An alternative to foreign cloud LLMs
The first large language model built natively for Azerbaijani, deployed entirely on your own infrastructure. Available in 587B, 99B and 39B sizes.
The first large language model built natively for Azerbaijani
Most large language models available today were designed primarily for English and a small set of high-resource languages. When applied to Azerbaijani, these models encounter fundamental structural problems: their tokenizers cannot handle the ə character correctly, they fragment agglutinative word forms into inefficient token sequences, and they lack the cultural and contextual grounding that authentic Azerbaijani communication requires. The result is higher token consumption, degraded accuracy, and outputs that feel foreign to native speakers. Allmaz was created to solve these problems at the root, not through post-hoc fine-tuning on a foreign base model, but by building a large language model from the ground up specifically for Azerbaijani — trained on a corpus of more than 651 million carefully curated Azerbaijani words and equipped with a native tokenizer engineered for the language's unique morphological structure.
Why organizations choose Allmaz over generic cloud language models
True Azerbaijani-first architecture — the model and its tokenizer were engineered from the ground up for Azerbaijani, not adapted from a foreign base, ensuring authentic handling of the language's morphology, characters, and cultural context.
Complete data sovereignty — Allmaz runs fully on-premise within your own network, so sensitive documents, queries, and model outputs are never transmitted to an external cloud provider under any circumstances.
Proven tokenization efficiency — the native tokenizer processes Azerbaijani text 4.6× more efficiently than tokenizers designed for other languages, reducing computational overhead and improving response quality across all use cases.
Flexible deployment scale — three parameter sizes, 587B, 99B, and 39B, allow organizations to select the configuration that best matches their infrastructure capacity, latency requirements, and task complexity.
Benchmark-validated language understanding — performance is measured on TUMLU, a 38,139-question native benchmark spanning 11 disciplines, providing a transparent and domain-diverse quality baseline that translated test sets cannot offer.
Simplified compliance and data residency — fully on-premise deployment removes the legal and regulatory complexity of routing sensitive data through external cloud services, making it easier to meet local data protection obligations.
What sets Allmaz apart
Native Azerbaijani tokenizer
The tokenizer was designed specifically to handle the ə character and the agglutinative structure of Azerbaijani, where a single word can carry the meaning of an entire phrase. This results in 4.6× greater processing efficiency compared to tokenizers borrowed from models trained on other languages.
651M+ curated training words
The model was trained on a corpus of more than 651 million carefully curated Azerbaijani words, giving it a deep and authentic grounding in the language rather than relying on machine-translated or incidentally scraped content.
Three deployment sizes
Allmaz is available in 587B, 99B, and 39B parameter configurations. Organizations can select the size that best fits their infrastructure, latency targets, and use-case complexity without being locked into a single cloud-hosted option.
Fully on-premise deployment
The entire model runs within your own network. No data is sent to external servers, no API calls leave your perimeter, and no third-party cloud provider has access to your queries or outputs.
TUMLU benchmark validation
Model quality is assessed on TUMLU, a benchmark comprising 38,139 questions written by native speakers across 11 academic and professional disciplines. This provides a transparent, domain-diverse measure of real Azerbaijani language understanding.
How Allmaz fits into your organization
Frequently asked questions about Allmaz
How is Allmaz fundamentally different from applying a general-purpose cloud language model to Azerbaijani tasks?
General-purpose cloud models were not designed with Azerbaijani in mind. Their tokenizers cannot handle the ə character or agglutinative word structures efficiently, which leads to inflated token counts, higher processing costs, and reduced accuracy on native text. Allmaz was built natively for Azerbaijani from the training corpus up, and its purpose-built tokenizer processes the language 4.6× more efficiently, producing outputs that reflect genuine linguistic and cultural understanding.
Does any data leave our organization when we use Allmaz?
No. Allmaz is deployed fully on-premise within your own infrastructure. Every query, document, and model output remains inside your network at all times. No external cloud provider, including Allmaz, has any access to your data or the content of your interactions with the model.
How do we choose the right parameter size for our organization?
The appropriate size depends on your available hardware, acceptable latency, and the complexity of your language tasks. The 39B model is well suited to organizations with more constrained infrastructure or strict latency requirements. The 99B model provides a balanced mid-range option for a broad range of enterprise workloads. The 587B model is intended for the most demanding language understanding tasks where maximum capability takes priority over infrastructure cost.
What is the TUMLU benchmark and why does it matter for evaluating Azerbaijani language models?
TUMLU is a benchmark containing 38,139 questions written by native Azerbaijani speakers across 11 academic and professional disciplines. Unlike translated benchmarks, which carry the biases and gaps of their source language, TUMLU was created entirely in Azerbaijani, making it a reliable measure of how well a model actually understands the language in real-world contexts. Allmaz reports its performance on TUMLU to give organizations a transparent, domain-diverse quality baseline.
What types of organizations is Allmaz designed to serve?
Allmaz is suited for any organization that operates primarily in Azerbaijani and has data sovereignty, regulatory compliance, or language quality requirements that generic cloud models cannot meet. This includes public sector bodies, financial institutions, healthcare providers, legal organizations, and enterprises subject to local data protection regulations — any environment where keeping data on-premise and working with a genuinely native language model are non-negotiable requirements.
Ready to bring a native Azerbaijani LLM into your infrastructure?
Contact the Allmaz team to discuss deployment options, select the right model size for your environment, and see how a purpose-built Azerbaijani language model compares to the generic tools you are using today.
Request a demo