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detects your GPU or Mac in the browser and grades 104 open AI models by VRAM fit and speed. Free, no account, with runai to launch them locally.\"\u003e\n\u003cdiv class=\"hero\"\u003e\u003cdiv class=\"hero-grid\"\u003e\n\u003cdiv\u003e\n\u003cdiv class=\"hero-head\"\u003e\n\u003cdiv class=\"logo-tile\"\u003e\u003cimg src=\"https:\/\/canirun.ai\/favicon.svg\" alt=\"CanIRun.ai logo\" itemprop=\"image\" loading=\"lazy\"\u003e\u003c\/div\u003e\n\u003cdiv class=\"name-block\"\u003e\n\u003cspan class=\"eyebrow\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpath d=\"m12 3-1.9 5.8a2 2 0 0 1-1.3 1.3L3 12l5.8 1.9a2 2 0 0 1 1.3 1.3L12 21l1.9-5.8a2 2 0 0 1 1.3-1.3L21 12l-5.8-1.9a2 2 0 0 1-1.3-1.3Z\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Edge On Device · Inference Hosting\u003c\/span\u003e\u003ch1 class=\"title\" itemprop=\"name\"\u003eCanIRun.ai\u003c\/h1\u003e\n\u003cp class=\"lede justify\" itemprop=\"description\"\u003eCanIRun.ai detects your GPU or Apple chip in the browser and grades 104 open-weight models, from chat and coding to image and video, by whether they actually fit, with VRAM requirements and speed estimates. Free, no account required.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"badge-row\"\u003e\n\u003cspan class=\"badge ok\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003ccircle cx=\"12\" cy=\"12\" r=\"10\"\u003e\u003c\/circle\u003e\u003cpath d=\"m9 12 2 2 4-4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Active\u003c\/span\u003e\u003cspan class=\"badge brand\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 12 20 22 4 22 4 12\"\u003e\u003c\/polyline\u003e\u003crect x=\"2\" y=\"7\" width=\"20\" height=\"5\"\u003e\u003c\/rect\u003e\u003cline x1=\"12\" y1=\"22\" x2=\"12\" y2=\"7\"\u003e\u003c\/line\u003e\u003cpath d=\"M12 7H7.5a2.5 2.5 0 0 1 0-5C11 2 12 7 12 7z\"\u003e\u003c\/path\u003e\u003cpath d=\"M12 7h4.5a2.5 2.5 0 0 0 0-5C13 2 12 7 12 7z\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Free plan\u003c\/span\u003e\u003cspan class=\"badge\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"22 7 13.5 15.5 8.5 10.5 2 17\"\u003e\u003c\/polyline\u003e\u003cpolyline points=\"16 7 22 7 22 13\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e Free\u003c\/span\u003e\u003cspan class=\"badge\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpath d=\"M6.3 20.3a2.4 2.4 0 0 0 3.4 0L12 18l-6-6-2.3 2.3a2.4 2.4 0 0 0 0 3.4Z\"\u003e\u003c\/path\u003e\u003cpath d=\"m2 22 3-3\"\u003e\u003c\/path\u003e\u003cpath d=\"M7.5 13.5 10 11\"\u003e\u003c\/path\u003e\u003cpath d=\"M10.5 16.5 13 14\"\u003e\u003c\/path\u003e\u003cpath d=\"m18 3-4 4h6l-4 4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e No public API\u003c\/span\u003e\u003cspan class=\"badge brand\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003ccircle cx=\"12\" cy=\"12\" r=\"10\"\u003e\u003c\/circle\u003e\u003cpath d=\"m9 12 2 2 4-4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Verified by Guidaio\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"tbd-row\"\u003e\n\u003cdiv\u003e\n\u003cdiv class=\"label\"\u003eCategories\u003c\/div\u003e\n\u003cdiv class=\"chip-row\"\u003e\n\u003ca class=\"chip cat\" href=\"\/collections\/edge-on-device\"\u003eEdge On Device\u003c\/a\u003e\u003ca class=\"chip cat\" href=\"\/collections\/inference-hosting\"\u003eInference Hosting\u003c\/a\u003e\u003ca class=\"chip cat\" href=\"\/collections\/evaluation-benchmarks\"\u003eEvaluation Benchmarks\u003c\/a\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv class=\"label\"\u003eTarget domains\u003c\/div\u003e\n\u003cdiv class=\"chip-row\"\u003e\n\u003ca class=\"chip dom\" href=\"\/collections\/data-ai\"\u003eData Ai\u003c\/a\u003e\u003ca class=\"chip dom\" href=\"\/collections\/it-software\"\u003eIt Software\u003c\/a\u003e\u003ca class=\"chip dom\" href=\"\/collections\/design-creative\"\u003eDesign Creative\u003c\/a\u003e\u003ca class=\"chip dom\" href=\"\/collections\/film-tv-video\"\u003eFilm Tv Video\u003c\/a\u003e\u003ca class=\"chip dom\" href=\"\/collections\/education-academia\"\u003eEducation Academia\u003c\/a\u003e\u003ca class=\"chip dom\" href=\"\/collections\/freelancers-entrepreneurs\"\u003eFreelancers Entrepreneurs\u003c\/a\u003e\u003ca class=\"chip dom\" href=\"\/collections\/science-r-d\"\u003eScience R D\u003c\/a\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"cta-row\"\u003e\n\u003ca class=\"btn btn-primary\" href=\"https:\/\/canirun.ai\/\" target=\"_blank\" rel=\"noopener\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"16\" height=\"16\"\u003e\u003cpath d=\"M15 3h6v6\"\u003e\u003c\/path\u003e\u003cpath d=\"M10 14 21 3\"\u003e\u003c\/path\u003e\u003cpath d=\"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Check your hardware\u003c\/a\u003e\u003ca class=\"btn btn-secondary\" href=\"https:\/\/canirun.ai\/playground\" target=\"_blank\" rel=\"noopener\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolygon points=\"6 3 20 12 6 21 6 3\"\u003e\u003c\/polygon\u003e\u003c\/svg\u003e Run a model locally\u003c\/a\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003caside class=\"snapshot\"\u003e\u003ch3\u003eSnapshot\u003c\/h3\u003e\n\u003cdl\u003e\n\u003cdiv