Can I run large models on RAM instead of VRAM?

Yes, the Stratat engine is built to utilize system RAM for larger models. This allows you to run models that exceed your GPU's limited VRAM capacity.

Is VRAM faster than RAM for AI?

Yes, VRAM is significantly faster than system RAM. Using VRAM results in faster token generation, but it limits the model size you can load.

How do I check my RAM size on Windows?

Open Task Manager, click the Performance tab, and select Memory to see your total RAM. Check the GPU section for VRAM size.

Does using RAM cost more in electricity?

Yes, using system RAM typically increases CPU load. At $0.17/kWh, this increases your energy cost compared to using an efficient GPU.

vram-vs-ram: Local AI Memory Guide

Learn why VRAM matters for local LLMs. Stratat uses RAM for large models. See how to check your hardware and optimize your Stratat client setup.

The critical distinction for running large language models locally is that VRAM is fast but limited, whereas system RAM is slower but abundant. For the Stratat engine, leveraging system RAM allows you to run significantly larger models than your GPU alone would permit, making RAM the primary capacity driver for local deployment.

Why This Distinction Matters

When you are setting up the Stratat client, understanding where your data lives determines your experience. VRAM (Video RAM) is attached directly to your GPU. It is incredibly fast, which leads to rapid token generation, but it is expensive and scarce. A high-end consumer GPU might have 24GB. If your model exceeds this, the system must move data to system RAM. This is where the concept of "vram-vs-ram" becomes vital for your workflow. System RAM is the memory your CPU uses. It is much slower than VRAM but offers vastly more capacity. The Stratat engine is designed to utilize this abundant resource, allowing you to load models that simply would not fit on a dedicated graphics card.

How to Check Your Setup

You can verify your resources directly on your machine to see what the Stratat engine will access. On Windows, open Task Manager and look at the "Performance" tab to see your GPU memory and total RAM. On macOS, use the Activity Monitor to check memory pressure. Understanding this helps you decide whether to prioritize GPU upgrades or simply adding more RAM sticks. The engine intelligently manages this transition, ensuring that even if you are using RAM, the process remains stable.

Resource Type	Location	Speed	Capacity Limit
VRAM	GPU	Very Fast	Low (e.g., 8-24 GB)
System RAM	CPU Board	Moderate	High (e.g., 32-128 GB)
Storage	SSD/HDD	Slow	Very High (TBs)

VRAM is the high-speed memory dedicated to graphics processing, while system RAM is the general-purpose memory accessible by the CPU.

Cost and Efficiency

Using system RAM reduces the need for expensive GPU hardware. However, consider the electricity cost. Running a large model on RAM uses more CPU power. At a local price of $0.17/kWh, this adds to your operational expenses. Calculate your usage to stay within budget while leveraging the flexibility of the Stratat engine.