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Xu","lazyEnabled":true,"width":"512",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-237061ae66","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/danmei-xu/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Danmei Xu"}},{"authorImage":{"id":"image-95cfd3d98b","height":"653","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8049a19c-6f69-400d-9f50-c2ee72ba1d88/arun-agarwal.jpg?quality=85&preferwebp=true","alt":"Arun Agarwal","lazyEnabled":true,"width":"720",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-8f199d45b6","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/arun-agarwal/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Arun Agarwal"}}],"image":{"id":"image-943d0cd38a","height":"720","isLcpImage":false,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--4783ab6d-6b62-498d-9580-10dd7ab80d9d/18-snowflake.png?quality=85&preferwebp=true","lazyEnabled":true,"width":"1680",":type":"snowflake-site/components/image"},"timeToRead":"7","publicationDate":"SEP 24, 2026","tag":{"tagText":"AI e ML","tagColor":"#C54985"},"title":{"lines":["Kimi K3 arriva su Snowflake Cortex AI"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnCount":12,"columnClassNames":{"blog_text_188642604":"aem-GridColumn aem-GridColumn--default--12","code_snippet":"aem-GridColumn aem-GridColumn--default--12","blog_text_454844314":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn aem-GridColumn--default--12","blog_text_92641987":"aem-GridColumn aem-GridColumn--default--12","code_snippet_35906726":"aem-GridColumn aem-GridColumn--default--12","blog_text_1752957085":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-8cadd28227","text":"\u003Cp\u003EKimi K3, il modello open-weight di Moonshot AI, è ora disponibile in private preview su Snowflake Cortex AI. Aggiungere K3 a Cortex AI offre ai team un’altra opzione per abbinare il modello giusto al processo, che si tratti di reasoning di frontiera per task complessi o di riservare i modelli proprietari più costosi ai casi in cui fanno la differenza maggiore.\u003C/p\u003E\n\u003Cp\u003EAl lancio, i clienti in preview possono utilizzare Kimi K3 tramite Cortex AI Functions e Snowflake Cortex Inference. Il supporto per Snowflake CoCo, Snowflake Cortex Agents e Snowflake CoWork sarà disponibile a breve.\u003C/p\u003E\n\u003Cp\u003ENelle \u003Ca href=\"https://www.kimi.ai/blog/kimi-k3\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Evalutazioni pubblicate da Moonshot\u003C/a\u003E, K3 ha costruito autonomamente un compilatore GPU funzionante da zero, ha riprodotto una complessa ricerca di astrofisica in due ore e ha sostenuto sessioni di coding di più giorni su repository di grandi dimensioni. Questi risultati indicano un modello progettato per lavori che richiedono molti passaggi, utilizzano più di un tipo di input e devono mantenere il contesto nel tempo. Le sezioni seguenti illustrano come si traduce tutto questo su Snowflake.\u003C/p\u003E\n\u003Ch2\u003EKimi K3: progettato per lavori di lunga durata\u003C/h2\u003E\n\u003Cp\u003EKimi K3 è un modello mixture-of-experts con 2.800 miliardi di parametri che attiva 16 esperti su 896 per token, utilizzando il framework Stable LatentMoE di Moonshot. Moonshot riporta un miglioramento di 2,5 volte nell’efficienza di scaling rispetto al suo predecessore, K2.\u003C/p\u003E\n\u003Cp\u003EIn pratica, questa architettura è pensata per lavori che richiedono più passaggi e utilizzano più di un tipo di input. Moonshot evidenzia sessioni di coding di lunga durata che navigano tra repository di grandi dimensioni, orchestrano strumenti da terminale e utilizzano screenshot per perfezionare il risultato. I suoi esempi di ricerca combinano revisione della letteratura, codice eseguibile, validazione e visualizzazioni interattive. Per i team, ciò si traduce in sviluppo a livello di repository, sintesi della ricerca e workflow analitici in cui il modello deve mantenere il contesto nel corso di molti turni.\u003C/p\u003E\n\u003Cp\u003EQuesti esempi descrivono funzionalità a livello di modello. Gli input, gli strumenti, i limiti di contesto e la gestione dell’output disponibili dipendono dalla superficie Snowflake e dalla configurazione utilizzata.\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E\u003Csub\u003EFonti: Moonshot AI, \u003Ca href=\"https://www.kimi.ai/blog/kimi-k3\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EKimi K3: Open Frontier Intelligence\u003C/a\u003E e \u003Ca href=\"https://huggingface.co/moonshotai/Kimi-K3%20\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EHugging Face\u003C/a\u003E\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\n\u003Ch2\u003ECosa puoi fare con Kimi K3 su Snowflake\u003C/h2\u003E\n\u003Cp\u003EKimi K3 aggiunge un’opzione di modello open-weight alla selezione di modelli Snowflake. Durante la private preview, i team possono valutarlo rispetto ai propri task e requisiti di qualità senza configurare un deployment di inferenza separato.