Why fine-tuned gemini-1.5-flash is so slow?

[jsm_llm]

Hi,

I've fine-tuned the gemini-1.5-flash model with some dataset and tried to use it. But the inferencing time is extremly slow. It takes about 10 seconds for the simple input. I just wrote "Hi" and received output message after many seconds.

Please let me know the reason and how to accelorate it like normal gemini-1.5-flash model.

And if there is a documentation about this issue please reply me.

[jordan-extend]

We're seeing the same thing. Much slower completions on fine tuned models (2x or more slower).

[ibaui · Accepted solution]

Hi @jsm_llm,

Welcome to Google Cloud Community!

The slow inference time you're seeing with your fine-tuned Gemini-1.5-flash model after inputting just "Hi" could be due to a few different factors. These issues aren't directly mentioned in the official documentation because they result from your specific fine-tuning process and setup, not from the base model itself. The main point is understanding the trade-offs you made during fine-tuning and how they affect the model's performance during inference. Here's why this happens and how to potentially speed things up:

Possible Reasons for Slow Inference:

• Model Complexity: Fine-tuning adds new layers and parameters to the model, making it larger and more complex. This increased complexity can lead to longer inference times.
• Data Dependency: The fine-tuned model is now customized for your specific dataset. It may need to process more data to generate responses that match your information, which could result in slower inference times.
• Resource Allocation: The resources allocated to your model during inference might be insufficient. Vertex AI might be limiting the number of CPUs or GPUs available for your model, resulting in slower performance.

Potential Strategies to Accelerate Inference:

• Optimize Your Fine-Tuning:
  • Smaller Dataset: If possible, use a smaller, more focused dataset for fine-tuning. This can reduce the complexity of the model and potentially improve inference speed.
  • Training Parameters: Experiment with different training parameters, such as learning rate, batch size, and epochs, to find the optimal settings for your model and dataset.

• Optimize Your Inference Code:
  • Efficient Prompts: Make sure your prompts are clear and to the point. Avoid using extra words or phrases that could slow down processing time.

• Increase Resources:
  • CPU/GPU: If possible, increase the number of CPUs or GPUs allocated to your model during inference. This can improve performance.

I hope the above information is helpful.

[jsm_llm]

Thank you for your answer. I understood the reason.