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&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Si conosce la struttura interna solo di LLM &lt;em&gt;open&lt;/em&gt; o comunque documentati, mentre per i modelli proprietari la conoscenza &amp;egrave; parziale o solo ad alto livello.&lt;span&gt;[&lt;a href=&quot;https://aws.amazon.com/it/what-is/large-language-model/&quot;&gt;aws.amazon&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;h2 id=&quot;cosa-significa-struttura-interna&quot; class=&quot;font-editorial font-bold mb-2 mt-4 [.has-inline-images_&amp;amp;]:clear-end text-lg first:mt-0 md:text-lg [hr+&amp;amp;]:mt-4&quot;&gt;Cosa significa &amp;ldquo;struttura interna&amp;rdquo;&lt;/h2&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Per &amp;ldquo;struttura interna&amp;rdquo; di un LLM si intende l&amp;rsquo;architettura dettagliata: numero di layer, dimensioni dei &lt;strong&gt;embedding&lt;/strong&gt;, numero di teste di attenzione, tipo di posizional encoding, eventuali modifiche al Transformer standard, ecc.&lt;span&gt;[&lt;a href=&quot;https://culturedigitali.eu/notizie/llm-come-funziona-spiegazione-semplice-dei-large-language-models/&quot;&gt;culturedigitali&lt;/a&gt;]&lt;/span&gt;&lt;br /&gt;Tutti i modelli moderni si basano su varianti dell&amp;rsquo;architettura &lt;strong&gt;Transformer&lt;/strong&gt;, con self‑attention, feed‑forward, residual connections e layer normalization, ma le implementazioni concrete differiscono nei dettagli.&lt;span&gt;[&lt;a href=&quot;https://culturedigitali.eu/notizie/llm-come-funziona-spiegazione-semplice-dei-large-language-models/&quot;&gt;culturedigitali&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;h2 id=&quot;llm-con-architettura-pubblica&quot; class=&quot;font-editorial font-bold mb-2 mt-4 [.has-inline-images_&amp;amp;]:clear-end text-lg first:mt-0 md:text-lg [hr+&amp;amp;]:mt-4&quot;&gt;LLM con architettura pubblica&lt;/h2&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Per questi modelli sono noti sia i paper che spesso i pesi, quindi la struttura interna &amp;egrave; completamente ispezionabile:&lt;/p&gt;
&lt;ul class=&quot;marker:text-quiet list-disc pl-8&quot;&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;GPT‑2 (OpenAI): paper e codice disponibili, architettura Transformer decoder‑only, dimensioni e numero di layer documentati.&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;BERT e derivate (Google, open source): architettura encoder‑only Transformer, tutte le hyper‑parameter pubbliche.&lt;span&gt;[&lt;a href=&quot;https://www.redhat.com/it/topics/ai/what-are-large-language-models&quot;&gt;redhat&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;T5, mT5 (Google): encoder‑decoder Transformer, descritti nel paper &amp;ldquo;Exploring the Limits of Transfer Learning with a Unified Text‑to‑Text Transformer&amp;rdquo;.&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;LLaMA / LLaMA 2 (Meta): paper con dettagli architetturali, varianti decoder‑only con modifiche su attention, posizional encoding, ecc.&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Mistral, Mixtral (Mistral AI): architetture sparse Mixture‑of‑Experts, numero di esperti, routing, dimensioni dei layer pubbliche nei rispettivi report tecnici.&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Falcon, GPT‑J, GPT‑NeoX, BLOOM, Pythia, Phi‑2, Gemma, etc.: tutti con repository e/o paper che descrivono nel dettaglio la struttura dei blocchi Transformer e le dimensioni.&lt;span&gt;[&lt;a href=&quot;https://www.redhat.com/it/topics/ai/what-are-large-language-models&quot;&gt;redhat&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;In generale, quasi tutti i LLM rilasciati come open‑source (modelli Hugging Face, Meta, Mistral, EleutherAI, BigScience, Microsoft Phi, Google Gemma&amp;hellip;) hanno architettura interna nota e spesso anche pesi accessibili.