Most Efficient Large Language Models for AI PC

This page is regularly updated to help you identify the best-performing LLMs on the Intel® Core™ Ultra processor family and AI PCs.

The tables below list the key performance indicators for a selection of Large Language Models, running on an Intel® Core™ Ultra 7-165H based system, on built-in GPUs.

OpenVINO

Model name	Throughput: (tokens/sec. 2nd token)	1st token latency (msec)	Max RSS memory used. (MB)	Input tokens	Output tokens	Model Precision	Beam	Batch size	Framework
OPT-2.7b	20.2	2757	7084	937	128	INT4	1	1	PT
Phi-3-mini-4k-instruct	19.9	2776	7028	1062	128	INT4	1	1	PT
Orca-mini-3b	19.2	2966	7032	1024	128	INT4	1	1	PT
Phi-2	17.8	2162	7032	1024	128	INT4	1	1	PT
Stable-Zephyr-3b-dpo	17.0	1791	7007	946	128	INT4	1	1	PT
ChatGLM3-6b	16.5	3569	6741	1024	128	INT4	1	1	PT
Dolly-v2-3b	15.8	6891	6731	1024	128	INT4	1	1	PT
Stablelm-3b-4e1t	15.7	2051	7018	1024	128	INT4	1	1	PT
Red-Pajama-Incite-Chat-3b-V1	14.8	6582	7028	1020	128	INT4	1	1	PT
Falcon-7b-instruct	14.5	4552	7033	1049	128	INT4	1	1	PT
Codegen25-7b	13.3	3982	6732	1024	128	INT4	1	1	PT
GPT-j-6b	13.2	7213	6882	1024	128	INT4	1	1	PT
Stablelm-7b	12.8	6339	7013	1020	128	INT4	1	1	PT
Llama-3-8b	12.8	4356	6953	1024	128	INT4	1	1	PT
Llama-2-7b-chat	12.3	4205	6906	1024	128	INT4	1	1	PT
Llama-7b	11.7	4315	6927	1024	128	INT4	1	1	PT
Mistral-7b-v0.1	10.5	4462	7242	1007	128	INT4	1	1	PT
Zephyr-7b-beta	10.5	4500	7039	1024	128	INT4	1	1	PT
Qwen1.5-7b-chat	9.9	4318	7034	1024	128	INT4	1	1	PT
Baichuan2-7b-chat	9.8	4668	6724	1024	128	INT4	1	1	PT
Qwen-7b-chat	9.0	5141	6996	1024	128	INT4	1	1	PT

For complete information on the system config, see: Hardware Platforms [PDF]

To view the data in an editable form, you can download the .csv file here:

Click for OpenVINO LLM results [CSV]