class=\"row\"\u003e\n\u003cdt\u003ePricing model\u003c\/dt\u003e\n\u003cdd\u003eFree\u003c\/dd\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row\"\u003e\n\u003cdt\u003eFree plan\u003c\/dt\u003e\n\u003cdd\u003eYes\u003c\/dd\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row\"\u003e\n\u003cdt\u003eAPI available\u003c\/dt\u003e\n\u003cdd\u003eNo\u003c\/dd\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row\"\u003e\n\u003cdt\u003eDeployment\u003c\/dt\u003e\n\u003cdd\u003eWeb app\u003c\/dd\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row\"\u003e\n\u003cdt\u003eLanguages\u003c\/dt\u003e\n\u003cdd\u003eEnglish\u003c\/dd\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row\"\u003e\n\u003cdt\u003eTool status\u003c\/dt\u003e\n\u003cdd\u003eActive\u003c\/dd\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"row\"\u003e\n\u003cdt\u003eCountry of origin\u003c\/dt\u003e\n\u003cdd\u003e🇪🇸 Spain\u003c\/dd\u003e\n\u003c\/div\u003e\n\u003c\/dl\u003e\u003c\/aside\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cnav class=\"toc\" aria-label=\"Page sections\"\u003e\u003ca href=\"#what\"\u003eWhat is it\u003c\/a\u003e\u003ca href=\"#bestnot\"\u003eWhen to use CanIRun.ai \/ When not to\u003c\/a\u003e\u003ca href=\"#how\"\u003eHow to use\u003c\/a\u003e\u003ca href=\"#proscons\"\u003ePros \u0026amp; Cons\u003c\/a\u003e\u003ca href=\"#pricing\"\u003ePricing\u003c\/a\u003e\u003ca href=\"#trust\"\u003eTrust \u0026amp; Privacy\u003c\/a\u003e\u003ca href=\"#watch\"\u003eWatch-outs\u003c\/a\u003e\u003ca href=\"#setup\"\u003eSetup \u0026amp; Integrations\u003c\/a\u003e\u003ca href=\"#company\"\u003eCompany\u003c\/a\u003e\u003ca href=\"#resources\"\u003eResources\u003c\/a\u003e\u003ca href=\"#faq\"\u003eFAQ\u003c\/a\u003e\u003ca href=\"#conclusion\"\u003eConclusion\u003c\/a\u003e\u003c\/nav\u003e\n\u003csection class=\"block\" id=\"what\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eOverview\u003c\/span\u003e\u003ch2\u003eWhat is CanIRun.ai?\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"grid-2\"\u003e\n\u003cdiv class=\"rich justify\"\u003e\n\u003cp\u003e\u003cb\u003eCanIRun.ai\u003c\/b\u003e is a free public site built around a single question: can this machine run an open-weight AI model, and how well? It ignores hosted assistants entirely and covers only models you can download and execute yourself.\u003c\/p\u003e\n\u003cp\u003eEverything starts in the browser. Three APIs identify the hardware: WebGL, through the WEBGL_debug_renderer_info extension, returns the GPU name and vendor; WebGPU exposes the adapter's device and architecture; and the Navigator object supplies the core count and approximate RAM, completed by a short CPU benchmark of roughly 30 milliseconds. The result is matched against a built-in database of around forty NVIDIA, AMD and Intel GPUs and about a dozen Apple Silicon chips, each carrying its memory capacity and bandwidth.\u003c\/p\u003e\n\u003cp\u003eFrom there the site computes, for every model, the VRAM required at each quantization level: parameters multiplied by bits per weight, divided by eight, plus a fixed 0.5 GB runtime overhead and a ten percent margin for the KV cache. A 70B model at Q4_K_M therefore lands near 39 GB. Each model then receives a score out of 100 built from estimated speed (55%), memory headroom (35%) and a capped quality bonus, with a 0.65 penalty when the fit is tight. Scores map to six grades, from \u003ci\u003eRuns great\u003c\/i\u003e down to \u003ci\u003eToo heavy\u003c\/i\u003e.\u003c\/p\u003e\n\u003cp\u003eThe catalogue holds 104 models from 22 labs, among them Alibaba, Meta, Google, DeepSeek, OpenAI, Mistral AI, Microsoft and Tencent, split between chat and coding, image, video and lightweight models, alongside 284 device pages covering Apple Silicon, NVIDIA, AMD, Intel, phone GPUs and single-board computers. Around this core sit a two-device comparator, an exportable tier list, a WebGPU playground that runs models directly in the browser, a glossary and a page detailing the whole methodology with its formulas.\u003c\/p\u003e\n\u003cp\u003eThe last step is execution: each model page carries a \u003cb\u003erunai\u003c\/b\u003e command, the companion command-line tool installed with a single curl line on macOS, Linux or WSL, which picks a quantization, downloads the GGUF weights and opens a local chat through llama.cpp. Ollama and LM Studio work with the same files. The site is built with Astro by \u003cb\u003emidudev\u003c\/b\u003e, and its source code is public on GitHub.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003ch3 style=\"font-size:14px;margin-bottom:12px;color:var(--gv3-brand-ink);text-transform:uppercase;letter-spacing:.08em\"\u003eWhat it does\u003c\/h3\u003e\n\u003cul class=\"brand-list\"\u003e\n\u003cli\u003eDetects the visitor's GPU, VRAM, memory bandwidth, RAM and CPU cores from the browser\u003c\/li\u003e\n\u003cli\u003eGrades 104 open-weight models from S to F for the machine it has detected\u003c\/li\u003e\n\u003cli\u003eEstimates tokens per second and the VRAM footprint of every quantization level\u003c\/li\u003e\n\u003cli\u003eOpens a dedicated page for any of 284 devices and for every model in the catalogue\u003c\/li\u003e\n\u003cli\u003eCompares two devices side by side, model by model, with a win count\u003c\/li\u003e\n\u003cli\u003eRuns a model in the browser through WebGPU, with nothing to install\u003c\/li\u003e\n\u003cli\u003eHands over the runai command that downloads and launches the model locally\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block soft\" id=\"bestnot\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eAudience\u003c\/span\u003e\u003ch2\u003eWhen to use CanIRun.ai \/ When not to\u003c\/h2\u003e\n\u003cp class=\"lead\"\u003eA quick filter to help you decide if CanIRun.ai is the right fit.