There's quite a few tangential features that must be implemented correctly or risk affecting the LLM output in significant ways.
Parsing/encoding is one example: A couple of months ago I've debugged a reasoning loop bug in Step 3.7 Flash on llama.cpp that was caused by the parser capturing an extra `\n` as part of a reasoning block. It was something that only manifested at longer multi-turn agentic sessions, and the extra linefeed was steering the model into making reasoning self corrections that only got worse with longer sessions (more details about this issue: https://github.com/ggml-org/llama.cpp/issues/24181#issuecomm...)
No inference engine is perfect, but I feel that llama.cpp is the most reliable way to run language models locally.
Parsing/encoding is one example: A couple of months ago I've debugged a reasoning loop bug in Step 3.7 Flash on llama.cpp that was caused by the parser capturing an extra `\n` as part of a reasoning block. It was something that only manifested at longer multi-turn agentic sessions, and the extra linefeed was steering the model into making reasoning self corrections that only got worse with longer sessions (more details about this issue: https://github.com/ggml-org/llama.cpp/issues/24181#issuecomm...)
No inference engine is perfect, but I feel that llama.cpp is the most reliable way to run language models locally.