Dubawa
Ƙarfin sarrafawa wanda ke yanke shawarar wane samfurin kwafi, GPU, ko baya ya kamata ya kula da kowane buƙatun LLM mai shigowa, da yadda za a yada zirga-zirga don kada uwar garken guda ɗaya ta mamaye. An yi shi da kyau, yana rage jinkiri da farashi; yayi rashin kyau, yana haifar da ƙarewar lokaci da GPUs marasa aiki.
LLM Inference Routing da Load Daidaitawa wani shingen gini ne na fasaha wanda ke shafar ingancin samfuri, farashin kayayyakin more rayuwa, latency, da aminci a sikeli.
Zurfafa nutsewa
Bauta wa LLM a sikeli yana nufin gudanar da kwafi da yawa a cikin GPUs da yawa, kuma zirga-zirgar zirga-zirgar ababen hawa ta fashe kuma ba ta da daidaituwa - buƙatun sun bambanta sosai cikin tsayi da wahala. Mai na'ura mai ba da hanya tsakanin hanyoyin sadarwa yana zaune a gaba kuma ya zaɓi wurin da za a yi amfani da shi ta amfani da sigina mafi arziƙi fiye da na zagaye-zagaye. Masu ba da hanya tsakanin hanyoyin sadarwa na zamani na LLM suna la'akari da zurfin layi, zama na KV-cache, da kuma ko kwafi ya riga ya riƙe prefix ɗin da ya dace (prefix-cache affinity), don haka neman biyo baya ya sauka inda cache ɗinsa ke rayuwa. Wasu masu amfani da hanyar sadarwa kuma suna zaɓar nau'in samfurin da za su yi amfani da su — suna aika tambayoyi masu sauƙi zuwa ƙaramin ƙira mai arha da masu wuya zuwa babba (model routing). Daidaita kaya sannan yana daidaita matsa lamba a cikin kwafi don guje wa wuraren zafi, mutunta iyakokin ƙima, da kiyaye ƙarancin wutsiya yayin haɓaka ƙimar gabaɗaya da amfani da GPU.
Fahimtar Fasaha
Masu daidaita ma'auni marasa nauyi suna ɗauka cewa buƙatun suna canzawa kuma masu arha don ƙaura-ƙarya ga LLMs. Kowace alamar fitarwa tana biyan kuɗin wucewar gaba, kuma ma'ajin KV na kwafi ya sa ya zama 'mai la'akari' don zama. Saboda haka masu amfani da hanyoyin sadarwa masu wayo suna haɓakawa don buguwar cache: hashing ko haɗa-lokaci don haka haɓakar prefix ɗin tattaunawa ta sake yin amfani da maɓallan da aka adana maimakon ƙididdige su. Hakanan suna karanta telemetry na baya-bayan nan (alamu masu jiran gado, cikar tsari) maimakon ƙidayar buƙata kawai, tunda dogon buƙatu na iya fin gajerun gajeru da yawa.
Ƙwararren Ƙwararren Ƙwararren LLM da Daidaita Load
Ƙarfin sarrafawa wanda ke yanke shawarar wane samfurin kwafi, GPU, ko baya ya kamata ya kula da kowane buƙatun LLM mai shigowa, da yadda za a yada zirga-zirga don kada uwar garken guda ɗaya ta mamaye. An yi shi da kyau, yana rage jinkiri da farashi; yayi rashin kyau, yana haifar da ƙarewar lokaci da GPUs marasa aiki. LLM Inference Routing da Load Daidaitawa wani shingen gini ne na fasaha wanda ke shafar ingancin samfuri, farashin kayayyakin more rayuwa, latency, da aminci a sikeli. Don gina fahimta mai zurfi, bi da Hanyar Hanya ta LLM da Daidaita Load a matsayin samfurin aiki, ba fasali ɗaya ba: ayyana sakamakon da ake so, fayyace zato, da raba abin da tsarin zai iya yi da dogaro daga abin da har yanzu yana buƙatar yanke hukunci na ƙwararru.
A aikace, ƙungiyoyi masu ƙarfi waɗanda ke amfani da Rarraba Inference na LLM da Daidaita Load suna haɓaka gine-gine, bayanai, da zaɓin abubuwan more rayuwa a kan dogaro da farashi. Suna rubuta ƙayyadaddun ƙa'idodin nasara, gwaji akan bayanan gaskiya da gudanawar aiki, da jujjuyawar bisa ga tsarin gazawar da aka lura maimakon cin nasara na lokaci ɗaya. Wannan shine inda fahimtar ka'idar ta juya zuwa iyawa mai dorewa a cikin samfura, manufofi, da ayyuka.
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru. A lokaci guda, Haɓaka ma'auni ɗaya na iya ɓoye manyan raunin tsarin. Hanyar da ta fi dacewa ita ce haɗa saurin gwaji tare da horon gudanarwa: gudanar da matukin jirgi, kama shaida, buga rajistan ayyukan yanke shawara, da ci gaba da sabunta abubuwan tsaro kamar yadda halayen ƙira, tsammanin mai amfani, da buƙatun tsari ke tasowa.