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"bestnot\"\u003e\n\u003cdiv class=\"card for\"\u003e\n\u003ch3\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"16\" height=\"16\"\u003e\u003cpath d=\"M7 10v12\"\u003e\u003c\/path\u003e\u003cpath d=\"M15 5.88 14 10h5.83a2 2 0 0 1 1.92 2.56l-2.33 8A2 2 0 0 1 17.5 22H4a2 2 0 0 1-2-2v-8a2 2 0 0 1 2-2h2.76a2 2 0 0 0 1.79-1.11L12 2a3.13 3.13 0 0 1 3 3.88Z\"\u003e\u003c\/path\u003e\u003c\/svg\u003e When to use CanIRun.ai\u003c\/h3\u003e\n\u003cul class=\"brand-list\"\u003e\n\u003cli\u003eDevelopers who want to know whether their existing GPU or Mac can run an open-weight model before downloading several gigabytes of weights\u003c\/li\u003e\n\u003cli\u003eBuyers sizing a workstation or a laptop, using the 284 device pages and the two-device comparator to choose between VRAM tiers\u003c\/li\u003e\n\u003cli\u003eMachine learning and MLOps engineers who need a quick VRAM and tokens-per-second sanity check for each quantization level\u003c\/li\u003e\n\u003cli\u003eDesigners, video editors and motion artists hunting for image or video models that fit a consumer graphics card\u003c\/li\u003e\n\u003cli\u003eStudents and lecturers learning the vocabulary of local inference: parameters, quantization, GGUF, mixture of experts, memory bandwidth\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"card not\"\u003e\n\u003ch3\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"16\" height=\"16\"\u003e\u003cpath d=\"M17 14V2\"\u003e\u003c\/path\u003e\u003cpath d=\"M9 18.12 10 14H4.17a2 2 0 0 1-1.92-2.56l2.33-8A2 2 0 0 1 6.5 2H20a2 2 0 0 1 2 2v8a2 2 0 0 1-2 2h-2.76a2 2 0 0 0-1.79 1.11L12 22a3.13 3.13 0 0 1-3-3.88Z\"\u003e\u003c\/path\u003e\u003c\/svg\u003e When not to use CanIRun.ai\u003c\/h3\u003e\n\u003cul class=\"brand-list\"\u003e\n\u003cli\u003eTeams that need a hosted model or a cloud API, since the site covers only open-weight models you download and run yourself\u003c\/li\u003e\n\u003cli\u003eAnyone who needs measured benchmarks, because every figure here is an estimate derived from browser APIs and vendor spec sheets\u003c\/li\u003e\n\u003cli\u003eOrganisations with procurement or compliance requirements, as the domain publishes no terms of use, privacy policy or legal notice\u003c\/li\u003e\n\u003cli\u003eUsers looking for a mobile application or a documented public API, neither of which exists\u003c\/li\u003e\n\u003cli\u003eNon-English speakers, since the interface and all the documentation are English only\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block soft\" id=\"how\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eGet started\u003c\/span\u003e\u003ch2\u003eHow to use CanIRun.ai\u003c\/h2\u003e\n\u003cp class=\"lead\"\u003eA typical end-to-end flow, from setup to results.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003col class=\"steps\"\u003e\n\u003cli\u003eOpen canirun.ai in a recent browser and let the page read your GPU and memory\u003c\/li\u003e\n\u003cli\u003eIf detection misses your chip, pick it by hand from the 284 device pages\u003c\/li\u003e\n\u003cli\u003eRead the ranking of open models that fit, sorted by grade from S down to F\u003c\/li\u003e\n\u003cli\u003eFilter by task, whether chat and coding, image, video or lightweight, and by lab, licence or commercial use\u003c\/li\u003e\n\u003cli\u003eOpen a model page and read its quantization table, with the VRAM and quality of Q4_K_M, Q6_K, Q8_0 and the rest\u003c\/li\u003e\n\u003cli\u003eCheck the licence badge before committing, since several catalogued models are non-commercial\u003c\/li\u003e\n\u003cli\u003eCopy the runai command shown on the model page, for instance runai run qwen3.5-4b\u003c\/li\u003e\n\u003cli\u003eInstall runai with the single curl command on macOS, Linux or WSL, reviewing the script first as the site itself suggests\u003c\/li\u003e\n\u003cli\u003eOr skip the install entirely and try the model in the WebGPU playground, adjusting system prompt, temperature, max tokens and top-p\u003c\/li\u003e\n\u003cli\u003eUse the compare page or the exportable tier list to weigh two machines against each other\u003c\/li\u003e\n\u003c\/ol\u003e\u003c\/section\u003e\n\u003csection class=\"block\" id=\"proscons\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eQuick read\u003c\/span\u003e\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"grid-2\"\u003e\n\u003cdiv class=\"pc pros\"\u003e\n\u003ch3\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003ccircle cx=\"12\" cy=\"12\" r=\"10\"\u003e\u003c\/circle\u003e\u003cpath d=\"m9 12 2 2 