This is the third post in the large language model latency-throughput benchmarking series, which aims to instruct developers on how to benchmark LLM inference…
Overview
This article provides a comprehensive guide on benchmarking LLM inference using TensorRT-LLM, focusing on performance tuning techniques. It covers practical steps for setting up a GPU environment, preparing datasets, running benchmarks, and serving models effectively.
What You'll Learn
1
How to set up a GPU environment for benchmarking LLMs
2
How to prepare a dataset for LLM benchmarking
3
How to run benchmarks using trtllm-bench
4
How to analyze performance results from LLM benchmarks
5
How to serve a large language model using trtllm-serve
Prerequisites & Requirements
- Understanding of large language models and benchmarking concepts
- Familiarity with TensorRT-LLM and GPU management tools(optional)
Key Questions Answered
What is trtllm-bench and how is it used for benchmarking?
trtllm-bench is a Python-based utility in TensorRT-LLM designed for benchmarking models without the overhead of full inference deployment. It sets up the engine with optimal settings to provide insights into model performance quickly.
How do you prepare a dataset for benchmarking with TensorRT-LLM?
You can prepare a synthetic dataset using the prepare_dataset command or create a custom dataset formatted as a JSON Lines (jsonl) file. Each line should contain a payload, such as task_id, prompt, and output_tokens.
What are the key performance metrics reported by trtllm-bench?
Key performance metrics include Request Throughput (req/sec), Total Output Throughput (tokens/sec), Total Latency (ms), Average request latency (ms), and Average time-to-first-token (TTFT) (ms). For example, the Request Throughput was reported as 86.5373 req/sec.
How can you serve a large language model using TensorRT-LLM?
You can serve a large language model using the trtllm-serve command, specifying parameters such as model path, backend, max number of tokens, and max batch size. This allows you to deploy an OpenAI-compatible endpoint.
Key Statistics & Figures
Request Throughput
86.5373 req/sec
Measured during the benchmark run using the trtllm-bench tool.
Total Output Throughput
11076.7700 tokens/sec
Indicates the total number of tokens generated per second during the benchmark.
Total Latency
1155.5715 ms
Total time taken for all requests during the benchmark.
Average time-to-first-token
162.6706 ms
Average time taken to return the first token after a request.
Technologies & Tools
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Backend
Tensorrt-llm
Used for benchmarking and serving large language models.
Backend
Pytorch
Backend framework used for running the model during benchmarking.
Key Actionable Insights
1To achieve optimal performance in LLM inference, ensure your GPU environment is correctly configured before running benchmarks.Proper GPU setup is crucial for accurate benchmarking results. Use commands like 'sudo nvidia-smi -rgc' to restore default settings and 'nvidia-smi -q -d POWER' to check maximum usage.
2Utilize the trtllm-bench tool to quickly assess model performance and make informed tuning decisions.By running benchmarks with trtllm-bench, you can gather essential performance metrics that guide adjustments to your model and deployment strategy.
3When preparing datasets, consider using JSON Lines format for custom datasets to streamline the benchmarking process.This format allows for easy integration with trtllm-bench, ensuring that your benchmarking runs smoothly and efficiently.
4Analyze the performance results carefully to prioritize user experience based on throughput and latency metrics.Understanding the trade-offs between request throughput and latency can help you optimize the user experience, especially in high-demand scenarios.
Common Pitfalls
1
Failing to properly configure the GPU environment can lead to inaccurate benchmarking results.
Ensure that commands like 'sudo nvidia-smi -rgc' are run to restore settings before starting benchmarks to avoid skewed performance metrics.
2
Not preparing the dataset correctly can result in errors during benchmarking.
Using the correct JSON Lines format for custom datasets is essential for seamless integration with the benchmarking tools.
Related Concepts
Large Language Models (llms)
Benchmarking Techniques
Performance Tuning Strategies