Optimizing LLaMA 2 Inference Across Languages
Comparative performance analysis of programming frameworks for LLM efficiency
This research provides a comprehensive comparison of programming languages and frameworks for optimizing LLaMA 2 inference performance.
- Multiple frameworks evaluated including TensorFlow, PyTorch, Python, Mojo, C++, and Java
- Performance metrics analyzed across speed, memory consumption, and implementation complexity
- Trade-offs identified between development efficiency and runtime performance
- Optimization strategies proposed for each implementation approach
This research matters for engineering teams deploying LLMs in production environments where balancing inference speed, resource utilization, and development effort is critical for cost-effective AI implementation.