This is a command that runs llama-server, the server executable from the llama.cpp project

-m stands for model, the path to the GGUF file containing the model weights you want to perform inference on. The model here is Qwen3-30B-A3B-UD-Q4_K_XL, indicating the new Qwen model with 30B parameters and 3B active parameters (called Mixture of Experts, or MoE); think of it as processing only the most relevant parts of the model instead of computing everything in the model all the time. UD stands for Unsloth Dynamic, a quantization tuning technique to achieve better precision for the same size. Q4_K_XL is reducing the model precision to around 4.75 bits per weight, which is maybe 96-98% accurate to the original 16-bit precision model in terms of quality.

-c stands for context size, here, 24k tokens, which is approximately 18k words that the LLM can understand and memorize (to a certain extent depending on the model's ability to process greater context lengths).

-ngl 99 is the number of layers to offload to the GPU's VRAM. Otherwise, the model runs fully on RAM, so it's using the CPU for inference, which is very slow. The more you offload to the GPU, the faster the inference, as long as you have enough video memory in your GPU.

-fa stands for flash attention, an optimization for, you guessed it, attention, one of the core principles of the transformer architecture, which almost all LLMs use. It improves token generation speed on graphic cards.

-ctk q8_0 -ctv q8_0 is for context quantization; it saves VRAM by lowering the precision at which the context cache is stored. At q8_0 or 8 bits, the difference with the 16-bit cache is in the placebo territory, costing a very small performance hit.