VRAM Usage Before and After Chatting

Oct 13, 2025 by magicalceylontours.com 91 views

VRAM Usage Fluctuates Significantly After Chatting: A Critical Issue in AI Model Deployment

The deployment of AI models in various applications has become increasingly popular in recent years. However, one of the critical issues that developers face is the management of VRAM (Video Random Access Memory) usage. In this article, we will discuss a significant fluctuation in VRAM usage after chatting, which may lead to OOM (Out of Memory) errors during the chat. We will also explore the possible causes and propose a solution to control the VRAM growth.

The VRAM usage after chatting may significantly differ from the VRAM calculated by gguf-parser (42% difference in this case), which may cause OOM during the chat. This issue is critical because it can lead to unexpected behavior, crashes, or even data loss. The problem is not limited to a specific model or environment, but it is a common issue that affects many AI model deployments.

VRAM Usage Before and After Chatting

To understand the issue better, let's take a look at the VRAM usage before and after chatting. The following images show the VRAM usage right after deployment and after chatting with short text, medium text (ctx-size/2), and long text (ctx-size) prompts respectively.

VRM usage right after deployment

VRAM usage after chatting with short text, medium text (ctx-size/2), and long text (ctx-size) prompts respectively; increased by more than 15GB

As we can see from the images, the VRAM usage increases significantly after chatting, especially with long text prompts. This increase in VRAM usage can lead to OOM errors, which can cause the model to crash or behave unexpectedly.

There are several possible causes for the significant fluctuation in VRAM usage after chatting. Some of the possible causes include:

Model complexity: The model used in this deployment is a complex one, which may require more VRAM to process the input data.
Context size: The context size of the input data may also contribute to the increase in VRAM usage.
GPU usage: The GPU usage may also play a role in the increase in VRAM usage.

To control the VRAM growth and align it closely with the values calculated by gguf-parser, we can implement the following solution:

Monitor VRAM usage: Monitor the VRAM usage before and after chatting to identify the pattern of increase.
Adjust context size: Adjust the context size of the input data to reduce the VRAM usage.
Optimize model complexity: Optimize the model complexity to reduce the VRAM usage.
Use GPU optimization techniques: Use GPU optimization techniques to reduce the VRAM usage.

The deployment environment used in this case is as follows:

Llama-box: v0.0.123
Model: unsloth/DeepSeek-R1-Distill-Qwen-32B-F16
OS: Ubuntu 22.04
GPU: 910B1 * 2 (RC mode)

In conclusion, the VRAM usage fluctuates significantly after chatting, which may lead to OOM errors during the chat. The possible causes of this issue include model complexity, context size, and GPU usage. To control the VRAM growth and align it closely with the values calculated by gguf-parser, we can implement the solution proposed in this article. By monitoring VRAM usage, adjusting context size, optimizing model complexity, and using GPU optimization techniques, we can reduce the VRAM usage and prevent OOM errors during the chat.
VRAM Usage Fluctuates Significantly After Chatting: A Critical Issue in AI Model Deployment

In our previous article, we discussed the critical issue of VRAM usage fluctuating significantly after chatting, which may lead to OOM errors during the chat. In this article, we will answer some frequently asked questions related to this issue and provide more insights into finding solutions.

Q: What is VRAM, and why is it important in AI model deployment?

A: VRAM (Video Random Access Memory) is a type of memory that is used to store data temporarily while it is being processed by a computer. In AI model deployment, VRAM is crucial because it stores the model's weights, biases, and other parameters that are used to make predictions. If the VRAM is not sufficient, the model may not be able to process the input data correctly, leading to errors or crashes.

Q: What causes the VRAM usage to fluctuate significantly after chatting?

A: There are several possible causes of the VRAM usage fluctuation after chatting, including:

Model complexity: The model used in this deployment is a complex one, which may require more VRAM to process the input data.
Context size: The context size of the input data may also contribute to the increase in VRAM usage.
GPU usage: The GPU usage may also play a role in the increase in VRAM usage.

Q: How can I monitor VRAM usage before and after chatting?

A: You can use various tools and techniques to monitor VRAM usage before and after chatting, including:

GPU monitoring software: Use software such as NVIDIA's GPU Monitor or AMD's Radeon Software to monitor VRAM usage.
System monitoring tools: Use system monitoring tools such as Prometheus or Grafana to monitor VRAM usage.
Custom scripts: Write custom scripts to monitor VRAM usage and log the data for analysis.

Q: How can I adjust context size to reduce VRAM usage?

A: You can adjust context size to reduce VRAM usage by:

Reducing the input data size: Reduce the size of the input data to reduce the VRAM usage.
Using a smaller model: Use a smaller model that requires less VRAM to process the input data.
Optimizing the model architecture: Optimize the model architecture to reduce the VRAM usage.

Q: How can I optimize model complexity to reduce VRAM usage?

A: You can optimize model complexity to reduce VRAM usage by:

Simplifying the model architecture: Simplify the model architecture to reduce the number of parameters and weights.
Using a smaller number of layers: Use a smaller number of layers to reduce the VRAM usage.
Optimizing the model weights: Optimize the model weights to reduce the VRAM usage.

Q: How can I use GPU optimization techniques to reduce VRAM usage?

A: You can use GPU optimization techniques to reduce VRAM usage by:

Using a GPU with a large amount of VRAM: Use a GPU with a large amount of VRAM to reduce the VRAM usage.
Optimizing the GPU usage: Optimize the GPU usage to reduce the VRAM usage.
Using a GPU with a high clock speed: Use a GPU with a high clock speed to reduce the VRAM usage.

In conclusion, the VRAM usage fluctuates significantly after chatting, which may lead to OOM errors during the chat. By understanding the possible causes of this issue and using various techniques to monitor and optimize VRAM usage, we can reduce the VRAM usage and prevent OOM errors during the chat.