19 июн. 2023 г. · I tried optimizing the number of threads executing a model and I've seen great variation in performance by merely changing the number of executing threads.

3 апр. 2023 г. · It's the number of tokens in the prompt that are fed into the model at a time. For example, if your prompt is 8 tokens long at the batch size is 4, then it'll ...

26 июн. 2023 г. · The best number of threads is equal to the number of cores/threads (however many hyperthreads your CPU supports). Good performance (but not ...

12 дек. 2023 г. · The long and short is that Threads should equal the number of real cores you have, and threads_batch should equal how many threads you have.

7 янв. 2024 г. · It seems to me that llama.cpp threads are running a dead loop while they wait on data from memory. A thread should not be consuming all the ...

29 июл. 2024 г. · When I look at the available options when serving a model via llama.cpp, I'm amazed. What options, possibly non-obvious options, do you like to use?

19 сент. 2024 г. · Mistral.rs is a rust-based llama.cpp alternative, supports most of your requirements. vLLM / TGI if you can fit the models into VRAM, mostly Nvidia stuff.

26 мар. 2024 г. · Hi I have few questions regarding llama.cpp server: What are the disadvantages of continuous batching? I think there must be some, ...

14 июн. 2024 г. · Exllama V2 defaults to a prompt processing batch size of 2048, while llama.cpp defaults to 512. They are much closer if both batch sizes are set ...

8 июн. 2023 г. · I have an M1 MacBook Air, which is spec'd as 4 performance cores and 4 efficiency cores. Is it better to set N_THREAD for llama.cpp to 4 or 8 on this CPU?