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EngGPT 2-16B-A3B e' il Large Language Model italiano sviluppato da Engineering Group per ottimizzare i processi di business garantendo controllo, efficienza e trasparenza nell'adozione dell'AI, in conformita' all'AI ACT.
Overview
EngGPT 2-16B-A3B e' il Large Language Model italiano sviluppato da Engineering Group per ottimizzare i processi di business garantendo controllo, efficienza e trasparenza nell'adozione dell'AI. Sviluppato from scratch in Italia, senza utilizzare modelli preesistenti, garantisce trasparenza sui processi di training e conformita' nativa all'AI Act. Progettato per contesti enterprise e PA, grazie all'architettura Mixture-of-Experts ottimizza costi di training e di inferenza garantendo prestazioni elevate, risultati accurati e ampia possibilita' di personalizzazione sul proprio dominio specifico. E' rilasciato open weight per analisi indipendenti e test, con piena disclosure del processo di training all'interno del technical report. Include la capacita' di doppio ragionamento e risposta del modello: una specifica ed una rapida, in lingue diverse in base all'esigenza. Nativamente ottimizzato per la lingua italiana, si integra nei sistemi esistenti per accelerare casi d'uso come assistenza knowledge-intensive, automazione di workflow complessi, analisi di dati complessi in NL, interpretazione di schemi industriali. E' agent-ready per orchestrare flussi agentic e automazioni su processi e use case mission-critical. Disponibile come servizio su marketplace AWS per un accesso immediato, con standard di sicurezza certificati e scalabilita' globale.
Con l'uso, si accetta il seguente AUP https://www.eng.it/content/dam/eng-portal/resources/documents/legal/Engineering_EngGPT_AUP_2026.03.10.pdf
Highlights
- Open Weight & Auditability - il modello e' pubblicato su HuggingFace, con pesi, documentazione e benchmark pubblici. Anche il processo di training e' trasparente: dataset, pipeline, tecniche, metriche e relative limitazioni sono disponibili nel "Technical Report" suArxiv.
- Training and Inference efficiency - Architettura Mixture-of-Experts e double reasoning consentono output di qualita' elevata e costi sotto controllo. Agent-ready per orchestrare e ottimizzare i processi di business.
- Customizability - Costi di training ridotti grazie al MoE consentono una rapida ed efficace verticalizzazione sul proprio dominio. Ottimizzato per la lingua e il contesto italiano, e' disponibile nelle 6 principali lingue europee (Italiano, Inglese, Tedesco, Spagnolo, Portoghese e Francese).
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $4.00 |
ml.g6e.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6e.xlarge instance type, real-time mode | $4.00 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $4.00 |
ml.g4dn.12xlarge Inference (Real-Time) | Model inference on the ml.g4dn.12xlarge instance type, real-time mode | $4.00 |
ml.g5.12xlarge Inference (Real-Time) | Model inference on the ml.g5.12xlarge instance type, real-time mode | $4.00 |
ml.g5.24xlarge Inference (Batch) | Model inference on the ml.g5.24xlarge instance type, batch mode | $4.00 |
ml.g5.24xlarge Inference (Real-Time) | Model inference on the ml.g5.24xlarge instance type, real-time mode | $4.00 |
ml.g5.48xlarge Inference (Batch) | Model inference on the ml.g5.48xlarge instance type, batch mode | $4.00 |
ml.g5.48xlarge Inference (Real-Time) | Model inference on the ml.g5.48xlarge instance type, real-time mode | $4.00 |
ml.g6e.12xlarge Inference (Real-Time) | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $4.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
- Primo rilascio del modello LLM deployato su AWS SageMaker AI
- Supporto inferenza real-time e batch, scalabile
- Pronto per integrazione pipeline e applicazioni cloud-native
Additional details
Inputs
- Summary
{ "model":"EngGPTMoe", "messages": [ { "role": "user", "content": "" } ], "chat_template_kwargs": { "enable_thinking": true | false , "reasoning_lang":"ita" | "en", "enable_turbo": true | false } }
{
"model":"EngGPTMoe",
"messages": [
{
"role": "user",
"content": "who are you?"
}
],
"chat_template_kwargs": {
"enable_thinking": false ,
"reasoning_lang": "en",
"enable_turbo": true
}
}
{"messages": [{"role": "user","content": "who are you?"}], "chat_template_kwargs": { "enable_thinking": false ,"reasoning_lang": "en","enable_turbo": true }}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
model | The model identifier to use. See /v1/models for available models. | String | Yes |
messages | List of message objects forming the conversation. | Array of strings | Yes |
temperature | Sampling temperature between 0.0 and 2.0. Lower values make output more deterministic. | float, between 0.0 and 2.0 | No |
max_tokens | Maximum number of tokens to generate in the response. | integer | No |
stream | If true, responses are streamed as Server-Sent Events (SSE). | Boolean (true, false) | No |
stop | Up to 4 sequences at which the model will stop generating further tokens. | string or array of strings | Yes |
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