CUDA System Memory Fallback: Should You Enable It?

When working with CUDA and GPU-based computations, you might come across the option to enable System Memory Fallback—a mode that allows the system to automatically switch to using your computer’s RAM if the GPU memory runs out. Let's dive into when this feature can be beneficial and when it might cause slowdowns or other issues.

What is CUDA System Memory Fallback?

CUDA System Memory Fallback is a mechanism that allows a program to utilize system RAM when the GPU's memory (VRAM) becomes insufficient. When there isn't enough VRAM to complete a task, this system memory fallback automatically steps in, helping to avoid errors by continuing the process with system memory.

Advantages of Enabling System Memory Fallback

  • Reduced memory-related errors: Enabling fallback can prevent critical errors and program crashes due to VRAM shortages, which can be useful for lengthy calculations or deep learning tasks.
  • Better performance on large data sets: If your data almost fits into VRAM, fallback allows the task to finish rather than ending abruptly.

Drawbacks of Using System Memory Fallback

  • Performance Slowdown: Switching to slower system RAM significantly increases processing time since data transfer between GPU and RAM is much slower than within VRAM.
  • Higher system load: Excessive use of RAM for CUDA tasks can slow down your system overall, as other applications may also rely on system memory.

When to Enable CUDA System Memory Fallback

Enabling fallback is beneficial in the following situations:

  • You’re working on a model or task that nearly fits in VRAM, and a slight overflow prevents critical errors.
  • You are willing to compromise on performance in favor of successfully completing lengthy processes.
  • Scaling down the task (e.g., reducing the model size) is not an option.

When Not to Enable It

It’s best to disable fallback if:

  • Your main priority is execution speed.
  • The data volume significantly exceeds VRAM, which will heavily use RAM and slow down the task.
  • Your system has limited RAM needed by other applications as well.