{"id":"https://openalex.org/W7162565777","doi":"https://doi.org/10.48550/arxiv.2605.27358","title":"MobileMoE: Scaling On-Device Mixture of Experts","display_name":"MobileMoE: Scaling On-Device Mixture of Experts","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162565777","doi":"https://doi.org/10.48550/arxiv.2605.27358"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27358","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.27358","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137119376","display_name":"Yanbei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yanbei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110397904","display_name":"Hanxian Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Hanxian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137133899","display_name":"Ernie Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Ernie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128885764","display_name":"Jacob Szwejbka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Szwejbka, Jacob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033525394","display_name":"Digant Desai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Desai, Digant","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137161442","display_name":"Zechun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zechun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137184365","display_name":"Vikas Chandra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chandra, Vikas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137186028","display_name":"Raghuraman Krishnamoorthi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishnamoorthi, Raghuraman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.18410000205039978,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.18410000205039978,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11400000005960464,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.04910000041127205,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6642000079154968},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.47440001368522644},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.43529999256134033},{"id":"https://openalex.org/keywords/commodity","display_name":"Commodity","score":0.42890000343322754},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.42829999327659607},{"id":"https://openalex.org/keywords/frontier","display_name":"Frontier","score":0.4108999967575073},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.39419999718666077},{"id":"https://openalex.org/keywords/recipe","display_name":"Recipe","score":0.3824000060558319},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.3813000023365021}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6642000079154968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6603999733924866},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.460099995136261},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.43529999256134033},{"id":"https://openalex.org/C2779439359","wikidata":"https://www.wikidata.org/wiki/Q317088","display_name":"Commodity","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C2778571376","wikidata":"https://www.wikidata.org/wiki/Q1355821","display_name":"Frontier","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C2778671685","wikidata":"https://www.wikidata.org/wiki/Q219239","display_name":"Recipe","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37770000100135803},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C186379835","wikidata":"https://www.wikidata.org/wiki/Q253276","display_name":"Mile","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C176649486","wikidata":"https://www.wikidata.org/wiki/Q2308807","display_name":"Memory management","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.2946999967098236},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2797999978065491},{"id":"https://openalex.org/C68649174","wikidata":"https://www.wikidata.org/wiki/Q1379116","display_name":"Base station","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C2992317946","wikidata":"https://www.wikidata.org/wiki/Q712144","display_name":"De facto","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C144543869","wikidata":"https://www.wikidata.org/wiki/Q2738570","display_name":"Mobile computing","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27358","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.27358","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27358","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mixture-of-Experts":[0],"(MoE)":[1],"has":[2],"become":[3],"the":[4,97,139,151,159,186],"de":[5],"facto":[6],"architecture":[7,68],"for":[8,18,53],"hundred-billion-parameter":[9],"language":[10,36],"models,":[11],"yet":[12],"its":[13],"advantages":[14],"at":[15],"sub-billion":[16,39],"scales":[17],"on-device":[19,34,54,60,77,127,169],"deployment":[20],"remain":[21],"largely":[22],"unexplored.":[23],"To":[24,149],"close":[25],"this":[26],"gap,":[27],"we":[28,100,157],"present":[29],"MobileMoE,":[30],"a":[31,49,104],"family":[32],"of":[33],"MoE":[35,61,67,141,162],"models":[37],"with":[38,83,103,130,143,167],"active":[40,43],"parameters":[41],"(0.3-0.9B":[42],"and":[44,72,85,93,112,135,181],"1.3-5.3B":[45],"total)":[46],"that":[47,64,89],"establish":[48],"new":[50],"Pareto":[51],"frontier":[52],"LLMs.":[55],"We":[56],"first":[57,160],"formulate":[58],"an":[59,76],"scaling":[62],"law":[63],"jointly":[65],"optimizes":[66],"under":[69],"mobile":[70,155],"memory":[71,92],"compute":[73],"constraints,":[74],"identifying":[75],"sweet":[78],"spot":[79],"-":[80,88],"moderate":[81],"sparsity":[82],"fine-grained":[84],"shared":[86],"experts":[87],"is":[90],"simultaneously":[91],"compute-optimal.":[94],"Building":[95],"on":[96,116,164],"derived":[98],"architectures,":[99],"train":[101],"MobileMoE":[102,122],"four-stage":[105],"recipe":[106],"covering":[107],"pre-training,":[108],"mid-training,":[109],"instruction":[110],"fine-tuning,":[111],"quantization-aware":[113],"training,":[114],"all":[115],"open-source":[117],"datasets.":[118],"Across":[119],"14":[120],"benchmarks,":[121],"matches":[123,136],"or":[124,137],"exceeds":[125],"leading":[126],"dense":[128,187],"LLMs":[129],"2-4$\\times$":[131],"fewer":[132,147],"inference":[133,163],"FLOPs,":[134],"surpasses":[138],"state-of-the-art":[140],"OLMoE-1B-7B":[142],"up":[144],"to":[145,154],"60%":[146],"parameters.":[148],"bridge":[150],"last":[152],"mile":[153],"deployment,":[156],"provide":[158],"efficient":[161],"commodity":[165],"smartphones":[166],"comprehensive":[168],"profiling.":[170],"At":[171],"comparable":[172],"INT4":[173],"weight":[174],"memory,":[175],"MobileMoE-S":[176],"delivers":[177],"$1.8$-$3.8\\times$":[178],"faster":[179,183],"prefill":[180],"$2.2$-$3.4\\times$":[182],"decode":[184],"than":[185],"baseline":[188],"MobileLLM-Pro.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}
