apt update
apt install -y gpg-agent wget
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list
apt update

apt install -y intel-oneapi-base-toolkit
#

. /opt/intel/oneapi/2025.1/oneapi-vars.sh
oneapi-cli

#apt -y install intel-cpp-essentials
#apt -y install cmake pkg-config build-essential

wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/491d5c2a-67fe-48d0-884f-6aecd88f5d8a/ai-tools-2025.0.0.75_offline.sh
sh ai-tools-2025.0.0.75_offline.sh

wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
echo "deb https://apt.repos.intel.com/openvino ubuntu24 main" | sudo tee /etc/apt/sources.list.d/intel-openvino.list
sudo apt update
apt-cache search openvino
sudo apt install openvino-2025.1.0

wget https://github.com/openvinotoolkit/model_server/releases/download/v2025.1/ovms_ubuntu24_python_on.tar.gz
tar -xzvf ovms_ubuntu24_python_on.tar.gz
export LD_LIBRARY_PATH=${PWD}/ovms/lib
export PATH=$PATH:${PWD}/ovms/bin
curl --create-dirs -k https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/resnet50-binary-0001/FP32-INT1/resnet50-binary-0001.xml -o models/resnet50/1/model.xml
curl --create-dirs -k https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/2/resnet50-binary-0001/FP32-INT1/resnet50-binary-0001.bin -o models/resnet50/1/model.bin
chmod -R 755 models
export PYTHONPATH=${PWD}/ovms/lib/python
sudo apt -y install libpython3.12
pip3 install "Jinja2==3.1.6" "MarkupSafe==3.0.2"
ovms --port 9000 --model_name resnet --model_path models/resnet50

sudo apt update
sudo apt upgrade
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.11 -y
sudo apt install python3.11-venv -y
python3.11 -V
python3.11 -m venv llm_env
source llm_env/bin/activate
pip install --pre --upgrade ipex-llm[cpp]
mkdir llama-cpp
cd llama-cpp
# Run Ollama Serve with Intel GPU
export OLLAMA_NUM_GPU=128
export no_proxy=localhost,127.0.0.1
export ZES_ENABLE_SYSMAN=1
source /opt/intel/oneapi/setvars.sh
export SYCL_CACHE_PERSISTENT=1
# localhost access
# ./ollama serve
# for non-localhost access
OLLAMA_HOST=0.0.0.0 ./ollama serve

(base) root@server1:~/llama-cpp# ./ollama list
NAME                    ID              SIZE      MODIFIED
phi3:14b                cf611a26b048    7.9 GB    3 minutes ago
llama3.3:70b            a6eb4748fd29    42 GB     16 minutes ago
mistral-small3.1:24b    b9aaf0c2586a    15 GB     23 minutes ago
llama4:scout            4f01ed6b6e01    67 GB     56 minutes ago
openchat:7b             537a4e03b649    4.1 GB    About an hour ago
qwen3:32b               e1c9f234c6eb    20 GB     2 hours ago
gemma3:27b              a418f5838eaf    17 GB     2 hours ago
deepseek-r1:70b         0c1615a8ca32    42 GB     3 hours ago

(base) root@server1:~/llama-cpp# ./ollama list
NAME               ID              SIZE     MODIFIED
qwen3:32b          e1c9f234c6eb    20 GB    28 minutes ago
gemma3:27b         a418f5838eaf    17 GB    37 minutes ago
deepseek-r1:70b    0c1615a8ca32    42 GB    About an hour ago
(base) root@server1:~/llama-cpp# ./ollama pull openchat:7b
pulling manifest
pulling 1cecc26325a1... 100% ▕████████████████████████████████████████████████████████████████████████████████ ▏ 4.1 GB/4.1 GB  102 MB/s      0s
pulling 43070e2d4e53... 100% ▕████████████████████████████████████████████████████████████████████████████████▏  11 KB
pulling d68706c17530... 100% ▕████████████████████████████████████████████████████████████████████████████████▏   98 B
pulling 415f0f6b43dd... 100% ▕████████████████████████████████████████████████████████████████████████████████▏   65 B
pulling 278996753456... 100% ▕████████████████████████████████████████████████████████████████████████████████▏  483 B
verifying sha256 digest
writing manifest
success

source llm_env/bin/activate
#pip install open-webui==0.2.5
pip install open-webui # 0.6.10
open-webui serve

git clone https://github.com/tabletuser-blogspot/ollama-benchmark
cd ollama-benchmark/
chmod +x obench.sh
time ./obench.sh

curl -fsSL https://ollama.com/install.sh | sh

>>> Installing ollama to /usr/local
>>> Downloading Linux amd64 bundle
######################################################################## 100.0%
>>> Creating ollama user...
>>> Adding ollama user to render group...
>>> Adding ollama user to video group...
>>> Adding current user to ollama group...
>>> Creating ollama systemd service...
>>> Enabling and starting ollama service...
Created symlink /etc/systemd/system/default.target.wants/ollama.service → /etc/systemd/system/ollama.service.
>>> The Ollama API is now available at 127.0.0.1:11434.
>>> Install complete. Run "ollama" from the command line.
WARNING: No NVIDIA/AMD GPU detected. Ollama will run in CPU-only mode.

ollama pull mistral-small3.1:24b

rm -Rf /usr/share/ollama/.ollama/models/
mv /root/.ollama/models/ /usr/share/ollama/.ollama/models/
ln -s /usr/share/ollama/.ollama/models/ /root/.ollama/models
ollama list