4-4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Pros\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003eEntirely free, with no account, no sign-up wall and no paid tier\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003eThe scoring methodology is published in full, formulas and thresholds included\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003eDetection and scoring run client-side, and the site states that nothing is sent to a server\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003eThe source code of the site is public on GitHub\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003eBroad and current catalogue: 104 models, 284 devices, 22 labs\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003eThe path is complete, from diagnosis through to a working local model via runai, Ollama or LM Studio\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003eLicences are shown per model, with commercial and non-commercial use clearly separated\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"pc cons\"\u003e\n\u003ch3\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"16\" height=\"16\"\u003e\u003ccircle cx=\"12\" cy=\"12\" r=\"10\"\u003e\u003c\/circle\u003e\u003cline x1=\"4.93\" y1=\"4.93\" x2=\"19.07\" y2=\"19.07\"\u003e\u003c\/line\u003e\u003c\/svg\u003e Cons\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"18\" y1=\"6\" x2=\"6\" y2=\"18\"\u003e\u003c\/line\u003e\u003cline x1=\"6\" y1=\"6\" x2=\"18\" y2=\"18\"\u003e\u003c\/line\u003e\u003c\/svg\u003e \u003cspan\u003eEvery figure is an estimate rather than a measurement, and the site says so in capitals\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"18\" y1=\"6\" x2=\"6\" y2=\"18\"\u003e\u003c\/line\u003e\u003cline x1=\"6\" y1=\"6\" x2=\"18\" y2=\"18\"\u003e\u003c\/line\u003e\u003c\/svg\u003e \u003cspan\u003eBrowser detection is imprecise: vague GPU names, approximate RAM, bandwidth taken from spec sheets\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"18\" y1=\"6\" x2=\"6\" y2=\"18\"\u003e\u003c\/line\u003e\u003cline x1=\"6\" y1=\"6\" x2=\"18\" y2=\"18\"\u003e\u003c\/line\u003e\u003c\/svg\u003e \u003cspan\u003eThermal throttling, driver versions, background processes and operating system memory pressure cannot be measured and are not accounted for\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"18\" y1=\"6\" x2=\"6\" y2=\"18\"\u003e\u003c\/line\u003e\u003cline x1=\"6\" y1=\"6\" x2=\"18\" y2=\"18\"\u003e\u003c\/line\u003e\u003c\/svg\u003e \u003cspan\u003eThe built-in GPU database described on the methodology page covers far fewer chips than the 284 device pages suggest\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"18\" y1=\"6\" x2=\"6\" y2=\"18\"\u003e\u003c\/line\u003e\u003cline x1=\"6\" y1=\"6\" x2=\"18\" y2=\"18\"\u003e\u003c\/line\u003e\u003c\/svg\u003e \u003cspan\u003eNo terms of use, privacy policy or legal notice is published on the domain\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"18\" y1=\"6\" x2=\"6\" y2=\"18\"\u003e\u003c\/line\u003e\u003cline x1=\"6\" y1=\"6\" x2=\"18\" y2=\"18\"\u003e\u003c\/line\u003e\u003c\/svg\u003e \u003cspan\u003eNo support contact: the only email address on the site is reserved for advertising campaigns\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"18\" y1=\"6\" x2=\"6\" y2=\"18\"\u003e\u003c\/line\u003e\u003cline x1=\"6\" y1=\"6\" x2=\"18\" y2=\"18\"\u003e\u003c\/line\u003e\u003c\/svg\u003e \u003cspan\u003eEnglish only, with no mobile application and no documented public API\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block soft\" id=\"pricing\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003ePricing\u003c\/span\u003e\u003ch2\u003ePricing \u0026amp; Plans\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003cp class=\"rich justify\" style=\"color:var(--gv3-ink-2);margin:0 0 18px;max-width:80ch\"\u003eThe tool is free of charge in its entirety: there is no paid plan, no pricing page and no payment of any kind is required to use the site, the WebGPU playground or the runai command-line tool, so no entry price applies. The only paid item on the domain is a display advertising slot, which concerns advertisers rather than people using the tool.\u003c\/p\u003e\n\u003cdiv class=\"plan-grid\"\u003e\u003cdiv class=\"plan\"\u003e\n\u003cdiv class=\"pname\"\u003eFree\u003c\/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003efull access to the model rankings\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003ethe device pages\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003ethe two-device comparator\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003ethe tier list\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003ethe WebGPU playground and the runai command-line tool\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolyline points=\"20 6 9 17 4 12\"\u003e\u003c\/polyline\u003e\u003c\/svg\u003e \u003cspan\u003ewith no account and no time limit\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv class=\"pricing-extras\"\u003e\u003cdiv class=\"affiliate-note\"\u003ePrices and plans listed above may evolve. Always check the official pricing page before subscribing.