AI for the Azerbaijani language
The first large language model built natively for Azerbaijani, deployed entirely on your own infrastructure. Available in 587B, 99B and 39B sizes.
AI Built Natively for the Azerbaijani Language
Most large language models are designed around high-resource languages, leaving Azerbaijani speakers with tools that routinely mishandle native script, grammar, and vocabulary. Allmaz has developed the first large language model built natively for Azerbaijani — trained from scratch on over 651 million curated Azerbaijani words rather than adapted from a foreign-language base. The model is equipped with a purpose-built tokenizer that correctly processes the ə character and the language's agglutinative morphology, where meaning is layered through chains of suffixes that generic tokenizers fragment and misrepresent. This native foundation makes the model 4.6 times more efficient on Azerbaijani text than general-purpose alternatives, delivering faster responses and lower compute costs for the same workload.
Why a Native Azerbaijani LLM Matters
Accurate language understanding from the ground up: the model is trained natively on Azerbaijani, not adapted from a foreign-language base, so it correctly handles the grammar, vocabulary, and script that general-purpose models routinely misprocess.
4.6 times greater efficiency on Azerbaijani text: because the native tokenizer segments words correctly, the model requires fewer tokens to represent the same content, translating directly into faster inference and lower computational cost per request.
Full data sovereignty with on-premise deployment: the model weights and inference engine run entirely on servers you own or control, and no text — whether a query, a document, or a conversation — is ever transmitted to an external service.
Flexible sizing to match your infrastructure: choose from 587B, 99B, or 39B parameter configurations to align capability with your available hardware, from large enterprise servers requiring maximum performance to resource-constrained edge environments.
Benchmark-validated quality across 11 disciplines: performance is measured on TUMLU, a rigorous benchmark of 38,139 native Azerbaijani questions covering academic and professional domains, providing an objective and reproducible standard of real-world quality.
Morphology-aware tokenization that preserves meaning: the purpose-built tokenizer handles the ə character and agglutinative word forms correctly, preventing the token fragmentation that degrades comprehension and generation quality in models built for other languages.
Key Technical Features
Native Azerbaijani Training Corpus
The model was trained on 651 million or more curated Azerbaijani words, giving it a deep and authentic grounding in the language. This native corpus ensures the model learns genuine Azerbaijani patterns rather than relying on translated or transliterated data that introduces noise and structural distortions.
Morphology-Aware Tokenizer
A purpose-built tokenizer handles the ə character and the agglutinative structure of Azerbaijani, where meaning is encoded through layered suffixes. This prevents the token fragmentation that degrades output quality in generic models and is a prerequisite for accurate comprehension and generation in the language.
Three Deployment Sizes
Available in 587B, 99B, and 39B parameter configurations, the model can be matched precisely to your infrastructure. The 587B size serves high-demand enterprise workloads, the 99B balances capability with resource use, and the 39B suits environments where hardware capacity or latency constraints are significant.
Fully On-Premise Deployment
The entire model runs within your own network. No queries, documents, or outputs are transmitted to external servers at any point, satisfying strict data-residency, regulatory, and confidentiality requirements without requiring architectural compromises.
TUMLU Benchmark Validation
Model quality is evaluated on TUMLU, a benchmark comprising 38,139 native Azerbaijani questions across 11 academic and professional disciplines. This provides a transparent and reproducible measure of real-world Azerbaijani language performance, independent of evaluations designed for other languages.
How the Model Is Built and Deployed
Frequently Asked Questions
Why does Azerbaijani need its own dedicated language model?
Azerbaijani is an agglutinative language with distinctive characters such as ə and a grammar structure where meaning is built through chains of suffixes. General-purpose models are predominantly trained on other languages and use tokenizers that fragment Azerbaijani words incorrectly, leading to degraded comprehension and generation quality. A model and tokenizer trained natively on Azerbaijani address these structural issues directly rather than working around them.
What does fully on-premise deployment mean in practice?
It means the model weights and inference engine run on servers you own or control within your own network. No text you submit — whether a query, a document, or a multi-turn conversation — is sent to any external service or processed outside your infrastructure. This is essential for organizations operating under regulatory, legal, or confidentiality obligations that restrict where data may be processed or stored.
How should I choose between the 587B, 99B, and 39B model sizes?
Larger parameter counts generally produce higher-quality outputs on complex or nuanced tasks but require proportionally more compute resources and memory. The 587B model is suited to high-demand enterprise workloads where maximum capability is the priority. The 99B model offers a strong balance between output quality and resource consumption. The 39B model is designed for environments where hardware capacity, latency budgets, or energy constraints are significant factors.
What is the TUMLU benchmark and why does it matter?
TUMLU is a benchmark of 38,139 questions written entirely in native Azerbaijani, spanning 11 academic and professional disciplines. It provides an objective and reproducible way to measure how well a model understands and reasons in Azerbaijani, rather than relying on translated evaluations that were designed for other languages and may not reflect the specific challenges of Azerbaijani text. Validation on TUMLU gives organizations a credible, independent basis for assessing model quality before deployment.
What does 4.6 times more efficient on Azerbaijani text mean in practical terms?
Efficiency here refers to tokenization: because the native tokenizer segments Azerbaijani words correctly, the model represents the same content using significantly fewer tokens than a generic model would require. Fewer tokens per request means the model processes each query faster and consumes less compute per inference, which reduces both latency and infrastructure cost at scale — particularly important for high-volume Azerbaijani-language workloads.
Bring Native Azerbaijani AI to Your Organization
Whether you need a high-capacity enterprise deployment or a resource-efficient on-premise solution, Allmaz offers a model size and configuration built for your requirements. Contact us to discuss how the Azerbaijani-native LLM can be integrated into your infrastructure.
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