{"id":791,"date":"2026-06-06T01:59:15","date_gmt":"2026-06-06T01:59:15","guid":{"rendered":"https:\/\/convly.ai\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/"},"modified":"2026-06-06T01:59:15","modified_gmt":"2026-06-06T01:59:15","slug":"ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/","title":{"rendered":"Ollama vs LM Studio vs vLLM vs llama.cpp: Which Should You Use in 2026?"},"content":{"rendered":"<p>&#8220;What should I use to run LLMs locally?&#8221; is the most common question in local AI, and the honest answer is: it depends on whether you&#8217;re one developer prototyping or a team serving thousands of requests. These four tools are not really competitors \u2014 they solve different problems. This guide sorts out which is which.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li><strong>Ollama<\/strong> \u2014 best for one-developer prototyping on any OS. Lowest friction, the &#8220;lowest regret&#8221; default.<\/li>\n<li><strong>LM Studio<\/strong> \u2014 best if you want a polished GUI to browse, download, and chat with models. The only full-featured desktop app of the four.<\/li>\n<li><strong>vLLM<\/strong> \u2014 best for multi-user production serving on GPUs. Roughly <strong>16\u201320\u00d7 Ollama&#8217;s throughput<\/strong> under concurrent load thanks to PagedAttention and continuous batching.<\/li>\n<li><strong>llama.cpp<\/strong> \u2014 the engine the others are built on. Use it directly for maximum speed or embedded\/edge hardware.<\/li>\n<li>Most people should <strong>start with Ollama<\/strong> and only graduate to vLLM when concurrency becomes the bottleneck.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a23c7d1ae0dc\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a23c7d1ae0dc\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/#Theyre_not_the_same_kind_of_thing\" >They&#8217;re not the same kind of thing<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/#Head-to-head_comparison\" >Head-to-head comparison<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/#The_performance_gap_that_matters\" >The performance gap that matters<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/#Apple_Silicon_changed_the_math_in_2026\" >Apple Silicon changed the math in 2026<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/#Which_one_should_you_actually_pick\" >Which one should you actually pick?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/#Bottom_line\" >R\u00e9sultat<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Theyre_not_the_same_kind_of_thing\"><\/span>They&#8217;re not the same kind of thing<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The single biggest source of confusion is treating these as four versions of one product. They sit at different layers of the stack:<\/p>\n<ul>\n<li><strong>llama.cpp and MLX are engines<\/strong> \u2014 the low-level code that runs the math of a quantized model on your hardware.<\/li>\n<li><strong>Ollama and LM Studio are experience layers<\/strong> \u2014 they both wrap <code>llama.cpp<\/code> (and increasingly MLX on Mac) and add model management, a friendly interface, and an API.<\/li>\n<li><strong>vLLM is a serving system<\/strong> \u2014 built from the ground up for high-throughput GPU serving, not local-first development.<\/li>\n<\/ul>\n<p>Once you see it this way, the choice gets simpler: pick the layer that matches your job.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Head-to-head_comparison\"><\/span>Head-to-head comparison<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>Ollama<\/th>\n<th>LM Studio<\/th>\n<th>vLLM<\/th>\n<th>llama.cpp<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Interface<\/td>\n<td>CLI + API<\/td>\n<td>Full GUI<\/td>\n<td>API \/ server<\/td>\n<td>CLI \/ library<\/td>\n<\/tr>\n<tr>\n<td>Setup difficulty<\/td>\n<td>Very easy<\/td>\n<td>Very easy<\/td>\n<td>Hard<\/td>\n<td>Mod\u00e9r\u00e9<\/td>\n<\/tr>\n<tr>\n<td>Best OS<\/td>\n<td>Tous<\/td>\n<td>Mac \/ Windows<\/td>\n<td>Linux + NVIDIA\/AMD<\/td>\n<td>Tous<\/td>\n<\/tr>\n<tr>\n<td>Concurrency<\/td>\n<td>Weak<\/td>\n<td>Weak<\/td>\n<td>Excellent<\/td>\n<td>Mod\u00e9r\u00e9<\/td>\n<\/tr>\n<tr>\n<td>Raw single-user speed<\/td>\n<td>Bon<\/td>\n<td>Bon<\/td>\n<td>Bon<\/td>\n<td>Le plus rapide<\/td>\n<\/tr>\n<tr>\n<td>Quant format<\/td>\n<td>GGUF \/ MLX<\/td>\n<td>GGUF \/ MLX<\/td>\n<td>Full + AWQ\/GPTQ<\/td>\n<td>GGUF<\/td>\n<\/tr>\n<tr>\n<td>Production-ready<\/td>\n<td>Entry-level<\/td>\n<td>Non<\/td>\n<td>Oui<\/td>\n<td>With work<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"The_performance_gap_that_matters\"><\/span>The performance gap that matters<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For a single user typing one prompt at a time, all four feel fast. The differences explode the moment you send <strong>concurrent requests<\/strong>.<\/p>\n<p>In 2026 production benchmarks, vLLM&#8217;s architecture \u2014 PagedAttention plus continuous batching \u2014 pulls dramatically ahead under load. At peak throughput, community tests put <strong>vLLM at roughly 793 tokens\/sec versus Ollama&#8217;s ~41 tokens\/sec<\/strong>, with P99 latency at peak of about 80 ms for vLLM against 673 ms for Ollama. That&#8217;s the 16\u201320\u00d7 gap people quote, and it&#8217;s real \u2014 but it only appears when many users hit the model at once.<\/p>\n<p>The lesson: <strong>throughput numbers measure a serving problem, not a prototyping problem.