ollama - 💡(How to fix) Fix why the ollama get response very slow? (QWEN3.5 35B A3B) [13 comments, 10 participants]

Fix Plan

The issue seems to be related to GPU resource management in Ollama. To resolve this, we can try the following steps:

• Update Ollama configuration: Increase the GPU memory allocation or adjust the batch size to prevent GPU resource exhaustion.
• Implement GPU warming: Add a GPU warming mechanism to ensure the GPU is properly initialized before handling requests.
• Optimize GPU utilization: Modify the code to optimize GPU utilization, reducing the time it takes to generate responses.

Example code snippet to optimize GPU utilization:

import torch

# Set GPU device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Move model to GPU
model.to(device)

# Define a function to generate responses
def generate_response(prompt):
    # Move input to GPU
    input_ids = prompt.to(device)
    
    # Generate response
    output = model.generate(input_ids)
    
    # Move output back to CPU
    output = output.cpu()
    
    return output

# Define a GPU warming function
def warm_up_gpu():
    # Perform a dummy computation to warm up the GPU
    torch.randn(100, 100, device=device)

# Call the GPU warming function before handling requests
warm_up_gpu()

Verification

To verify the fix, restart Ollama and test the response time for multiple prompts. Check the GPU utilization using nvidia-smi to ensure it's not maxed out.

Extra Tips

• Monitor GPU memory usage and adjust the configuration accordingly.
• Consider implementing a queueing system to handle multiple requests and prevent GPU resource exhaustion.
• Refer to the Ollama documentation for more information on optimizing GPU performance.