LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators
This work addresses the need for efficient hardware acceleration in LLM inference, providing a benchmarking suite to help researchers and practitioners optimize performance, though it is incremental as it applies existing methods to new data.
The authors tackled the challenge of benchmarking the inference performance of large language models (LLMs) on various hardware platforms, introducing LLM-Inference-Bench to evaluate models like LLaMA, Mistral, and Qwen with 7B and 70B parameters across GPUs and AI accelerators, revealing strengths and limitations of different configurations.
Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational demands of these complex models pose significant challenges, requiring efficient hardware acceleration. Benchmarking the performance of LLMs across diverse hardware platforms is crucial to understanding their scalability and throughput characteristics. We introduce LLM-Inference-Bench, a comprehensive benchmarking suite to evaluate the hardware inference performance of LLMs. We thoroughly analyze diverse hardware platforms, including GPUs from Nvidia and AMD and specialized AI accelerators, Intel Habana and SambaNova. Our evaluation includes several LLM inference frameworks and models from LLaMA, Mistral, and Qwen families with 7B and 70B parameters. Our benchmarking results reveal the strengths and limitations of various models, hardware platforms, and inference frameworks. We provide an interactive dashboard to help identify configurations for optimal performance for a given hardware platform.