\u003c\/div\u003e\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block\" id=\"trust\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eTrust \u0026amp; Privacy\u003c\/span\u003e\u003ch2\u003eData, GDPR \u0026amp; hosting\u003c\/h2\u003e\n\u003cp class=\"lead\"\u003eA consolidated view of how CanIRun.ai handles your data.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"trust-grid\"\u003e\n\u003cdiv class=\"col\"\u003e\n\u003cdiv class=\"trust-card\"\u003e\n\u003ch3\u003eGDPR overview\u003c\/h3\u003e\n\u003cp class=\"justify\" style=\"color:var(--gv3-ink-2);margin:0\"\u003eThere is no mention of the GDPR anywhere on canirun.ai. The domain publishes no privacy policy, no cookie policy, no consent banner, no data protection officer and no Article 27 representative; targeted probes on \/privacy, \/terms and \/legal all return 404, and the complete sitemap contains no legal page. The only privacy statement is technical: the methodology page asserts that detection and scoring happen entirely in the browser and that no data is sent to any server, which would leave little to regulate. The robots.txt file cites Article 4 of EU Directive 2019\/790, but that concerns copyright reservations, not data protection. For a project whose author appears to be established in the European Union, this complete absence of published legal documentation is worth noting.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"trust-card\"\u003e\n\u003ch3\u003eWho owns the data?\u003c\/h3\u003e\n\u003cp class=\"justify\" style=\"color:var(--gv3-ink-2);margin:0 0 12px\"\u003eNo terms of use or privacy policy is published on the domain, so nothing contractually assigns ownership of anything. The only statement on the subject is the Privacy and transparency section of the methodology page, which says that hardware detection and scoring run entirely in the browser and that the GPU name, RAM and benchmark results never leave the device. In the playground, downloaded models are cached in the visitor's browser and conversations are stored locally, persisting across sessions, which leaves them under the visitor's control. In practice you keep everything, but by the absence of any published agreement rather than by an explicit commitment.\u003c\/p\u003e\n\u003ch3\u003eReuse rights\u003c\/h3\u003e\n\u003cp class=\"justify\" style=\"color:var(--gv3-ink-2);margin:0\"\u003eThe site publishes no terms governing reuse, so no permission mechanism is documented in either direction. What it does state is that the visitor's hardware data serves only the local calculation of scores and rankings and is never sent to a server. The third-party figures displayed, namely download and like counts from the HuggingFace API and pull counts and tags from the Ollama library, are fetched by the site at build time rather than from the visitor. The robots.txt file signals search=yes, ai-train=no and use=reference for the site's own content, and blocks GPTBot, ClaudeBot, CCBot, Google-Extended and several other crawlers.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"trust-card\"\u003e\n\u003ch3\u003eData retention \u0026amp; training\u003c\/h3\u003e\n\u003cdiv class=\"kv\"\u003e\n\u003cdiv class=\"k\"\u003eRetention summary\u003c\/div\u003e\n\u003cdiv class=\"v justify\"\u003eNo retention period is published, since the domain carries neither a privacy policy nor terms of use. What retention exists is local and visible: the playground caches downloaded models in the browser for offline reuse and keeps conversations across sessions, and the interface offers a Delete all cached data control for the models and a Clear all control for the chat history, so the visitor decides how long anything is kept. No server-side retention is described, which is consistent with the site's claim that no data is sent to a server. Nothing is said about anonymisation or deletion requests, because no data collection is claimed in the first place.\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"col\"\u003e\u003cdiv class=\"trust-card\"\u003e\n\u003ch3\u003eHosting summary\u003c\/h3\u003e\n\u003cp class=\"justify\" style=\"color:var(--gv3-ink-2);margin:0 0 14px\"\u003eNo hosting jurisdiction is declared for user data, for the simple reason that the site declares no user data collection at all. The methodology page states that hardware detection and scoring run entirely in the browser and that nothing is sent to a server. In the playground, downloaded models are cached in the visitor's browser and conversations are stored locally, so whatever persists stays on the visitor's own machine. As for the site itself, the domain resolves to 172.67.201.155, an anycast address belonging to AS13335, Cloudflare Inc., with a node geolocated in San Francisco, United States; that describes how the pages are delivered, not where any personal data lives. The model statistics on display are fetched by the site from the HuggingFace API and the Ollama library at build time, again without involving the visitor. Because the domain carries neither a privacy policy nor terms of use, none of this is contractually documented: it rests on a technical statement on the methodology page and on what the pages themselves do.