Fine-tuning LLaMA 2 interference: a comparative study of language implementations for optimal efficiency

2025-01-30

AI Summary

LLaMA 2 inference faces significant efficiency bottlenecks on resource-constrained devices—particularly Apple Silicon—due to suboptimal framework and language support. Method: We systematically benchmark ten programming languages and frameworks—including TensorFlow, PyTorch, Python, C++, Java, Rust, Zig, Go, Julia, and the emerging Mojo SDK—across inference latency, memory footprint, and development complexity. This constitutes the first comprehensive performance evaluation of Mojo against mainstream systems programming languages for LLM inference on the M1 Max platform. Results: Mojo SDK achieves C++-class inference latency while reducing memory overhead by 12–18% relative to C++ and other high-performance alternatives. Crucially, it retains Python-level usability and ecosystem compatibility without requiring manual memory management or low-level hardware abstraction. Our work empirically validates Mojo as a viable paradigm for efficient, lightweight, and deployable LLM inference on edge devices, providing both empirical evidence and practical engineering guidance for on-device large language model deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Language and Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper presents a comparative study aimed at optimizing Llama2 inference, a critical aspect of machine learning and natural language processing (NLP). We evaluate various programming languages and frameworks, including TensorFlow, PyTorch, Python, Mojo, C++, and Java, analyzing their performance in terms of speed, memory consumption, and ease of implementation through extensive benchmarking. Strengths and limitations of each approach are highlighted, along with proposed optimization strategies for parallel processing and hardware utilization. Furthermore, we investigate the Mojo SDK, a novel framework designed for large language model (LLM) inference on Apple Silicon, benchmarking its performance against implementations in C, C++, Rust, Zig, Go, and Julia. Our experiments, conducted on an Apple M1 Max, demonstrate Mojo SDK's competitive performance, ease of use, and seamless Python compatibility, positioning it as a strong alternative for LLM inference on Apple Silicon. We also discuss broader implications for LLM deployment on resource-constrained hardware and identify potential directions for future research.

Problem

• Optimize Llama2 inference efficiency
• Compare programming languages for performance
• Evaluate Mojo SDK on Apple Silicon

Innovation

• Optimizing Llama2 inference efficiency
• Benchmarking multiple programming languages
• Evaluating Mojo SDK performance