Summary of Reddit Comments on "ollama"

ollama Features and Critiques

• "ollama" is a wrapper around "llama.cpp" with some critiques about being poorly written and hastily put together.
• It offers features like automatically setting up parameters and models, but users find it challenging to adjust parameters or manage caching effectively.
• Users are pleased with the speed and performance of ollama, especially when using models like "deepseek-coder" and "dolphincoder," even on AMD GPUs.
• There are concerns about potential security vulnerabilities when ollama is exposed to the internet or run on Docker deployments.
• Users question the necessity of using ollama when one can directly interact with "llama.cpp" for more control over models and better performance.
• Some users prefer other llama.cpp wrappers like "koboldcpp" or "Ooobabooga's textgen webui" due to reliability issues with ollama, especially behind proxies.
• An alternative to ollama is "ava," which offers an API, web UI, and various features that are deemed advantageous over ollama.
• Performance comparisons between ollama and llama.cpp indicate similar results with slight variations in token processing speeds.
• Users suggest building applications as API-based to avoid integration issues with wrappers like ollama and recommend direct interaction with llama.cpp for better control and performance.

ollama Hardware and Optimization

• ollama's memory calculations affect its performance, often being conservative, but similar results to llama.cpp can be achieved when adjusting layers.
• Users debate the use of CPU-only processing and the performance differences between llama.cpp and ollama.
• Tweaks and optimizations in ollama are suggested to achieve better performance, similar to the updates in llama.cpp itself.
• Discussion ensues regarding the hardware specifications and configurations that affect ollama's performance and prompt processing speeds.

Alternative Solutions and Recommendations

• Users highlight the advantages of using llama.cpp directly, especially with recent updates allowing for model retrieval from Hugging Face and providing a REST API.
• The possibility of improving ollama's performance by building it from scratch, updating underlying modules, and enabling additional features like flash attention is discussed.
• Questions arise about the compatibility of front ends like kobold with llama.cpp and the potential performance discrepancies between llama-cpp-python and llama.cpp.
• Users suggest exploring llama.cpp's numerous features and API endpoints to achieve similar functionality without the need for wrappers like ollama.

Overall, users appreciate the performance of ollama but also emphasize the importance of considering alternatives like direct interactions with llama.cpp for better control and efficiency. Users also recommend optimizing ollama and exploring other llama.cpp wrappers for improved reliability and performance.