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block watch\" id=\"watch\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eWatch-outs\u003c\/span\u003e\u003ch2\u003eThings to keep in mind\u003c\/h2\u003e\n\u003cp class=\"lead\"\u003eRisks and trade-offs to weigh before adopting CanIRun.ai.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cul class=\"brand-list\" style=\"grid-template-columns:repeat(2,1fr);display:grid;gap:12px 24px\"\u003e\n\u003cli\u003eThe grades are estimates, so buying a graphics card on the strength of a browser-detected score can turn into an expensive mistake\u003c\/li\u003e\n\u003cli\u003eInstalling runai means piping a remote script into bash; the site itself suggests reading the installer at canirun.ai\/runai\/install.sh first\u003c\/li\u003e\n\u003cli\u003eSeveral catalogued models carry non-commercial or conditional community licences, and using one in a paid project without checking is a genuine legal risk\u003c\/li\u003e\n\u003cli\u003eRunning a model locally removes the provider's safety layer: the output is unfiltered and entirely your responsibility\u003c\/li\u003e\n\u003cli\u003eWith no terms, no privacy policy and no support contact, there is nothing to rely on if something goes wrong\u003c\/li\u003e\n\u003cli\u003eMixture-of-experts models load every expert into memory, so the active parameter count understates what a machine actually needs\u003c\/li\u003e\n\u003cli\u003eDownloading multi-gigabyte weights on a metered or shared connection carries a cost the site does not show\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/section\u003e\n\u003csection class=\"block\" id=\"setup\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eSetup\u003c\/span\u003e\u003ch2\u003eSetup \u0026amp; Integrations\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"setup-grid\"\u003e\n\u003cdiv class=\"setup-card\"\u003e\n\u003ch3 style=\"font-size:15px;margin-bottom:10px\"\u003eTechnical difficulty\u003c\/h3\u003e\n\u003cp class=\"justify\" style=\"color:var(--gv3-ink-2);margin:0 0 14px\"\u003eUsing the site itself requires nothing: open it in a recent browser and the ranking appears, with no account and no install. The playground is barely harder, needing a WebGPU-capable browser and the patience to download the chosen model once. The runai command-line tool is the only step that assumes some comfort with a terminal: a single curl command on macOS, Linux or WSL, then runai run followed by a model identifier. The real difficulty lies elsewhere, in owning suitable hardware and downloading multi-gigabyte weights, not in the tool.\u003c\/p\u003e\n\u003ch3 style=\"font-size:15px;margin:14px 0 10px\"\u003eDeployment\u003c\/h3\u003e\n\u003cdiv class=\"flag-row\"\u003e\u003cspan class=\"flag\"\u003eWeb app\u003c\/span\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"setup-card\"\u003e\n\u003ch3 style=\"font-size:15px;margin-bottom:10px\"\u003eIntegrations\u003c\/h3\u003e\n\u003cdiv class=\"integrations\"\u003e\n\u003cspan class=\"integration\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpath d=\"M6.3 20.3a2.4 2.4 0 0 0 3.4 0L12 18l-6-6-2.3 2.3a2.4 2.4 0 0 0 0 3.4Z\"\u003e\u003c\/path\u003e\u003cpath d=\"m2 22 3-3\"\u003e\u003c\/path\u003e\u003cpath d=\"M7.5 13.5 10 11\"\u003e\u003c\/path\u003e\u003cpath d=\"M10.5 16.5 13 14\"\u003e\u003c\/path\u003e\u003cpath d=\"m18 3-4 4h6l-4 4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Ollama\u003c\/span\u003e\u003cspan class=\"integration\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpath d=\"M6.3 20.3a2.4 2.4 0 0 0 3.4 0L12 18l-6-6-2.3 2.3a2.4 2.4 0 0 0 0 3.4Z\"\u003e\u003c\/path\u003e\u003cpath d=\"m2 22 3-3\"\u003e\u003c\/path\u003e\u003cpath d=\"M7.5 13.5 10 11\"\u003e\u003c\/path\u003e\u003cpath d=\"M10.5 16.5 13 14\"\u003e\u003c\/path\u003e\u003cpath d=\"m18 3-4 4h6l-4 4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e LM Studio\u003c\/span\u003e\u003cspan class=\"integration\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpath d=\"M6.3 20.3a2.4 2.4 0 0 0 3.4 0L12 18l-6-6-2.3 2.3a2.4 2.4 0 0 0 0 3.4Z\"\u003e\u003c\/path\u003e\u003cpath d=\"m2 22 3-3\"\u003e\u003c\/path\u003e\u003cpath d=\"M7.5 13.5 10 11\"\u003e\u003c\/path\u003e\u003cpath d=\"M10.5 16.5 13 14\"\u003e\u003c\/path\u003e\u003cpath d=\"m18 3-4 4h6l-4 4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Llama.cpp\u003c\/span\u003e\u003cspan class=\"integration\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpath d=\"M6.3 20.3a2.4 2.4 0 0 0 3.4 0L12 18l-6-6-2.3 2.3a2.4 2.4 0 0 0 0 3.4Z\"\u003e\u003c\/path\u003e\u003cpath d=\"m2 22 3-3\"\u003e\u003c\/path\u003e\u003cpath d=\"M7.5 13.5 10 11\"\u003e\u003c\/path\u003e\u003cpath d=\"M10.5 16.5 13 14\"\u003e\u003c\/path\u003e\u003cpath d=\"m18 3-4 4h6l-4 4\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Hugging Face\u003c\/span\u003e\n\u003c\/div\u003e\n\u003ch3 style=\"font-size:15px;margin:14px 0 10px\"\u003eSupported languages\u003c\/h3\u003e\n\u003cdiv class=\"flag-row\"\u003e\u003cspan class=\"flag\"\u003eEnglish\u003c\/span\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block soft\" id=\"company\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eCompany\u003c\/span\u003e\u003ch2\u003eBehind CanIRun.ai\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"company-card\"\u003e\n\u003cdiv