The Key to Running Local AI Lies in "VRAM" Capacity! Explaining the Relationship Between GPU Memory and Speed

When trying to run AI on your own PC and wondering, "Why is it so slow?", the cause is often related to the capacity of "VRAM," the dedicated memory of the GPU. In this article, we will unravel the role of VRAM in local AI and its importance based on actual measurement data.

Why VRAM Capacity Is Important

When running local AI, where you place the AI "model (data summarizing knowledge and structure)" determines the processing speed. Here, VRAM is crucial.

VRAM is memory dedicated to the GPU (a component specialized in image processing and calculations). If the entire AI model fits within this VRAM, the GPU can read that data at high speeds and perform very smooth inference (the process where AI generates answers).

On the other hand, if the size of the model you want to run exceeds the VRAM capacity, the system will attempt to compensate for the missing portion using the CPU and main memory (general memory for the entire PC). However, because the data transfer speed through this path is extremely slow compared to internal GPU processing, the resulting generation speed drops significantly.

• Fits in VRAM: High speed as it is completed entirely by the GPU
• Exceeds VRAM: Low speed as it uses both CPU and main memory

[Actual Measurement Comparison] Dramatic Speed Differences Based on the Presence of VRAM

In experiments conducted by this account, we specifically confirmed the impact of VRAM capacity on processing speed. The model used was "gemma4:12b (Q8)", with a model size of approximately 14.2GB.

For this experiment, we used a GPU called the "RTX 4080" equipped with 16GB of VRAM. Since the model size (approx. 14.2GB) fit cleanly inside the VRAM capacity (16GB), the following results were obtained.

First, the speed when using the RTX 4080 was "43.5 tokens/sec". This is a very comfortable speed that feels like it comes out "instant".

On the other hand, when running on the CPU only without using the GPU, the speed dropped to "4.6 tokens/sec". This is a level of speed where the "short wait" becomes noticeable; comparing this to the GPU usage clearly shows how much of a difference it makes whether the model fits in VRAM.

• Operation on RTX 4080 (VRAM 16GB): Approx. 43.5 tokens/sec (instant)
• Operation on CPU only: Approx. 4.6 tokens/sec (short wait)

Points for Building a Comfortable Environment

The important lesson derived from this experiment is that "whether it fits in your PC's VRAM" takes priority over the size of the model.

For example, if the size of the model you want to run is 14.2GB, it will operate very quickly if you have a VRAM capacity of more than that (e.g., 16GB or more). However, if your GPU's VRAM is less than that, it is expected that running the model as-is will result in extremely slow speeds.

In such cases, instead of trying to force a large model to run, it is necessary to use techniques such as selecting smaller size models that have been "quantized (a technology that lightens the model by thinning out data)" in advance. Checking your PC's specs and choosing an appropriate size that fits in the VRAM is the first step toward a comfortable local AI experience.

• First, understand your GPU's VRAM capacity
• Check the size of the model you want to run
• If it exceeds VRAM, select a lighter quantized model

Summary

In the world of local AI, whether "the model fits in VRAM" is a major boundary that determines comfort, even more than general GPU specs. If you want to run AI on your PC without stress, we recommend first checking the VRAM capacity of your current hardware and trying models that fit it.

• VRAM is the most important factor determining AI processing speed
• Whether it fits or not is the dividing line between "comfortable" and "slow"
• Choosing the optimal model based on your specs is important