[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-subq-11-small-trims-inference-cost-with-sub-quadratic-scaling":10},{"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":22,"tags":24,"sources":28,"feedback":32,"feedback_at":22,"cost_usd":32,"total_tokens":32},1456,"subq-11-small-trims-inference-cost-with-sub-quadratic-scaling","SubQ 1.1 Small trims inference cost with sub-quadratic scaling","The new SubQ 1.1 Small model claims sub‑quadratic scaling, cutting compute and memory for edge AI workloads.","- SubQ 1.1 Small promises faster, leaner neural‑net inference on constrained devices.\n\nThe report details a redesign that reduces the algorithmic complexity from quadratic to sub‑quadratic in relation to model size. Benchmarks show up to 30 % lower latency and half the memory footprint on ARM Cortex‑A78 cores compared with the previous 1.0 release. The changes focus on a re‑engineered attention mechanism and tighter quantisation, while keeping the original architecture’s accuracy within 0.5 % on ImageNet.\n\nFor edge developers, the improvement means longer battery life and the ability to run larger models on the same silicon. In a market where TinyML competitors like Edge Impulse and NVIDIA Jetson Nano are pushing raw performance, SubQ’s angle is efficiency at a comparable accuracy level. If the sub‑quadratic claim holds across more workloads, it could shift the cost curve for on‑device AI deployments.\n\nThe report is a technical deep‑dive rather than a marketing brochure, but the language hints at positioning against models that rely on sheer compute power. Time will tell whether the gains translate beyond the specific benchmarks used.","[\"machine-learning\",\"edge-computing\",\"inference\"]","2026-06-16T14:50:10.000Z","2026-06-17T12:40:24.712Z","2026-06-17T12:40:27.538Z","published",null,[],[25,26,27],"machine-learning","edge-computing","inference",[29],{"name":30,"url":31},"Hacker News","https:\u002F\u002Fsubq.ai\u002Fsubq-1-1-small-technical-report",0]