Deploy a private Azerbaijani LLM
The first large language model built natively for Azerbaijani, deployed entirely on your own infrastructure. Available in 587B, 99B and 39B sizes.
Deploy a Private Azerbaijani LLM on Your Own Infrastructure
Organizations that process sensitive information in Azerbaijani face a fundamental challenge: most large language models were never designed with the language in mind, forcing teams to rely on general-purpose models that mishandle Azerbaijani morphology, fragment tokens incorrectly, and route confidential text through external servers. Allmaz addresses this directly with the first large language model built natively for Azerbaijani, trained on more than 651 million curated Azerbaijani words and validated on the TUMLU benchmark — 38,139 native questions spanning 11 academic and professional disciplines. The result is a model that understands real-world Azerbaijani text with the depth and accuracy that purpose-built training provides, available in 587B, 99B, and 39B parameter sizes to match a wide range of hardware environments.
Why Teams Choose a Native Azerbaijani LLM
Complete data sovereignty: all inference runs inside your own infrastructure, so sensitive Azerbaijani text never leaves your network and compliance obligations are met by design.
Deep native language understanding: training on 651M+ curated Azerbaijani words gives the model broad coverage of vocabulary, grammar, and domain-specific usage that general-purpose models cannot replicate.
4.6× inference efficiency on Azerbaijani text: the purpose-built architecture processes Azerbaijani significantly faster and at lower resource cost than adapting a model trained primarily on other languages.
Flexible deployment scale: the 39B variant suits resource-constrained servers, the 99B model balances capability with cost, and the 587B model handles the most demanding language tasks — all on your own hardware.
Rigorous, benchmark-verified quality: performance is measured on the TUMLU benchmark, covering 38,139 native Azerbaijani questions across 11 disciplines, providing an objective and comprehensive quality standard.
Accurate agglutinative morphology handling: the native tokenizer is engineered specifically for Azerbaijani, correctly representing the ə character and complex multi-suffix word forms that general-purpose tokenizers routinely fragment and misinterpret.
Key Capabilities of the Allmaz Azerbaijani LLM
Fully On-Premise Deployment
The model runs entirely within your own infrastructure. No API calls reach external servers, no data is shared with outside services, and no dependency on third-party cloud providers is introduced at any stage of inference.
Native Azerbaijani Tokenizer
A purpose-built tokenizer handles the ə character and the agglutinative morphology of Azerbaijani, producing accurate token representations that correctly capture multi-suffix word forms which general-purpose tokenizers consistently fragment and misrepresent.
Three Parameter Size Options
Select from 587B, 99B, or 39B parameter configurations to balance response quality, throughput, and hardware requirements. Each size is a fully capable Azerbaijani-native model, allowing you to right-size the deployment for your environment.
Trained on Curated Azerbaijani Data
The model was trained on more than 651 million curated Azerbaijani words, providing deep coverage of vocabulary, grammar, idiomatic usage, and domain-specific language across a wide range of real-world contexts.
TUMLU Benchmark Validation
Quality is assessed using the TUMLU benchmark, which contains 38,139 native Azerbaijani questions spanning 11 academic and professional disciplines, offering a rigorous and transparent measure of language understanding and reasoning.
Efficient Inference on Azerbaijani Text
At 4.6× greater efficiency on Azerbaijani text, the model delivers faster responses and lower resource consumption than a general-purpose model adapted for the language, making production deployment more cost-effective at any scale.
How to Get Started
Frequently Asked Questions
Does any of our data leave our network during inference?
No. The model is deployed entirely on your own infrastructure and all inference is processed locally. No data is transmitted to Allmaz or any external service at any point during operation.
Which parameter size is right for our organization?
The best choice depends on your available hardware, required response latency, and the complexity of your language tasks. The 39B model is well suited to resource-constrained environments, the 99B model offers a strong balance between capability and infrastructure cost, and the 587B model is designed for the most demanding Azerbaijani language tasks. The Allmaz team can walk you through the trade-offs based on your specific setup.
How was the model's language quality verified?
The model was evaluated on the TUMLU benchmark, a purpose-built assessment consisting of 38,139 native Azerbaijani questions covering 11 academic and professional disciplines. This provides a broad, rigorous, and transparent measure of real-world language understanding rather than relying on proxy metrics from other languages.
Why does Azerbaijani need its own tokenizer rather than a general-purpose one?
Azerbaijani is an agglutinative language, meaning grammatical information is encoded through chains of suffixes attached to a root word. It also uses characters such as ə that are absent from most standard vocabularies. General-purpose tokenizers split these structures incorrectly, producing fragmented representations that degrade model accuracy and waste compute. The native tokenizer was designed from the ground up for Azerbaijani morphology, ensuring that words are represented as the model was trained to understand them.
Can the model be fine-tuned on our own domain-specific data?
Fine-tuning options are available for organizations that need the model adapted to specialized terminology or workflows. Contact the Allmaz team to discuss your domain requirements and the most appropriate approach for your use case.
Ready to Deploy a Private Azerbaijani LLM?
Contact the Allmaz team to discuss your infrastructure requirements, select the parameter size that fits your environment, and begin a fully on-premise deployment that keeps your Azerbaijani-language data completely under your control.
Request a demo