Llama.cpp vs Ollama: Code Generation Throughput

A notable performance discrepancy has been observed between llama.cpp and Ollama in terms of code generation throughput when running the Qwen-3 Coder 32B model locally. The analysis reveals that llama.cpp achieves approximately 70% higher throughput compared to Ollama, despite both using the same model weights and hardware. Potential reasons for this difference include variations in CUDA kernels, attention implementations, context or batching defaults, scheduler or multi-GPU utilization, and overhead from Ollama’s runtime or API layer. Understanding these differences is crucial for optimizing performance in machine learning applications. This matters because optimizing code generation throughput can significantly impact computational efficiency and resource utilization in AI model deployment.

The comparison between llama.cpp and Ollama reveals a substantial difference in code generation throughput, with llama.cpp outperforming Ollama by approximately 70% when running the Qwen-3 Coder 32B model in FP16. This performance gap is intriguing and raises questions about the underlying factors contributing to such a disparity. Both implementations utilize the same model weights and hardware, suggesting that the differences lie in the software execution and optimization strategies employed by each tool.

Several potential reasons could explain this performance discrepancy. One possibility is the use of different CUDA kernels or attention mechanisms, which can significantly impact the efficiency of model execution. Additionally, variations in default context settings or batching strategies might affect how data is processed and managed, leading to differences in throughput. Another factor could be the scheduler or multi-GPU utilization, where one implementation might be better optimized for parallel processing across multiple GPUs.

Moreover, overhead from Ollama’s runtime or API layer could contribute to the slower performance compared to llama.cpp. This overhead might stem from additional layers of abstraction or less efficient handling of computational tasks. Understanding and addressing these differences is crucial for developers and researchers who rely on these tools for machine learning tasks, as it can lead to more efficient use of computational resources and faster model training and inference times.

Investigating these performance factors is essential for the machine learning community, as it can guide improvements in software implementations and inform best practices for model execution. By identifying the specific elements that lead to higher throughput in llama.cpp, developers can optimize Ollama or similar tools to close the performance gap. This not only enhances the efficiency of current projects but also contributes to the broader goal of advancing machine learning technologies and applications. Understanding these nuances ensures that practitioners can make informed decisions about which tools to use based on their specific needs and constraints.

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