{"id":87,"date":"2026-05-30T15:36:26","date_gmt":"2026-05-30T13:36:26","guid":{"rendered":"https:\/\/shop.bluedolomites.com\/guide-installer-llama-3-en-local-pas-a-pas-windows-et-linux\/"},"modified":"2026-05-30T15:36:26","modified_gmt":"2026-05-30T13:36:26","slug":"guide-installer-llama-3-en-local-pas-a-pas-windows-et-linux","status":"publish","type":"post","link":"https:\/\/shop.bluedolomites.com\/en\/guide-installer-llama-3-en-local-pas-a-pas-windows-et-linux\/","title":{"rendered":"Guide : Installer Llama 3 en local pas a pas (Windows et Linux)"},"content":{"rendered":"<p>Ce guide vous accompagne de A a Z pour installer et faire tourner Llama 3 sur votre propre machine. Aucune experience requise \u2014 suivez simplement les etapes.<\/p>\n<h2>Prerequis materiel<\/h2>\n<p>Minimum : 16 Go de RAM et un processeur recent (Intel 12e gen+ ou AMD Ryzen 5000+). Recommande : une <a href='\/recommends\/rtx-4070\/'>carte graphique NVIDIA RTX 4070<\/a> ou superieure pour des performances optimales. Sans GPU, ca fonctionne sur CPU mais 10x plus lentement.<\/p>\n<h2>Etape 1 \u2014 Installer Ollama (methode la plus simple)<\/h2>\n<p><strong>Windows :<\/strong> Telechargez Ollama depuis <code>ollama.com<\/code>. Lancez l installeur. C est tout.<\/p>\n<p><strong>Linux :<\/strong> Ouvrez un terminal et tapez :<\/p>\n<pre><code>curl -fsSL https:\/\/ollama.com\/install.sh | sh<\/code><\/pre>\n<p>Ollama s installe et demarre automatiquement en arriere-plan.<\/p>\n<h2>Etape 2 \u2014 Telecharger Llama 3<\/h2>\n<p>Dans votre terminal (PowerShell sur Windows, Terminal sur Linux) :<\/p>\n<pre><code>ollama pull llama3.1:8b<\/code><\/pre>\n<p>Le modele fait environ 4.7 Go. Le telechargement prend 5 a 15 minutes selon votre connexion. Pour la version plus performante (necessite 16+ Go de VRAM) :<\/p>\n<pre><code>ollama pull llama3.1:70b<\/code><\/pre>\n<h2>Etape 3 \u2014 Discuter avec Llama<\/h2>\n<p>Tapez simplement :<\/p>\n<pre><code>ollama run llama3.1:8b<\/code><\/pre>\n<p>Vous etes maintenant en conversation avec Llama 3 ! Tapez vos questions, le modele repond. Tapez <code>\/bye<\/code> pour quitter.<\/p>\n<h2>Etape 4 \u2014 Interface graphique (optionnel)<\/h2>\n<p>Pour une interface type ChatGPT, installez <strong>Open WebUI<\/strong> :<\/p>\n<pre><code>docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway ghcr.io\/open-webui\/open-webui:main<\/code><\/pre>\n<p>Ouvrez votre navigateur sur <code>http:\/\/localhost:3000<\/code>. Vous avez votre propre ChatGPT prive.<\/p>\n<h2>Methode alternative \u2014 llama.cpp (plus de controle)<\/h2>\n<p>Pour les utilisateurs avances qui veulent un controle total :<\/p>\n<pre><code># Cloner le repo\ngit clone https:\/\/github.com\/ggerganov\/llama.cpp\ncd llama.cpp\n\n# Compiler (Linux\/Mac)\nmake -j\n\n# Ou avec support GPU CUDA (NVIDIA)\nmake -j GGML_CUDA=1\n\n# Telecharger un modele GGUF depuis huggingface.co\n# Lancer\n.\/llama-cli -m votre-modele.gguf -p \"Bonjour, comment vas-tu ?\" -n 256<\/code><\/pre>\n<h2>Methode Python \u2014 llama-cpp-python<\/h2>\n<p>Pour integrer dans vos scripts Python :<\/p>\n<pre><code>pip install llama-cpp-python\n\n# Dans votre script :\nfrom llama_cpp import Llama\nllm = Llama(model_path=\".\/modele.gguf\", n_ctx=2048, n_gpu_layers=0)\nresponse = llm(\"Explique-moi le machine learning\", max_tokens=512)\nprint(response[\"choices\"][0][\"text\"])<\/code><\/pre>\n<h2>Performances attendues<\/h2>\n<p>Sur CPU (pas de GPU) : 5-10 tokens\/seconde \u2014 utilisable mais lent. Sur <a href='\/recommends\/rtx-4070\/'>RTX 4070<\/a> : 40-60 tokens\/seconde \u2014 fluide. Sur <a href='\/recommends\/rtx-5090\/'>RTX 5090<\/a> : 120+ tokens\/seconde \u2014 instantane.<\/p>\n<h2>Quel modele choisir<\/h2>\n<p>Llama 3.1 8B : le meilleur rapport taille\/qualite, tourne sur n importe quel PC recent. Qwen 2.5 7B : excellent en francais. Mistral 7B : rapide et polyvalent. Pour essayer : <code>ollama pull qwen2.5:7b<\/code><\/p>\n<p>\ud83d\udc49 <a href='\/recommends\/rtx-4070\/'>GPU recommande : RTX 4070 Ti<\/a> | <a href='\/recommends\/ssd-nvme\/'>SSD pour stocker les modeles<\/a> | <a href='\/recommends\/ram-ddr5\/'>RAM 32 Go DDR5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Ce guide vous accompagne de A a Z pour installer et faire tourner Llama 3 sur votre propre machine. Aucune experience requise \u2014 suivez simplement les etapes. Prerequis materiel Minimum : 16 Go de RAM et un processeur recent (Intel 12e gen+ ou AMD Ryzen 5000+). Recommande : une carte graphique NVIDIA RTX 4070 ou [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":110,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25],"tags":[],"class_list":["post-87","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-guides-pratiques"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Guide : Installer Llama 3 en local pas a pas (Windows et Linux) - Blue Dolomites Shop<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/shop.bluedolomites.com\/en\/guide-installer-llama-3-en-local-pas-a-pas-windows-et-linux\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Guide : Installer Llama 3 en local pas a pas (Windows et Linux) - Blue Dolomites Shop\" \/>\n<meta property=\"og:description\" content=\"Ce guide vous accompagne de A a Z pour installer et faire tourner Llama 3 sur votre propre machine. 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