LLM Inference Routing da Load Daidaita
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.
Dubawa
Done well, it cuts latency and cost; done poorly, it causes timeouts and idle GPUs.
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.
Dabarun Tasiri
Kudin da kasafin kuɗi
Hukunce-hukuncen gine-gine suna haifar da aiki da tsadar aiki na shekaru.
Shawarwari masu haske
Ilimin fasaha yana taimaka wa ƙungiyoyi su zaɓi tari mai kyau, ba kawai sabon abu ba.
Kula da inganci
Zaɓuɓɓukan injiniya mafi kyau suna rage abin dogaro a cikin samarwa.
Makomar Rarraba Inference na LLM da Daidaita Load
Roting yana zama babban aji na farko, abin koyi. Ayyuka kamar Ƙofar Kubernetes' Ƙofar API Extension, tarin samar da vLLM, da LiteLLM/Masu amfani da hanyar sadarwa na Manzo suna daidaita tsarin ma'auni da ƙididdiga masu tsada. Yi tsammanin ƙarin tsarin sarrafa harshe da tushen wahala (style RouteLLM), jerin fifikon fifikon SLA, yankuna da yawa da wayar da kan tabo, da manufofin ƙarfafa-koyi waɗanda ke daidaita latency, fitarwa, da farashin dala a cikin ainihin lokaci azaman samfuri, farashi, da canjin zirga-zirga.
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.
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.
Alamar ma'auni a ƙarƙashin ainihin kaya da yanayin bayanai.
Kula da kayan aiki don kurakurai, ɗigo, da tasirin mai amfani.
Shirya bijirowa da hanyoyin mayar da martani kafin sikeli.
Ci gaba da Bincike
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the LLM Inference Routing and Load Balancing quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Jagora na gaba
Zane-zane na Seldon Core da Inference
Tambayoyin da ake yawan yi
What is LLM Inference Routing and Load Balancing?
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.
Me yasa zagaye-robin bayyananne sau da yawa rashin dabarar daidaita nauyin nauyi don ƙaddamar da LLM?
Buƙatun LLM sun bambanta sosai cikin tsayi/ farashi, kuma ma'ajin KV na kwafi yana sa zaman zama mai ɗaci, don haka makauniyar hawan keke baya watsi da alaƙar cache da ainihin kaya.
Menene 'prefix-cache affinity' ke kokarin cimmawa?
Idan kwafi ya riga ya riƙe cache na KV don prefix ɗin da aka raba, zazzage bin diddigin a can yana sake amfani da cache maimakon sake lissafin shi, adana ƙididdigewa da latency.
A cikin tudun ƙirar ƙira mai wahala, menene yawanci ke faruwa ga tambaya mai sauƙi?
Model hanyoyin kamar RouteLLM suna aika tambayoyi masu sauƙi zuwa ƙaramin ƙira mai arha kuma suna adana samfuran iyaka masu tsada don masu wuya, yanke farashi tare da ƙarancin ƙarancin inganci.
Wanne sigina mai raye-raye ya fi amfani ga ma'aunin nauyi mai sane da LLM?
Haƙiƙanin telemetry na baya-alamu masu jiran gado, cikar tsari, zama cikin cache-yana nuna nauyin gaske fiye da ƙidaya mai sauƙi.
Menene kayan aiki kamar LiteLLM ke samarwa a cikin saitin masu ba da yawa?
LiteLLM yana aiki azaman wakili mai ba da hanya tsakanin masu samarwa (OpenAI, Anthropic, mai gudanar da kansa), yana ƙara faɗuwa da daidaita ma'auni-iyaka.