LLMConf: Knowledge-Enhanced Configuration Optimization for Large Language Model Inference (IWQoS 2025)

💡 What is LLMConf?

LLMConf is a multi-parameter tuning method for LLMs. By leveraging knowledge-enhanced techniques, we identify tuning parameters and their value ranges, significantly reducing the search space for parameter combinations. To capture the impact of configuration parameters on inference performance, we use the automated machine learning tool TPOT to model the functional relationships between configuration parameters and each performance metric. Additionally, to optimize multiple performance metrics simultaneously and resolve conflicts in optimization directions, we implement a multi-objective optimization module based on the NSGA-III algorithm.

The experimental results show that LLMConf significantly outperforms state-of-the-art methods, achieving an average performance improvement of 20.1% on 7 metrics.

LLMConf demonstrates a strong transferability across diverse datasets, varying concurrency levels and different LLM base models.

🚀 Performance snapshot

We evaluate the inference performance of LLMs from two aspects: latency and throughput. In terms of latency, we consider latency (the time taken to complete each request), time to first token(TTFT) and time per output token(TPOT). For throughput, we measure tokens per second(TPS). Our 7 optimized metrics include latency_average, latency_p99, TPS_average, TTFT_average, TTFT_p99, TPOT_average and TPOT_p99.

From the figure below, it can be seen that the optimization results of LLMConf are noticeably superior to those of other multi-objective optimization algorithms.

💻 Quickstart

Set Up Python Environment: Use the following commands to create and activate the python environment:

conda create -n LLMConf python=3.10
conda activate LLMConf

Install Dependencies: install the necessary dependencies by running:

pip install -r requirements.txt

After completing the above steps, move into the LLMConf directory and follow the steps below to run the LLMConf project.

- Knowledge-based Parameter Selection

We need to structure the constructed knowledge base into the prompt.

For the prompt used in parameter selection, refer to SelectConfiguration.txt. Run the following command to complete the tuning parameter selection, setting the file_path value to ./SelectConfiguration.txt.

cd LLMConf
python llm_chat.py

For the prompt used in determining the range and type of each tuning parameters, refer to TypeandRange.txt (using the determination of the value range for max-num-batched-tokens as an example). Run the following command to complete the determination of the range and type of each tuning parameter, setting the file_path value to ./TypeandRange.txt.

python llm_chat.py

✨️ Note: The api_key and base_url need to be filled in.

- Data Collector

Run the following command to deploy LLM (the BaseLLLM folder needs to be created before downloading LLM).

modelscope download --model 'LLM-Research/Meta-Llama-3-8B-Instruct' --local_dir 'BaseLLM/Meta-Llama-3-8B-Instruct'
modelscope download --model 'Qwen/Qwen2.5-14B-Instruct' --local_dir 'BaseLLM/Qwen/Qwen2.5-14B-Instruct'

Run the following command to automate data collection.

python auto.py

✨️ Note:

• config.yml: Include the range and type of all tuning parameters.
• SetConfig.py: Randomly set the values of various configuration parameters.
• vllm_benchmark.py: Test the inference performance of the LLM by a series of performance metrics.

If only collecting the inference performance of the LLM under a specific combination of configuration parameters, the following command can be run.

vllm serve /LLMConf/BaseLLM/Meta-Llama-3-8B-Instruct --port 8100
python /LLMConf/vllm_benchmark1.py --num_requests 200 --concurrency 80 --output_tokens 200 --vllm_url http://localhost:8100/v1 --api_key EMPTY

✨️ If using the ChatDoctor-HealthCareMagic-100k dataset for testing, vllm_benchmark_Health.py can be run.

✨️ Note: All the data collected in this experiment can be found in the Data folder.(The naming of the CSV file sequentially represents LLM, concurrency and dataset.)

- Performance Modeling

First, move the data that needs to be modeled to the Modeling directory. Run the following command to convert the Boolean type in the data to 0 or 1.

cd Modeling
python convert.py

Run the following command to complete the modeling of the tuning parameters with each performance metric.

python modeling.py

⚖️ Other modeling methods:

• CNN model:

cd comp
python CNN.py

• MLP model:

cd comp
python MLP.py

• Random Forest model:

cd comp
python RandomForest.py

• SVM model:

cd comp
python SVM.py

• XGBoost model:

cd comp
python XGBoost.py

- Multi-Objective Optimization

Run the following command to complete the recommendation of optimal configuration parameters.

cd Functions
python optimize.py

⚖️ Other multi-objective optimization algorithms:

• RS:

python RS.py

• SCOOT:

python SCOOT.py

• MAB:

python MultiBandit.py

• DDPG:

python DDPG.py

• NSGA-III:

python NSGA3.py