Velvet AI

Key takeaways

Velvet AI belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Velvet AI to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Velvet AI from memory before moving on to harder problems.

Reference excerpt

Velvet AI is a family of multilingual generative artificial intelligence models developed by Almawave, an Italian AI company. The Velvet models, including Velvet 14B and Velvet 2B, are foundational large language models (LLMs) designed and developed in Italy on Almawave's proprietary architecture. They were trained on the Leonardo supercomputer managed by CINECA and have been released in open-weight format. The Velvet models are designed to be energy-efficient. They support multiple languages, with an emphasis on Italian.

Models

Velvet 2B Velvet 2B, the model with 2 billion parameters, was trained in Italian and English on over 2 trillion tokens of data.

Velvet 14B Velvet 14B was trained on over 4 trillion tokens across six languages, with Italian comprising approximately 23% of the data. In addition to linguistic data, Velvet "incorporates over 400 billion tokens from more than 100 programming languages to facilitate more structured inferences." The development of Velvet AI reflects Almawave's strategic investment in creating high-performance, energy-efficient AI solutions that align with European regulatory frameworks. The models are ready to be used on major market platforms in the cloud, on-premise, and on the edge, and are integrated into Almawave's AIWave platform.

Velvet 25B Velvet 25B is a large language model (LLM) in Almawave's Velvet family. In general natural language processing terminology, LLMs are pretrained models learned by predicting tokens from context and used for conditional text generation. Almawave announced Velvet 25B on 14 October 2025, together with the multimodal text-to-speech model Velvet Speech 2B. The company described Velvet 25B as a 25-billion-parameter model optimized for text processing in all 24 official languages of the European Union and designed for advanced reasoning in complex environments. According to Almawave, Velvet 25B was trained natively across European languages rather than by using English as a reference language. The company states that training began from more than 15 trillion tokens and ended with more than 7 trillion tokens, and that the model has a 128,000-token context window, a 148,000-token vocabulary, six million instruction examples used for specialization, and a June 2025 data cutoff. Almawave said that the long-context architecture was intended to preserve consistency and accuracy across distant passages in large documents, including legal texts, scientific dossiers and legislative acts. On its technology page, Almawave presents Velvet 25B as suitable for large and complex texts in regulated or specialized domains and states that it can be deployed on a single GPU. The model is described by Almawave as supporting instruction following, information extraction, multi-step dynamic reasoning, structured output generation, function calling, machine translation, textual entailment, classification, question answering, multi-turn conversation, summarization, paraphrasing and retrieval-augmented generation (RAG). RAG is a method for combining parametric generation with retrieved non-parametric information sources. Almawave also said that Velvet 25B can be customized through reinforcement learning and integrated with agentic systems and external knowledge sources. A later company blog post listed healthcare, European law and public administration, education and culture, manufacturing and industry, and customer care among the domains represented in the training and customization pipeline. Almawave positioned Velvet 25B as an efficient enterprise model intended to reduce energy consumption and operating costs. The company said that the model could operate in cloud, on-premises and edge environments, and reported the use of optimization methods including adaptive computation time, post-training quantization, knowledge distillation and 4D parallelization, with claimed GPU-cluster efficiency above 90%. The model is integrated into Almawave's AIWave platform, which includes no-code and low-code tools for conversational agents, knowledge navigation and case management. Coverage by la Repubblica described Velvet 25B as part of an Italian, non-Anglocentric approach to generative AI, emphasizing privacy-preserving algorithms and compliance with European regulation. The Italian industry publication AI4Business listed Almawave among Italy's main enterprise AI developers and noted that Velvet 25B was presented for the 24 official EU languages with emphasis on reasoning, efficiency and operation on comparatively limited hardware, while cautioning that the performance claims came from the company. Mark Up similarly presented Velvet 25B as a European multilingual AI model designed to run on limited infrastructure and to support enterprise use cases, including retail applications.

References

See also CINECA Istituto Italiano per l’Intelligenza Artificiale (AI4I)

Worked examples

Example 1 — a first encounter with Velvet AI

Start with the simplest possible case. Write down what Velvet AI claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Velvet AI before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Velvet AI ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Velvet AI

In research: Velvet AI appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Velvet AI in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Velvet AI is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2024 in artificial intelligence, Artificial intelligence laboratories, Large language models, so understanding it makes those chapters shorter.
In everyday life: Look for Velvet AI outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Velvet AI in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Velvet AI means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Velvet AI out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Velvet AI in simple terms?

Velvet AI is a family of multilingual generative artificial intelligence models developed by Almawave, an Italian AI company. The Velvet models, including Velvet 14B and Velvet 2B, are foundational large language models (LLMs) designed and developed in Italy on Almawave's proprietary architecture.

Why does Velvet AI matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Velvet AI?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Velvet AI.