运行多个MATLAB实例时，单实例内存限制是否受影响及影响机制？

Great question—this is a common point of confusion when running multiple MATLAB instances, so let's break it down clearly into two key areas: MATLAB's internal per-instance limits, and system-wide memory resource sharing.

1. MATLAB's Per-Instance Memory Limits Don't Get Split #

Each MATLAB instance runs as a separate, independent process on your computer. That means any memory limits you've configured for a single instance (like Java heap size, array size caps, or custom memory limits set via flags such as -memmax) apply per instance, not across all running instances.

For example:

• If you set your MATLAB's Java heap to 4GB in the preferences, every MATLAB instance you launch will have its own dedicated 4GB heap allocation. One instance doesn't "steal" heap space from another.
• 32-bit MATLAB has a hard memory limit of ~4GB (varies slightly by operating system), and each 32-bit instance will adhere to this same limit—they don't split the 4GB pool between them.

These per-instance limits are baked into how each process initializes, so launching more instances won't alter or split them.

2. System-Wide Memory Is Shared (And This Creates Competition) #

The real constraint comes from your computer's total physical and virtual memory. All MATLAB instances (alongside every other running process on your machine) share this pool of system resources.

Here's how this plays out in practice:

• Suppose your computer has 16GB of physical RAM. If one MATLAB instance is using 10GB of that, the second instance will only have ~6GB of free physical RAM to work with (plus whatever virtual memory your system allows)—even if its internal memory limit is set higher.
• If a MATLAB instance tries to allocate more memory than what's available in the system's free pool, your OS will start using virtual memory (paging data to disk), which slows performance drastically. In extreme cases, the instance might throw an "out of memory" error if even virtual memory is exhausted.

This isn't MATLAB splitting its own limits—it's just basic system resource contention.

A Quick Note on Parallel Computing Toolbox #

If you're using the Parallel Computing Toolbox to run local workers, MATLAB will actively coordinate memory usage across those workers to avoid overloading the system. But this is a deliberate, configured behavior—regular standalone MATLAB instances don't do this automatically.

内容的提问来源于stack exchange，提问作者WJA