7 янв. 2024 г. · If you have 12 physical cores you can max out performance with 4 threads. If you set it to 11 threads, you'll peg 11 cores at 100% usage but it ...

1 апр. 2024 г. · This makes llama.cpp faster on CPU-only inference. It does not improve any scenario where the GPU is used, neither full nor partial ...

26 мар. 2024 г. · Use the Vulkan backend for llama.cpp. It's super easy and quite frankly, the ROCm isn't that much better to be worth the extra effort.

28 сент. 2023 г. · llama.cpp added support for LoRA finetuning using your CPU earlier today! I created a short(ish) guide on how to use it.

14 янв. 2024 г. · I'm currently using llama.cpp on my cpu only machine. I've heard a lot of good things about exllamav2 in terms of performance, just wondering if ...

16 апр. 2024 г. · On CPU inference, I'm getting a 30% speedup for prompt processing but only when llama.cpp is built with BLAS and OpenBLAS off.

31 мая 2023 г. · Set the thread count to the proper number of big cores in your device, and then set thread affinity to ensure they run on the right cores.

13 апр. 2024 г. · All I can say is that iq3xss is extremly slow on the cpu and iq4xs and q4ks are pretty similar in terms of cpu speed.

25 июн. 2023 г. · Using hyperthreading on all the cores, thus running llama.cpp with -t 32 on the 7950X3D results in 9% to 18% faster processing compared to 14 or ...

1 нояб. 2024 г. · I have a Lunar Lake laptop (see my in-progress Linux review) and recently sat down and did some testing on how llama.cpp works with it.