<\/strong> If you&#8217;re the only user, Ollama&#8217;s &#8220;slower&#8221; number is irrelevant \u2014 you&#8217;ll never notice it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Apple_Silicon_changed_the_math_in_2026\"><\/span>Apple Silicon changed the math in 2026<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you&#8217;re on a Mac, there&#8217;s a recent twist. On March 30, 2026, Ollama announced its Apple Silicon path is now powered by <strong>MLX<\/strong> rather than just the Metal <code>llama.cpp<\/code> backend. The speedup was large: on an M5 Max running Qwen 3.5, prefill jumped about 57% and decode roughly 93% faster than the previous build. LM Studio also offers an MLX path. For Mac users, this narrowed the single-user speed gap considerably and made Ollama and LM Studio genuinely fast, not just convenient.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Which_one_should_you_actually_pick\"><\/span>Which one should you actually pick?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Pick Ollama if<\/strong> you&#8217;re a developer who wants to prototype, script against an API, and not think about infrastructure. It&#8217;s the lowest-regret default and the easiest to automate. Start here \u2014 read our <a href=\"https:\/\/convly.ai\/fr\/what-is-ollama-complete-guide-2026\/\">complete guide to Ollama<\/a> if you&#8217;re new to it.<\/p>\n<p><strong>Pick LM Studio if<\/strong> you want a graphical app to discover, download, and chat with models without touching a terminal \u2014 especially on a Mac or Windows laptop. It&#8217;s the best &#8220;just let me click around&#8221; experience.<\/p>\n<p><strong>Pick vLLM if<\/strong> you&#8217;re putting a model in front of real users and need to serve many requests per second. The setup cost is real, but nothing else matches its concurrent throughput.<\/p>\n<p><strong>Pick llama.cpp directly if<\/strong> you need the absolute fastest single-stream inference, are deploying to embedded or unusual hardware, or want to embed inference in your own binary.<\/p>\n<p>A common and sensible path: <strong>prototype on Ollama, ship on vLLM.<\/strong> You validate the idea with zero friction, then move the proven workload to a serving stack when concurrency demands it. To choose the right model to run on either, see our pick of the <a href=\"https:\/\/convly.ai\/fr\/best-local-llms-to-run-on-ollama-2026\/\">best local LLMs in 2026<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is vLLM faster than Ollama?<\/h3>\n<p>Under concurrent load, dramatically \u2014 roughly 16\u201320\u00d7 higher throughput in 2026 benchmarks, because vLLM was built for serving with PagedAttention and continuous batching. For a single user sending one request at a time, the difference is negligible. vLLM&#8217;s advantage is throughput, not single-prompt latency.<\/p>\n<h3>Is LM Studio better than Ollama?<\/h3>\n<p>For non-developers, often yes \u2014 LM Studio&#8217;s GUI makes browsing and running models effortless with no terminal. For developers who want to script, automate, or integrate a local model into an app, Ollama&#8217;s CLI and API are more flexible. They&#8217;re built on the same engine, so model quality is identical.<\/p>\n<h3>Do Ollama and LM Studio use llama.cpp?<\/h3>\n<p>Yes. Both are experience layers that wrap <code>llama.cpp<\/code> (and Apple&#8217;s MLX on Apple Silicon). That&#8217;s why they run the same GGUF models at similar speeds \u2014 the underlying engine is shared. The difference is the interface and the management features around it.<\/p>\n<h3>What about llama.cpp vs Ollama directly?<\/h3>\n<p>llama.cpp is the engine; Ollama is a friendly wrapper around it. Running llama.cpp directly gives you the fastest single-stream performance and the most control, at the cost of doing the setup, model conversion, and flag-tuning yourself. Ollama trades a little speed for enormous convenience.<\/p>\n<h3>Which is best for production?<\/h3>\n<p>vLLM, clearly, if &#8220;production&#8221; means serving multiple concurrent users on GPUs. Ollama is fine for low-traffic internal tools or single-user desktop apps. llama.cpp can be productionized with effort. LM Studio is a desktop tool and not meant for server deployment.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>R\u00e9sultat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Stop thinking of these as four competing products and start thinking of them as four jobs. Ollama is the on-ramp, LM Studio is the GUI, vLLM is the server, and llama.cpp is the engine underneath. For most people reading this, the answer is: start with Ollama today, and reach for vLLM the day concurrency \u2014 not curiosity \u2014 becomes your constraint.<\/p>","protected":false},"excerpt":{"rendered":"<p>Four tools, four jobs. Ollama and LM Studio are experience layers, llama.cpp is the engine, and vLLM is a production server. Here&#8217;s exactly which one to pick \u2014 and when.<\/p>","protected":false},"author":1,"featured_media":797,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[3],"tags":[647,260,256,645,648,646],"class_list":["post-791","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-llms","tag-llama-cpp-vs-ollama","tag-lm-studio","tag-local-llm","tag-ollama-vs-lm-studio","tag-vllm","tag-vllm-vs-ollama"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/791","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/comments?post=791"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/791\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/797"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=791"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=791"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=791"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}