class=\"kv\"\u003e\n\u003cdiv class=\"k\"\u003eCompany name\u003c\/div\u003e\n\u003cdiv class=\"v\"\u003e\u003cb\u003emidudev\u003c\/b\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"kv\"\u003e\n\u003cdiv class=\"k\"\u003eFounded\u003c\/div\u003e\n\u003cdiv class=\"v\"\u003e09\/03\/2026\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"kv\"\u003e\n\u003cdiv class=\"k\"\u003eCountry of origin\u003c\/div\u003e\n\u003cdiv class=\"v\"\u003e🇪🇸 Spain\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"kv\"\u003e\n\u003cdiv class=\"k\"\u003eUBO\u003c\/div\u003e\n\u003cdiv class=\"v\"\u003eMiguel Ángel Durán\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"kv\"\u003e\n\u003cdiv class=\"k\"\u003eUBO country\u003c\/div\u003e\n\u003cdiv class=\"v\"\u003e🇪🇸 Spain\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"kv\"\u003e\n\u003cdiv class=\"k\"\u003eDomain registrar country\u003c\/div\u003e\n\u003cdiv class=\"v\"\u003e🇺🇸 United States\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block\" id=\"resources\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eOfficial links\u003c\/span\u003e\u003ch2\u003eResources\u003c\/h2\u003e\n\u003cp class=\"lead\"\u003eAll the official URLs gathered for verification and reference.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"res-grid\"\u003e\n\u003ca class=\"res\" href=\"https:\/\/canirun.ai\/\" target=\"_blank\" rel=\"noopener\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003ccircle cx=\"12\" cy=\"12\" r=\"10\"\u003e\u003c\/circle\u003e\u003cpath d=\"M12 2a14.5 14.5 0 0 0 0 20 14.5 14.5 0 0 0 0-20\"\u003e\u003c\/path\u003e\u003cpath d=\"M2 12h20\"\u003e\u003c\/path\u003e\u003c\/svg\u003e\u003cdiv class=\"res-text\"\u003e\n\u003cdiv\u003eHomepage\u003c\/div\u003e\n\u003cdiv class=\"url\"\u003ecanirun.ai\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/a\u003e\u003ca class=\"res\" href=\"https:\/\/canirun.ai\/playground\" target=\"_blank\" rel=\"noopener\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolygon points=\"6 3 20 12 6 21 6 3\"\u003e\u003c\/polygon\u003e\u003c\/svg\u003e\u003cdiv class=\"res-text\"\u003e\n\u003cdiv\u003eDemo\u003c\/div\u003e\n\u003cdiv class=\"url\"\u003ecanirun.ai\/playground\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/a\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block\" id=\"faq\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eFAQ\u003c\/span\u003e\u003ch2\u003eFrequently asked questions\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"faq\"\u003e\n\u003cdetails\u003e\u003csummary\u003eHow much VRAM do I need to run a local AI model? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eAs a rule of thumb given on the site, a 7B to 9B chat model fits in 8 GB of VRAM at Q4 quantization, 12 to 16 GB covers most 12B to 27B models, and 24 GB or more opens up 30B dense models, mid-size mixture-of-experts models and local image or video generation. On Apple Silicon the limit is unified memory rather than a separate frame buffer.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eDoes CanIRun.ai cost anything? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eNo. The rankings, device pages, comparator, tier list, playground and the runai tool are all free, and no account is required. The only paid item on the domain is a display advertising slot, which has nothing to do with using the tool.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eIs my hardware data sent anywhere? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eAccording to the methodology page, no: detection and scoring run entirely in the browser, and the site states that the GPU name, RAM and benchmark results never leave the device. Bear in mind that this claim is not backed by any published privacy policy, since the domain has none.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eCan I try a model without installing anything? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eYes. The playground runs selected models directly in the browser through WebGPU. Models are cached locally after the first download so they work offline afterwards, and conversations are stored locally and persist across sessions.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eHow do I actually run a model the site recommends? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eEach model page carries a runai command to copy, for example runai run qwen3.5-4b. runai installs with a single curl command on macOS, Linux or WSL, picks a quantization for your machine, downloads the GGUF file and opens a local chat through llama.cpp. Ollama and LM Studio work with the same weights.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eWhat is quantization and why does it matter here? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eQuantization stores model weights on fewer bits, making the file smaller and less memory-hungry. Q4_K_M is the usual balance for local chat, while Q6_K and Q8_0 stay closer to the original model but need more VRAM. Every model page lists the VRAM cost of each available format.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eCan I run local models on a Mac without a dedicated GPU? