zkLLM is a novel zero-knowledge proof (ZKP) system specifically designed for large language models (LLMs). It addresses the challenge of verifying the authenticity of LLM outputs without revealing model parameters, which is crucial for legal and regulatory compliance. The system introduces tlookup, a parallelized lookup argument for non-arithmetic tensor operations in deep learning, and zkAttn, a ZKP tailored for the attention mechanism in LLMs. These components enable efficient and secure verification of LLM computations, with zkLLM capable of generating a correctness proof for the entire inference process of a 13-billion-parameter LLM in under 15 minutes, producing a compact proof of less than 200 kB. The system leverages a fully parallelized CUDA implementation to achieve these performance gains, ensuring privacy and security while maintaining computational efficiency. The design of zkLLM balances running time, memory usage, and accuracy, making it a significant advancement in the field of ZKP for LLMs. The paper also discusses the technical details of tlookup and zkAttn, including their implementation in deep learning frameworks and their application to various operations such as matrix multiplication, activation functions, and normalization. The study highlights the importance of ZKPs in ensuring the legitimacy and privacy of LLMs, particularly in the context of legal and regulatory requirements.zkLLM is a novel zero-knowledge proof (ZKP) system specifically designed for large language models (LLMs). It addresses the challenge of verifying the authenticity of LLM outputs without revealing model parameters, which is crucial for legal and regulatory compliance. The system introduces tlookup, a parallelized lookup argument for non-arithmetic tensor operations in deep learning, and zkAttn, a ZKP tailored for the attention mechanism in LLMs. These components enable efficient and secure verification of LLM computations, with zkLLM capable of generating a correctness proof for the entire inference process of a 13-billion-parameter LLM in under 15 minutes, producing a compact proof of less than 200 kB. The system leverages a fully parallelized CUDA implementation to achieve these performance gains, ensuring privacy and security while maintaining computational efficiency. The design of zkLLM balances running time, memory usage, and accuracy, making it a significant advancement in the field of ZKP for LLMs. The paper also discusses the technical details of tlookup and zkAttn, including their implementation in deep learning frameworks and their application to various operations such as matrix multiplication, activation functions, and normalization. The study highlights the importance of ZKPs in ensuring the legitimacy and privacy of LLMs, particularly in the context of legal and regulatory requirements.