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EProssimamente\u003C/b\u003E, il supporto per Kimi K3 in Snowflake CoCo, Snowflake CoWork e Cortex Agents estenderà questa valutazione ai workflow di coding, ricerca e agenti AI per i dati:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESviluppare con Snowflake CoCo:\u003C/b\u003E Il lavoro a livello di repository dipende da più del semplice file che si ha davanti. Uno sviluppatore potrebbe dover tracciare una chiamata attraverso diversi moduli, comprendere i test esistenti e portare una modifica fino a un risultato funzionante. L’attenzione di Kimi K3 per il coding di lunga durata rende quest’area particolarmente utile da valutare in CoCo.\u003Cbr\u003E\nAvvia una nuova sessione con Kimi K3 e assegna un task delimitato con criteri di accettazione espliciti. Moonshot consiglia di non passare a K3 durante una sessione già in corso con un altro modello, poiché il modello è sensibile al modo in cui viene mantenuta la cronologia della conversazione.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EPortare la ricerca negli agenti AI per i dati:\u003C/b\u003E Con il supporto in Snowflake CoWork e Cortex Agents, i team potranno valutare workflow di ricerca e analisi basati sui dati e sugli strumenti disponibili al proprio agente AI. Ad esempio, utilizza Kimi K3 per confrontare le note di ricerca sui clienti, identificare requisiti in conflitto e preparare un brief con le evidenze a supporto di ogni raccomandazione.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003ERisultati dei benchmark\u003C/h3\u003E\n\u003Cp\u003EIl seguente riepilogo mostra i risultati della tabella di valutazione pubblicata da Moonshot AI, che include benchmark di coding, agentici, di reasoning e visivi. Per la metodologia completa, i dettagli sull’harness e le note, consulta \u003Ca href=\"https://www.kimi.ai/blog/kimi-k3\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Ela scheda del modello Kimi K3 di Moonshot AI\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_188642604":{"id":"blog-text-2125492acf","text":"\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EBenchmark\u003C/th\u003E\n\u003Cth\u003ERisultato Kimi K3\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003EDeepSWE\u003C/td\u003E\n\u003Ctd\u003E67,5\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EProgramBench\u003C/td\u003E\n\u003Ctd\u003E77,8\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ETerminal-Bench 2.1\u003C/td\u003E\n\u003Ctd\u003E88,3\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EFrontierSWE\u003C/td\u003E\n\u003Ctd\u003E81,2\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EBrowseComp\u003C/td\u003E\n\u003Ctd\u003E91,2\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EDeepSearchQA\u003C/td\u003E\n\u003Ctd\u003E95,0\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EResearchRubrics\u003C/td\u003E\n\u003Ctd\u003E76,2\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EGDPval-AA v2\u003C/td\u003E\n\u003Ctd\u003E1686\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EGPQA Diamond\u003C/td\u003E\n\u003Ctd\u003E93,5\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EAA-LCR\u003C/td\u003E\n\u003Ctd\u003E74,7\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EOmniDocBench\u003C/td\u003E\n\u003Ctd\u003E91,1\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EPerceptionBench\u003C/td\u003E\n\u003Ctd\u003E58,5\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_454844314":{"id":"blog-text-17d1a3fb40","text":"\u003Cp\u003E\u003Ci\u003E\u003Csub\u003EFonte: Moonshot AI\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\n\u003Ch3\u003ESintetizzare la ricerca con Cortex AI Functions\u003C/h3\u003E\n\u003Cp\u003ECortex AI Functions porta le chiamate ai modelli in SQL. Un analista può utilizzare AI_COMPLETE per confrontare note di ricerca o sintetizzare il testo di un documento senza scrivere un client API separato. Questo esempio autonomo utilizza interviste fittizie sul prodotto:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-e2fc463dc4","codeSnippet":"SELECT AI_COMPLETE(  \r\n  'kimi-k3',  \r\n  'Compare these fictional product research notes. Interview A: export jobs time out on large reports. Interview B: users need scheduled exports. Interview C: report definitions are hard to reuse. Group the needs, explain how they differ, and propose follow-up questions. Use only the supplied notes.'  \r\n) AS research_summary;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_92641987":{"id":"blog-text-e92eb95040","text":"\u003Ch3\u003EChiamare Kimi K3 con l’SDK di OpenAI\u003C/h3\u003E\n\u003Cp\u003EKimi K3 supporta il formato di richiesta Chat Completions, quindi gli sviluppatori possono utilizzare l’SDK di OpenAI per richiamarlo tramite l’API REST Cortex di Snowflake. Imposta SNOWFLAKE_PAT sul tuo token di accesso programmatico Snowflake e SNOWFLAKE_ACCOUNT_URL sull’URL HTTPS del tuo account.