(base) root@server1:~# ollama list
NAME                    ID              SIZE      MODIFIED
phi3:14b                cf611a26b048    7.9 GB    23 minutes ago
llama3.3:70b            a6eb4748fd29    42 GB     36 minutes ago
mistral-small3.1:24b    b9aaf0c2586a    15 GB     43 minutes ago
llama4:scout            4f01ed6b6e01    67 GB     About an hour ago
openchat:7b             537a4e03b649    4.1 GB    2 hours ago
qwen3:32b               e1c9f234c6eb    20 GB     3 hours ago
gemma3:27b              a418f5838eaf    17 GB     3 hours ago
deepseek-r1:70b         0c1615a8ca32    42 GB     4 hours ago

(base) root@server1:~# ollama --version
ollama version is 0.7.0

apt install -y libcurl-ocaml-dev
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build
cmake --build build --config Release
cd build
make install
ldconfig

llama-cli -m model.gguf
llama-server -m model.gguf --port 8080
llama-bench -m model.gguf
llama-run 

#!/bin/bash
# Benchmark using ollama gives rate of tokens per second
# idea taken from https://taoofmac.com/space/blog/2024/01/20/1800
# other colors
#Black          0;30    Dark Gray       1;30
#Red            0;31    Light Red       1;31
#Green          0;32    Light Green   1;32
#Brown/Orange 0;33      Yellow          1;33
#Blue           0;34    Light Blue      1;34
#Purple         0;35    Light Purple  1;35
#Cyan           0;36    Light Cyan      1;36
#Light Gray   0;37      White           1;37
#ANSI option
#RED='\033[0;31m'
#NC='\033[0m' # No Color
#echo -e "${red}Hello Stackoverflow${NC}"
#set -e used for troubleshooting
set -e
#colors available
borange='\e[0;33m'
yellow='\e[1;33m'
purple='\e[0;35m'
green='\e[0;32m'
red='\e[0;31m'
blue='\e[0;34m'
NC='\e[0m' # No Color
cpu_def=$(cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor)
echo "Setting cpu governor to"
sudo echo performance | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
gpu_avail=$(sudo lshw -C display | grep product: | head -1 | cut -c17-)
cpugover=$(cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor)
cpu_used=$(lscpu | grep 'Model name' | cut -f 2 -d ":" | awk '{$1=$1}1')
echo ""
echo "Simple benchmark using ollama and"
echo "whatever local Model is installed."
echo "Does not identify if $gpu_avail is benchmarking"
echo ""
benchmark=3
echo "How many times to run the benchmark?"
echo  $benchmark
echo ""
for model in `ollama ls |awk '{print $1}'|grep -v NAME`; do
 echo -e "Total runs "${purple}$benchmark${NC}
 echo ""
 #echo "Current models available locally"
 #echo ""
 #ollama list
 #echo ""
 #echo "Example enter tinyllama or dolphin-phi"
 echo ""
 echo $model
 ollama show $model --system
 echo ""
 echo -e "Will use model: "${green}$model${NC}
 echo ""
 echo -e Will benchmark the tokens per second for ${cpu_used} and or ${gpu_avail}
 touch "${cpu_used}".txt
 echo "" > "${cpu_used}".txt
 echo ""
 echo -e Running benchmark ${purple}$benchmark${NC} times for ${cpu_used} and or ${gpu_avail}
 echo -e with ${borange}$cpugover${NC} setting for cpu governor
 echo ""
 for run in $(seq 1 $benchmark); do
  echo "Why is the blue sky blue?" | ollama run $model --verbose 2>&1 >/dev/null | grep "eval rate:" | tee -a "${cpu_used}".txt ;
  avg=$(cat "${cpu_used}".txt | grep -v "prompt eval rate:" | awk '{print $3}' | awk 'NR>1{ tot+=$1 } END{ print tot/(NR-1) }')
 done
 echo ""
 echo -e ${red}$avg${NC} is the average ${blue}tokens per second${NC} using ${green}$model${NC} model
 echo for $cpu_used and or $gpu_avail
done
echo
echo -e using ${borange}$cpugover${NC} for cpu governor.
echo ""
echo "Setting cpu governor to"
sudo echo $cpu_def | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
#comment this out if you are repeating the same model
#this clears model from Vram
sudo systemctl stop ollama; sudo systemctl start ollama
#EOF

(base) root@server1:~/ollama-benchmark# ./batch-obench.sh
Setting cpu governor to
performance

Simple benchmark using ollama and
whatever local Model is installed.
Does not identify if Meteor Lake-P [Intel Arc Graphics] is benchmarking

How many times to run the benchmark?
3

Total runs 3


deepseek-v2:16b

Will use model: deepseek-v2:16b

Will benchmark the tokens per second for Intel(R) Core(TM) Ultra 9 185H Intel(R) Core(TM) Ultra 9 185H To Be Filled By O.E.M. CPU @ 4.4GHz and or Meteor Lake-P [Intel Arc Graphics]

Running benchmark 3 times for Intel(R) Core(TM) Ultra 9 185H Intel(R) Core(TM) Ultra 9 185H To Be Filled By O.E.M. CPU @ 4.4GHz and or Meteor Lake-P [Intel Arc Graphics]
with performance setting for cpu governor

prompt eval rate:     56.10 tokens/s
eval rate:            25.88 tokens/s
prompt eval rate:     365.68 tokens/s
eval rate:            24.62 tokens/s
prompt eval rate:     377.67 tokens/s
eval rate:            24.64 tokens/s

...