{"id":63,"date":"2025-02-06T18:09:13","date_gmt":"2025-02-06T18:09:13","guid":{"rendered":"https:\/\/www.bgptrace.com\/blog\/?p=63"},"modified":"2025-02-06T18:13:15","modified_gmt":"2025-02-06T18:13:15","slug":"install-and-run-ollama-at-linux-machine","status":"publish","type":"post","link":"https:\/\/www.bgptrace.com\/blog\/?p=63","title":{"rendered":"Install and Run OLLAMA on Linux Machine"},"content":{"rendered":"\n

So many tech guys already share how to install the OLLAM. I wont say too details. Just a brief step for you.<\/p>\n\n\n\n

    \n
  1. Prepare a Machine with good GPU, CPU and > 16G RAM. (Raspberry Pi can run with the Deepseek 1.5B, other………. Please chec my last post)
    <\/li>\n\n\n\n
  2. Install update your linux repos.
    sudo apt-get update -y
    sudo apt-get upgrade -y<\/kbd>
    <\/li>\n\n\n\n
  3. Install Ollam by the follow command
    curl -fsSL https:\/\/ollama.com\/install.sh | sh<\/kbd>
    <\/li>\n\n\n\n
  4. Run the LLM model, if you wont have the model at your machine, it will be download automatically.
    ollama run <model>
    e.g.: ollama run deepseek-r1:8b<\/kbd>
    <\/li>\n\n\n\n
  5. The model will be downloaded to \/usr\/share\/ollama\/.ollama\/models\/
    <\/li>\n\n\n\n
  6. what model you can run? Check here
    https:\/\/ollama.com\/search<\/a>
    <\/li>\n\n\n\n
  7. OLLAMA command line is a little bit similar with Docker, check this.<\/li>\n<\/ol>\n\n\n\n
    \n
    \n
    \"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

    PS: You also can install OLLAMA at WINDOWS, please also check OLLAMA website.<\/p>\n\n\n\n

    Lets try your own AI locally!<\/mark><\/strong><\/p>\n\n\n\n

    #OLLAMA #Model #AI #CPU #GPU #CUDB #RAM #RaspberryPI #Docker<\/p>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    So many tech guys already share how to install the OLLAM. I wont say too details. Just a brief step for you. PS: You also can install OLLAMA at WINDOWS, please also check OLLAMA website. Lets try your own AI locally! #OLLAMA #Model #AI #CPU #GPU #CUDB #RAM #RaspberryPI #Docker<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[36,52,56,71,51,55,57,44,43,42],"class_list":["post-63","post","type-post","status-publish","format-standard","hentry","category-ai","tag-ai","tag-cpu","tag-cudb","tag-docker","tag-gpu","tag-model","tag-ollama","tag-ram","tag-raspberry-pi","tag-raspberrypi"],"_links":{"self":[{"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=\/wp\/v2\/posts\/63","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=63"}],"version-history":[{"count":7,"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=\/wp\/v2\/posts\/63\/revisions"}],"predecessor-version":[{"id":71,"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=\/wp\/v2\/posts\/63\/revisions\/71"}],"wp:attachment":[{"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=63"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=63"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bgptrace.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=63"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}