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_35906726":{"id":"code-snippet-082d1c461c","codeSnippet":"import os  \r\nfrom openai import OpenAI\r\n\r\naccount_url = os.environ[\"SNOWFLAKE_ACCOUNT_URL\"].rstrip(\"/\")\r\n\r\nclient = OpenAI(  \r\n    api_key=os.environ[\"SNOWFLAKE_PAT\"],  \r\n    base_url=f\"{account_url}/api/v2/cortex/v1\",  \r\n)  \r\nresponse = client.chat.completions.create(  \r\n    model=\"kimi-k3\",  \r\n    messages=[{  \r\n        \"role\": \"user\",  \r\n        \"content\": (  \r\n            \"Compare these fictional product research notes. \"  \r\n            \"Interview A: export jobs time out on large reports. \"  \r\n            \"Interview B: users need scheduled exports. \"  \r\n            \"Interview C: report definitions are hard to reuse. \"  \r\n            \"Group the needs, explain how they differ, and propose \"  \r\n            \"follow-up questions. Use only the supplied notes.\"  \r\n        ),  \r\n    }],  \r\n)  \r\nprint(response.choices[0].message.content)","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1752957085":{"id":"blog-text-273006b71a","text":"\u003Ch2\u003ECosa sapere prima di iniziare\u003C/h2\u003E\n\u003Cul\u003E\n\u003Cli\u003ELa cronologia della conversazione è importante. K3 è stato addestrato mantenendo la cronologia del ragionamento. Passare a K3 a metà sessione da un altro modello, oppure utilizzare un harness che elimina il contenuto di ragionamento, può peggiorare la qualità dell’output. Avvia sessioni nuove quando valuti K3.\u003C/li\u003E\n\u003Cli\u003EIl modello può essere proattivo. Quando K3 riscontra problemi minori o un intento ambiguo, potrebbe agire per tuo conto invece di chiedere chiarimenti. Utilizza vincoli espliciti nei prompt di sistema e assicurati di usare autorizzazioni di approvazione appropriate per l’agente AI nei deployment che richiedono limiti rigorosi.\u003C/li\u003E\n\u003Cli\u003EMoonshot afferma che la user experience complessiva di K3 “è ancora indietro rispetto ai modelli proprietari più potenti, Claude Fable 5 e GPT 5.6 Sol”. Considera K3 come una solida opzione open-weight da valutare, non come un sostituto immediato per ogni workload.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003EInizia subito\u003C/h2\u003E\n\u003Cp\u003EIl modello migliore per un task dipende dal task. In private preview, Kimi K3 offre ai team un modello open-weight avanzato che possono valutare tramite Cortex AI Functions e Cortex Inference senza dover effettuare il provisioning di un’infrastruttura separata. Utilizza l’ID modello \u003Ccode\u003Ekimi-k3\u003C/code\u003E, esegui un task che già conosci e confronta i risultati.\u003C/p\u003E\n\u003Cp\u003EI modelli open-weight di questa scala richiedevano in passato infrastrutture dedicate e un team per gestirle. Ora si riducono a un semplice cambio di ID modello. Ogni nuovo modello sulla piattaforma AI gestita di Snowflake amplia ciò che i team possono valutare, e K3 ne aggiunge uno progettato per lavori che richiedono molti passaggi per essere completati.\u003C/p\u003E\n\u003Ch2\u003EScopri di più\u003C/h2\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESviluppare applicazioni agentiche con Cortex Agents:\u003C/b\u003E inizia a usare \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECortex Agents\u003C/a\u003E e scopri come sviluppare esperienze di intelligenza artificiale basate sui tuoi dati aziendali.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIniziare a usare Snowflake CoWork:\u003C/b\u003E scopri di più su \u003Ca href=\"https://www.snowflake.com/it/product/snowflake-cowork/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake CoWork\u003C/a\u003E e provalo con la nostra \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cowork/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eguida introduttiva\u003C/a\u003E.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIniziare a sviluppare con Snowflake CoCo:\u003C/b\u003E scopri di più su \u003Ca href=\"https://www.snowflake.com/it/product/features/cortex-code/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESnowflake CoCo\u003C/a\u003E e mettiti all’opera con la \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-coco-desktop/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eguida introduttiva di CoCo Desktop\u003C/a\u003E.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EL’API REST Cortex ti dà accesso ai principali modelli di frontiera tramite l’endpoint o l’SDK che preferisci. Tutta l’inferenza viene eseguita all’interno del perimetro Snowflake, quindi i tuoi dati rimangono sicuri e all’interno dei tuoi confini di governance. Esplora \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-rest-api\" target=\"_blank\" rel=\"noopener noreferrer\"\u003El’API REST Cortex\u003C/a\u003E per saperne di più.\u003C/p\u003E\n\u003Ch3\u003E \u003C/h3\u003E\n\u003Cp\u003E\u003Ci\u003E\u003Csub\u003E\u003Cb\u003EDichiarazioni previsionali\u003C/b\u003E\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E\u003Csub\u003EQuesto articolo contiene delle affermazioni riferite al futuro, tra cui offerte future di prodotto, che però non rappresentano un impegno a fornire alcuna offerta di prodotto. I risultati e le offerte effettivi possono differire e sono soggetti a rischi e incertezze noti e ignoti. 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