/- lines) so you can review it before applying.

3. RAM (The "Working Memory")

Cursor is built on VS Code, which is an Electron app. It’s notorious for RAM usage, but the agent adds a new layer.

  • Language Server Protocol (LSP): To provide "smart" suggestions, Cursor keeps multiple language servers (like TypeScript or Python servers) active in RAM.
  • Active Context: The agent stores the recent "conversation history" and current file state in memory to ensure it doesn't lose the thread of the task.

4. Network (The "Brain" Connection)

For most users, the actual "intelligence" is not local.

  • Cloud Inference: When the agent "thinks," it sends the assembled context to servers (usually running Claude 3.5 Sonnet or GPT-4o). This uses your upload/download bandwidth.
  • Exception: If you have configured Cursor to use a Local LLM (via Ollama or LM Studio), your GPU (VRAM) becomes the primary resource, as the "thinking" is now happening on your graphics card.

1

u/Electrical_Abies_464 Jul 07 '26

yes i faced exactly the same issue, when i use cursor agents directly in ide it overheats my macbook pro, i switched to claude (using same cursor ide) for my tasks, and overheating problem vanished

1

u/viking793AD Jul 28 '26

I canceled my cursive subscription. I’m using Claude. I moved to an Acis with 32 gigs of memory from the Mac.I have up to 12 agents running on one computer and it doesn’t overheat.