&lt;span&gt;[&lt;a href=&quot;https://www.redhat.com/it/topics/ai/what-are-large-language-models&quot;&gt;redhat&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;h2 id=&quot;modelli-proprietari-con-struttura-solo-parzialment&quot; class=&quot;font-editorial font-bold mb-2 mt-4 [.has-inline-images_&amp;amp;]:clear-end text-lg first:mt-0 md:text-lg [hr+&amp;amp;]:mt-4&quot;&gt;Modelli proprietari con struttura solo parzialmente nota&lt;/h2&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Per i modelli commerciali di grandi aziende la situazione &amp;egrave; diversa: si conosce la macro‑architettura (Transformer decoder‑only, MoE, dimensioni indicative) ma non tutti i dettagli, n&amp;eacute; i pesi:&lt;/p&gt;
&lt;ul class=&quot;marker:text-quiet list-disc pl-8&quot;&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;GPT‑3, GPT‑4, GPT‑4.1, ecc. (OpenAI): descritti come transformer autoregressivi con enormi numeri di parametri, ma solo specifiche ad alto livello; molti dettagli (es. strutture interne precise, tecniche di pre‑training) non sono pubblici.&lt;span&gt;[&lt;a href=&quot;https://www.redhat.com/it/topics/ai/what-are-large-language-models&quot;&gt;redhat&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Claude (Anthropic), Gemini (Google), alcuni modelli di AWS Bedrock, Azure OpenAI: schemi generali disponibili nelle documentazioni, ma senza descrizione completa di tutti i layer e dei pesi.&lt;span&gt;[&lt;a href=&quot;https://aws.amazon.com/it/what-is/large-language-model/&quot;&gt;aws.amazon&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Modelli chiusi integrati in prodotti (assistenti vocali, piattaforme enterprise): in genere si sa che sono LLM basati su Transformer, ma la struttura concreta non &amp;egrave; documentata.&lt;span&gt;[&lt;a href=&quot;https://aws.amazon.com/it/what-is/large-language-model/&quot;&gt;aws.amazon&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;In questi casi si pu&amp;ograve; parlare di &amp;ldquo;architettura tipo&amp;rdquo;, ma non di conoscenza piena della struttura interna.&lt;/p&gt;
&lt;h2 id=&quot;sintesi-pratica-per-te-come-dev&quot; class=&quot;font-editorial font-bold mb-2 mt-4 [.has-inline-images_&amp;amp;]:clear-end text-lg first:mt-0 md:text-lg [hr+&amp;amp;]:mt-4&quot;&gt;Sintesi pratica per te come dev&lt;/h2&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Se ti interessa studiare o modificare la &lt;strong&gt;struttura interna&lt;/strong&gt;:&lt;/p&gt;
&lt;ul class=&quot;marker:text-quiet list-disc pl-8&quot;&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Devi guardare a modelli open source (Meta LLaMA, Mistral, Gemma, Falcon, BLOOM, Pythia, Phi‑2, ecc.), dove paper e pesi ti permettono di analizzare ogni layer.&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;py-0 my-0 prose-p:pt-0 prose-p:mb-2 prose-p:my-0 [&amp;amp;&amp;gt;p]:pt-0 [&amp;amp;&amp;gt;p]:mb-2 [&amp;amp;&amp;gt;p]:my-0&quot;&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Per i modelli chiusi (GPT‑4, Claude, Gemini) puoi solo inferire architetture generali e tecniche di training dalle info pubbliche, ma non hai accesso ai dettagli implementativi completi.&lt;span&gt;[&lt;a href=&quot;https://aws.amazon.com/it/what-is/large-language-model/&quot;&gt;aws.amazon&lt;/a&gt;]&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p class=&quot;my-2 [&amp;amp;+p]:mt-4 [&amp;amp;_strong:has(+br)]:inline-block [&amp;amp;_strong:has(+br)]:align-top&quot;&gt;Se vuoi, nel prossimo messaggio posso suggerirti una &amp;ldquo;lista da studiare&amp;rdquo; con link a repository/model card utili per capire davvero l&amp;rsquo;interno di diversi LLM (decoder‑only, encoder‑only, encoder‑decoder, MoE).&lt;/p&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;

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          api:5, kaggle:5, microsoft:5, tool:5, saas:5, software:5, slm:5, llm:5, deep learning:5        

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ID: 68133 • Tipo: WPG • Reg: 2026-07-16 • Mod: 29/07/2026 20:16