vllm - 💡(How to fix) Fix [Performance]: vllm and transformer call the same Qwen3-VL-AI4TEST-V1 model, with roughly the same configuration, but the visual label accuracy is 20% lower in testing. [1 participants]

Fix Plan

To improve performance, we will focus on optimizing the code and environment.

Steps to Improve Performance

• Profile the code: Identify performance bottlenecks using profiling tools.
• Optimize loops and database queries: Reduce unnecessary iterations and queries.
• Implement caching: Cache frequently accessed data to reduce computation time.
• Upgrade dependencies: Ensure all dependencies are up-to-date.

Example Code Optimizations

# Before: Inefficient loop
for i in range(len(data)):
    # Process data

# After: Efficient loop
for item in data:
    # Process item

# Example of caching using functools
import functools
@functools.lru_cache(maxsize=128)
def expensive_function(x):
    # Compute expensive result
    return result

Verification

• Run the profiling tool again to verify performance improvements.
• Monitor the application's performance metrics, such as response time and throughput.

Extra Tips

• Regularly review and optimize code to prevent performance regressions.
• Consider using a performance monitoring tool to track application performance over time.