llama.cpp - useful flags - share your thoughts please
The article discusses the use of various flags to improve the performance of the llama.cpp language model. The author shares their experience of using the GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 flag, which resulted in a 10-15% performance increase.
Why it matters
Optimizing the performance of language models like llama.cpp is crucial for their effective deployment and usage in various applications.
Key Points
- 1The author compiled llama.cpp with the GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 flag, which improved performance by 10-15%
- 2The author is looking for additional flags or tricks to further improve the performance of llama.cpp
- 3The author's system specifications include a Ryzen 9 9950X3D CPU, RTX 5090 GPU, and 128GB of DDR5 RAM running Arch Linux
Details
The article discusses the use of various flags to optimize the performance of the llama.cpp language model. The author shares their experience of using the GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 flag, which resulted in a 10-15% performance increase. The author also provides examples of other models, such as gpt-oss-120b and Qwen3-VL-235B-A22B-Instruct-Q4_K_M, where the use of flags improved the performance from 36 to 46 tokens/sec and 5.3 to 8.9 tokens/sec, respectively. The author is running this on a high-end system with a Ryzen 9 9950X3D CPU, RTX 5090 GPU, and 128GB of DDR5 RAM on Arch Linux, and is looking for additional flags or tricks to further improve the performance of llama.cpp.
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