When you deploy a function, Firebase Cloud Functions lets you specify how much memory it should have. The platform automatically gives more CPU power if you allocate more memory. So, it's crucial to set an allocation that meets your function's needs without wasting resources.

To do this, use the --memory CLI option when deploying your function. Or, in the Node.js runtime, you can specify memory in the function configuration options by adding .runWith({memory: '256MB'}) to your function. Firebase Cloud Functions supports several memory allocation tiers ranging from 128MB to 16GB.

Avoid initializing heavy objects or libraries at the top level. Instead, use lazy loading tactics where resources are loaded only when needed. Modules and objects are loaded when they are first requested, which helps save memory. You can do this by moving `require` calls from the global scope into the function scope. The efficiency of an application greatly depends on how data is organized and manipulated. When writing functions, choose data structures that consume less memory and implement efficient algorithms. Whenever possible, batch database operations. Separate operations consume more memory. Firebase SDKs like Firestore provide an easy way to batch write, update, and delete operations in a single atomic operation. To prevent a memory leak, make sure all unused objects are disposed of by setting them to null or undefined once you're done with them. This allows JavaScript's garbage collector to reclaim the memory. Choose the right trigger for your functions. Some types of triggers, like HTTP requests, can cause functions to spawn more instances, thus consuming more memory. It's important to understand how different triggers work and their effect on memory so that you can make informed choices. The Firebase Local Emulator Suite lets you build and test your Firebase project locally. It provides detailed logs with resource usage of your cloud functions, making it easier to inspect your functions' memory consumption and optimization levels. It's a great tool to help pinpoint potential memory leaks and high memory usage areas. Writing monolithic functions can lead to high memory usage as every instance of the function has to load and initialize all dependencies and global objects, regardless of whether they are used. To keep memory usage low, consider breaking your functions into smaller, more modular units, each handling a specific task. This way, each function instance only needs to load the resources it will use, saving memory.