{"id":182824,"date":"2026-08-11T04:54:15","date_gmt":"2026-08-11T02:54:15","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/yazykovaya-model-s-289-mln-parametrov-zarabotala-na-esp32-s3"},"modified":"2026-08-12T18:21:41","modified_gmt":"2026-08-12T16:21:41","slug":"yazykovaya-model-s-289-mln-parametrov-zarabotala-na-esp32-s3","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/news\/yazykovaya-model-s-289-mln-parametrov-zarabotala-na-esp32-s3","title":{"rendered":"Modelul lingvistic cu 28,9 milioane de parametri a func\u021bionat pe ESP32-S3","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

<\/i><\/a>
\nDezvoltatorul Viacheslav Serbov, cunoscut sub pseudonimul slvDev, a demonstrat<\/a> realizeaz\u0103 o generare complet local\u0103 de text pe microcontrolerul ESP32-S3. Modelul s\u0103u lingvistic con\u021bine 28,9 milioane de parametri \u0219i produce aproximativ 9,9 tokeni pe secund\u0103, f\u0103r\u0103 a apela la servere sau alte resurse de calcul externe. Pove\u0219tile scurte generate sunt afi\u0219ate pe un display OLED conectat.<\/p>\n

 <\/p>\n

Proiect esp32-ai<\/a> a fost testat pe modulul ESP32-S3 N16R8, care dispune de 512 KB SRAM integrat, 8 MB PSRAM \u0219i 16 MB memorie flash. Modelul cuantificat la patru bi\u021bi ocup\u0103 14,9 MB. \u00cen depozitul sunt publicate codul surs\u0103 al mediului de execu\u021bie \u00een C, unelte pentru antrenament \u0219i cuantificare a modelului, firmware, teste \u0219i scenarii de \u00eenc\u0103rcare pe plac\u0103.<\/p>\n

 <\/p>\n

Celor 28,9 milioane de parametri nu trebuie s\u0103 li se fac\u0103 compara\u021bii directe cu num\u0103rul de parametri ai LLM-urilor obi\u0219nuite de birou. Conform raportului detaliat al dezvoltatorului<\/a>, modelul este format dintr-un nucleu de calcul dens de aproximativ 559 de mii de parametri, un strat de ie\u0219ire de 3,1 milioane de parametri \u0219i o tabel\u0103 de reprezent\u0103ri vectoriale stratificate de aproximativ 25 de milioane de parametri. Aceast\u0103 din urm\u0103 component\u0103 asigur\u0103 cea mai mare parte din dimensiunea formal\u0103 a modelului, dar nu este procesat\u0103 integral la generarea fiec\u0103rui token.<\/p>\n

 <\/p>\n

Modelul utilizeaz\u0103 trei niveluri de memorie. Datele cele mai frecvent utilizate se afl\u0103 \u00een SRAM intern, stratul de ie\u0219ire, cache-ul KV \u0219i buffer-ele temporare \u00een PSRAM, iar tabelul de 25 de milioane de parametri r\u0103m\u00e2ne \u00een memoria flash \u0219i este accesibil printr-o mapare \u00een spa\u021biul de adresare. Pentru fiecare token, sunt citite doar aproximativ \u0219ase r\u00e2nduri, cu un volum total de aproximativ 450 de bytes.<\/p>\n

 <\/p>\n

Un astfel de dispozitiv se bazeaz\u0103 pe tehnologia Per-Layer Embeddings, aplicat\u0103 de Google \u00een Gemma 3n. Conform documenta\u021biei Google<\/a>, parametrii PLE pot fi stoca\u021bi \u00een afara memoriei active a modelului \u0219i ad\u0103uga\u021bi \u00een procesul de inferen\u021b\u0103 pe m\u0103sur\u0103 ce sunt procesate straturile individuale. \u00cen esp32-ai, aceast\u0103 abordare este transferat\u0103 \u00een ierarhia de memorie a microcontrolerului, unde SRAM-ul rapid este deosebit de limitat.<\/p>\n

 <\/p>\n

Prima versiune corect\u0103 a mediei de execu\u021bie scrise \u00een C genera doar 0,57 tokeni pe secund\u0103. Dup\u0103 plasarea stratului de ie\u0219ire \u00een PSRAM, trecerea la activ\u0103ri pe 8 bi\u021bi, distribuirea calculelor \u00eentre cele dou\u0103 nuclee Xtensa LX7 \u0219i alte optimiz\u0103ri, \u00eent\u00e2rzierea a fost redus\u0103 la 94,9 ms pe pasul modelului. Viteza final\u0103 m\u0103surat\u0103 a atins 9,88 tokeni pe secund\u0103, lu\u00e2nd \u00een considerare \u00eentregul proces de generare. Principala limitare acum este l\u0103\u021bimea de band\u0103 PSRAM la citirea stratului de ie\u0219ire.<\/p>\n

 <\/p>\n

Modelul este antrenat pe un set de date sintetic TinyStories<\/a>, const\u00e2nd din pove\u0219ti simple \u00een limba englez\u0103 cu un vocabular limitat. Prin urmare, este capabil s\u0103 continue fraze \u0219i s\u0103 compun\u0103 pove\u0219ti scurte \u0219i coerente, dar nu este destinat s\u0103 r\u0103spund\u0103 la \u00eentreb\u0103ri, s\u0103 execute instruc\u021biuni, s\u0103 programeze sau s\u0103 reproduc\u0103 cuno\u0219tin\u021be exacte. Dezvoltatorul consider\u0103 c\u0103 aceast\u0103 munc\u0103 reprezint\u0103 \u00een primul r\u00e2nd o demonstra\u021bie a plas\u0103rii eficiente a modelului \u00een memoria microcontrolerului, nu un chatbot autonom finalizat.<\/p>\n

 <\/p>\n

\u00cen documenta\u021bia actual\u0103, autorul subliniaz\u0103 separat c\u0103 esp32-ai este o dezvoltare de sine st\u0103t\u0103toare. Proiectele llama2.c<\/a> Andrei Karpathy \u0219i un lansare anterioar\u0103 a modelului de 260.000 de parametri pe ESP32-S3 sunt men\u021bionate doar ca repere \u0219i exemple ale lucr\u0103rilor anterioare: codul, punctele de control \u0219i metodologia nu sunt preluate direct din acestea. Codul surs\u0103 esp32-ai este distribuit sub licen\u021ba MIT<\/a>.<\/p>\n

 <\/p>\n

Sursa: linux.org.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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esp32-ai \u0438\u0441\u043f\u044b\u0442\u0430\u043d \u043d\u0430 \u043c\u043e\u0434\u0443\u043b\u0435 ESP32-S3 N16R8, \u0440\u0430\u0441\u043f\u043e\u043b\u0430\u0433\u0430\u044e\u0449\u0435\u043c […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-182824","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Modelul lingvistic cu 28,9 milioane de parametri a func\u021bionat pe ESP32-S3 | 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