Summary of Llm-inference-bench: Inference Benchmarking Of Large Language Models on Ai Accelerators, by Krishna Teja Chitty-venkata et al.
LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators
by Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus, Aditya Tanikanti, Ken Raffenetti, Valerie Taylor, Murali Emani, Venkatram Vishwanath
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary LLMs have revolutionized multiple domains, but their computational demands pose challenges that require efficient hardware acceleration. To understand LLM scalability and throughput characteristics, we introduce LLM-Inference-Bench, a comprehensive benchmarking suite evaluating LLM inference performance across diverse hardware platforms, including GPUs from Nvidia and AMD, Intel Habana, and SambaNova. Our analysis includes various LLM inference frameworks and models with 7B and 70B parameters from LLaMA, Mistral, and Qwen families. The results reveal strengths and limitations of different models, hardware platforms, and inference frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have made big progress in many areas, but they need strong computers to run fast. We want to see how well these language models work on different computers, like the ones from Nvidia, AMD, Intel Habana, and SambaNova. We looked at many types of language models and computer programs that help them work. Our results show which language models do best on which computers. |
Keywords
» Artificial intelligence » Inference » Llama