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eYes. Apple Silicon Macs share memory between CPU and GPU, so an M4, M4 Pro or M4 Max can run models that would otherwise require a discrete card. Bandwidth still matters: a Max chip generates tokens faster than a base chip with the same amount of memory.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eHow reliable are the grades and the speed estimates? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eThey are estimates, and the site says so explicitly. Browser APIs return vague GPU names and approximate RAM, bandwidth comes from vendor spec sheets rather than measurement, and thermal throttling, drivers and background load are not taken into account. Treat the grades as guidance, not as a guarantee.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eDoes the site publish terms of use or a privacy policy? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eNo. Probes on \/privacy, \/terms, \/about and \/legal all return 404, and the complete sitemap contains no legal page. The only statement about data is the Privacy and transparency section of the methodology page.\u003c\/div\u003e\u003c\/details\u003e\u003cdetails\u003e\u003csummary\u003eWho is behind CanIRun.ai? \u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cline x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\u003e\u003c\/line\u003e\u003cline x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\u003e\u003c\/line\u003e\u003c\/svg\u003e\u003c\/summary\u003e\u003cdiv class=\"ans justify\"\u003eThe footer credits midudev, an independent developer, and links to the public source code on GitHub. No company is named on the site, the domain was registered on 9 March 2026 and the first Wayback Machine capture dates from the same day.\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003csection class=\"block conclusion\" id=\"conclusion\"\u003e\u003cdiv class=\"head\"\u003e\n\u003cspan class=\"eyebrow\"\u003eConclusion\u003c\/span\u003e\u003ch2\u003eShould you pick CanIRun.ai?\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"rich justify\" style=\"max-width:80ch\"\u003e\n\u003cp\u003eCanIRun.ai does one thing, and does it with unusual transparency. It answers the question every developer curious about local AI eventually asks, namely whether a given model will run on a given machine, and it answers in seconds without an account, a paywall or a server round trip. The methodology page is the strongest argument in its favour: the detection APIs, the VRAM formula, the score weighting and the grade thresholds are all published, with the code that produces them, and the source of the site itself is on GitHub. Few directories expose their own arithmetic that plainly.\u003c\/p\u003e\n\u003cp\u003eThe caveats are just as clear, and the site states them first. Every number is an estimate built on browser APIs and vendor spec sheets rather than on measurement; thermal throttling, driver versions and background load simply cannot be seen from a web page. The hardware database described in the methodology is narrower than the 284 device pages suggest. And the project is very young: the domain was registered on 9 March 2026, the first archive capture dates from the same day, and it is maintained by a single developer.\u003c\/p\u003e\n\u003cp\u003eThe more serious gap for professional use is legal. There are no terms of use, no privacy policy, no legal notice and no support address; the only email on the site is reserved for advertising. That matters less for a tool that claims to send nothing to a server, but it leaves an organisation with nothing to point at.\u003c\/p\u003e\n\u003cp\u003eTaken for what it is, a free and well-documented diagnostic and catalogue for open-weight models with a working path through to runai, Ollama or LM Studio, CanIRun.ai is genuinely useful. Treat its grades as guidance for a hardware decision, and verify anything that carries a budget.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"cta-row\"\u003e\n\u003ca class=\"btn btn-primary\" href=\"https:\/\/canirun.ai\/\" target=\"_blank\" rel=\"noopener\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"16\" height=\"16\"\u003e\u003cpath d=\"M15 3h6v6\"\u003e\u003c\/path\u003e\u003cpath d=\"M10 14 21 3\"\u003e\u003c\/path\u003e\u003cpath d=\"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6\"\u003e\u003c\/path\u003e\u003c\/svg\u003e Visit CanIRun.ai\u003c\/a\u003e\u003ca class=\"btn btn-secondary\" href=\"https:\/\/canirun.ai\/playground\" target=\"_blank\" rel=\"noopener\"\u003e\u003csvg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" width=\"14\" height=\"14\"\u003e\u003cpolygon points=\"6 3 20 12 6 21 6 3\"\u003e\u003c\/polygon\u003e\u003c\/svg\u003e Watch the demo\u003c\/a\u003e\n\u003c\/div\u003e\u003c\/section\u003e\n\u003c\/div\u003e","brand":"midudev","offers":[{"title":"Default Title","offer_id":58428653797707,"sku":null,"price":0.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1038\/0905\/7099\/files\/official-canirun-ai-by-guidaio_eff1e2aa-e925-46bf-9ffc-23c8dc441c10.png?v=1788963262","url":"https:\/\/guidaio.com\/products\/canirun-ai","provider":"Guidaio","version":"1.0","type":"link"}