[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-primacpp-runs-70b-models-across-home-hardware":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},4103,"primacpp-runs-70b-models-across-home-hardware","prima.cpp Runs 70B Models Across Home Hardware","A new open-source inference system stitches together mismatched consumer devices to run large language models without cloud dependency.","Running a 70-billion-parameter language model on the computers already in your house is now a documented, benchmarked possibility.\n\nResearchers released prima.cpp, a distributed inference system designed to spread model execution across a home cluster of mixed consumer hardware — laptops, desktops, phones, whatever is available — connected over Wi-Fi. The system introduces a scheduling layer called Halda that assigns workloads to each device based on its actual CPU, GPU, RAM, and VRAM constraints. A second technique, pipelined-ring parallelism, keeps disk reads from stalling computation by overlapping storage I\u002FO with processing. On four ordinary consumer devices, the researchers ran a 70B model at 674 milliseconds per token and a 32B model at 26 tokens per second using speculative decoding — with memory pressure staying below 6%.\n\nThe gap between those numbers and existing tools is the real headline. Against llama.cpp, exo, and dllama, prima.cpp posts 5 to 17 times lower time-per-output-token. That range matters because it suggests the improvement holds across different hardware configurations, not just a cherry-picked benchmark setup. Private, offline inference at this model scale has been technically possible in theory but practically unusable for most people; closing that gap changes who can self-host a capable model.\n\nThe code is open-source, which invites scrutiny — real-world results on random consumer hardware will vary considerably from a controlled four-device test. Still, this is meaningfully further along than the usual academic demo.","[\"ai\",\"open-source\",\"inference\",\"llms\"]","2026-07-07T04:00:00.000Z","2026-07-07T17:20:05.133Z","2026-07-07T17:20:07.721Z","published",null,[],"ai",[24,26,27,28],"open-source","inference","llms",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.08791",0,{"sections":35},[36,40,45,50,55,60,65,70,75,79,84,88,93,98],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":18},"Dev Tools","dev-tools",59,{"name":80,"slug":81,"count":82,"latest_published_at":83},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":85,"slug":86,"count":82,"latest_published_at":87},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":89,"slug":90,"count":91,"latest_published_at":92},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":94,"slug":95,"count":96,"latest_published_at":97},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":99,"slug":100,"count":101,"latest_published_at":102},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]