[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-open-source-tool-squeezes-a-35b-ai-model-onto-a-24gb-pc":10,"sections":35},{"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":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},6506,"open-source-tool-squeezes-a-35b-ai-model-onto-a-24gb-pc","Open-Source Tool Squeezes a 35B AI Model Onto a 24GB PC","Edge0 predicts each layer's expert routing one step ahead, letting a 35B mixture-of-experts model run from SSD in just 3GB of active memory.","Running large AI models locally usually means buying a lot of expensive memory. A new open-source project called Edge0 gets around that by predicting, one step ahead, which parts of the model it will need next - so it can pull them off a hard drive fast enough to keep up.\n\nThe model in question is a 35-billion-parameter mixture-of-experts (MoE) system, a design built from many smaller sub-networks that only activates a handful of them per word it generates. That sparsity should make MoEs cheap to run, but it doesn't shrink the file: even compressed to 4-bit precision, the weights still take up 19.5GB, more than fits comfortably in memory on typical consumer hardware. The catch is that you don't know which sub-networks a layer needs until the previous layer has finished computing, so simply streaming weights from disk normally stalls the whole process. Edge0's fix is a small prediction module that guesses the next layer's routing a token in advance and then treats that guess as the actual decision, while a bolt-on adapter claws back the accuracy lost to compression and prediction. The result: 20 tokens per second on a single 24GB machine, using just 3GB of active memory, and scoring within a few points of the uncompressed model across five public benchmarks.\n\nThat's a meaningfully bigger model running on meaningfully smaller hardware than most local-AI setups manage, and the code, checkpoints, and adapters are all released openly. For anyone trying to run large open models without renting a GPU cluster or paying per-token API fees, this closes a real gap between what labs ship and what a laptop can actually hold in memory.\n\nWorth noting: this is a fresh arXiv preprint, not a peer-reviewed or independently benchmarked release, and the whole approach hinges on the routing predictor guessing correctly - the paper doesn't say how often it's wrong, or what happens to speed and accuracy when it is.","[\"mixture-of-experts\",\"ai-inference\",\"open-source\",\"edge-ai\"]","2026-09-17T04:00:00.000Z","2026-09-17T21:22:25.945Z","2026-09-17T21:22:37.957Z","published",null,[],"ai",[26,27,28,29],"mixture-of-experts","ai-inference","open-source","edge-ai",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.18063",0,{"sections":36},[37,41,45,50,55,59,63,68,73,77,82,87,92,97],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",3852,"2026-09-17T08:27:09.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":18},"Security","security",648,{"name":46,"slug":47,"count":48,"latest_published_at":49},"Policy","policy",338,"2026-09-11T04:00:00.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":18},"Hardware","hardware",154,{"name":60,"slug":61,"count":62,"latest_published_at":18},"Science","science",114,{"name":64,"slug":65,"count":66,"latest_published_at":67},"Consumer Tech","consumer-tech",99,"2026-09-09T17:27:33.000Z",{"name":69,"slug":70,"count":71,"latest_published_at":72},"Software","software",75,"2026-09-10T20:41:21.000Z",{"name":74,"slug":75,"count":76,"latest_published_at":18},"Dev Tools","dev-tools",73,{"name":78,"slug":79,"count":80,"latest_published_at":81},"Startups","startups",55,"2026-09-09T23:14:29.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"Gaming","gaming",43,"2026-09-10T12:18:06.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"General","general",41,"2026-09-08T01:57:23.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]