Dabarun Tasiri
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru.
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba.
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa.
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa. A cikin ƙawance masu inganci, ana fassara wannan zuwa ƙa'idodin aiki waɗanda za a iya aunawa, iyakokin ikon mallaka, da kuma bita-da-kullin bita don ƙungiyoyi su iya haɓaka kwarin gwiwa a maimakon ɓata shakku.
Aiwatar da Gaskiyar Duniya
Dandali na chatbot yana sanya kowane zance zuwa kwafi yana riƙe da cache ɗinsa na KV, don haka bibiyar bibiyar ta buga cache ɗin prefix kuma amsa sauri.
Tsarin salo na RouteLLM yana aika tambayoyi masu sauƙi zuwa ƙaramin ƙira mai arha kuma yana haɓaka masu wuya kawai zuwa ƙirar iyaka, yanke farashi tare da ƙarancin ƙarancin inganci.
Kubernetes Ƙofar API Hannun Ƙofar Ƙofar Ƙofar API ta hanyar zurfin layin GPU mai rai da yanayin cache maimakon a fili zagaye-robin a cikin kwasfa.
LiteLLM yana ƙaddamar da zirga-zirgar ababen hawa a cikin OpenAI, Anthropic, da samfura masu ɗaukar nauyi tare da daidaita koma baya da ƙima-iyaka-ƙididdigewa lokacin da mai ba da sabis ya yi nasara.
Hanyoyin Aiwatarwa
LLM Inference Routing da Load Daidaita a aikace
Dandali na chatbot yana sanya kowane zance zuwa kwafi yana riƙe da cache ɗinsa na KV, don haka bibiyar bibiyar ta buga cache ɗin prefix kuma amsa sauri.
Dandali na chatbot yana sanya kowane tattaunawa zuwa kwafi yana riƙe da cache ɗinsa na KV, don haka bibiyar jujjuyawar ta buga cache prefix da amsa da sauri Ƙungiyoyi yawanci suna samun kyakkyawan sakamako lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓakar ɗan adam don ƙararraki, da bin diddigin nasarorin samarwa da farashi na kuskure akan lokaci.
LLM Inference Routing da Load Daidaita a aikace
Tsarin salo na RouteLLM yana aika tambayoyi masu sauƙi zuwa ƙaramin ƙira mai arha kuma yana haɓaka masu wuya kawai zuwa ƙirar iyaka, yanke farashi tare da ƙarancin ƙarancin inganci.
Tsarin salo na RouteLLM yana aika tambayoyi masu sauƙi zuwa ƙaramin ƙira mai arha kuma yana haɓaka masu wahala kawai zuwa ƙirar kan iyaka, yanke farashi tare da ƙarancin asarar ƙima Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓakar ɗan adam don shari'o'in gefen, da kuma bin diddigin abubuwan da ake samu da ƙima a kan lokaci.
LLM Inference Routing da Load Daidaita a aikace
Kubernetes Ƙofar API Hannun Ƙofar Ƙofar Ƙofar API ta hanyar zurfin layin GPU mai rai da yanayin cache maimakon a fili zagaye-robin a cikin kwasfa.
Kubernetes Ƙofar API Ƙofar Ƙofar Ƙofar Ƙofar API ta hanyar zurfin layin GPU mai rai da yanayin cache maimakon a fili zagaye-robin a cikin kwas ɗin Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓakar ɗan adam don shari'o'i, da kuma bin duk abubuwan da ake samu da kuma farashi na kuskure akan lokaci.
LLM Inference Routing da Load Daidaita a aikace
LiteLLM yana ƙaddamar da zirga-zirgar ababen hawa a cikin OpenAI, Anthropic, da samfura masu ɗaukar nauyi tare da daidaita koma baya da ƙima-iyaka-ƙididdigewa lokacin da mai ba da sabis ya yi nasara.
LiteLLM proxies zirga-zirga a fadin OpenAI, Anthropic, da kuma nau'ikan da aka gudanar da kai tare da daidaita koma baya da iyaka-ƙididdigar ƙima lokacin da mai ba da sabis ɗaya ya ƙulla Ƙungiyoyi yawanci suna samun sakamako mafi kyau lokacin da suka ayyana ma'auni masu inganci a gaba, kiyaye hanyar haɓaka ɗan adam don ƙarar ƙima, da kuma yin kuskure akan ƙimar yawan lokaci.
Hatsari & Tsare-tsare
Haɓaka ma'auni ɗaya na iya ɓoye manyan raunin tsarin.
Sau da yawa ana raina kayan more rayuwa da kuma kuɗin kulawa.
Tsaro da gibin lura na iya girma yayin da tsarin ke ƙara haɓaka.
Taswirar Hanya
Ƙayyade latency, inganci, da maƙasudin farashi kafin aiwatarwa.
Ƙayyade latency, inganci, da maƙasudin farashi kafin aiwatarwa. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli. Ɗauki kowane mataki azaman ƙofar shaida: idan ba a cika sharuɗɗa ba, dakatar da fitar, rufe tazarar, sannan kawai faɗaɗa amfani.