What is a large language model (LLM)?
The first large language model built natively for Azerbaijani, deployed entirely on your own infrastructure. Available in 587B, 99B and 39B sizes.
What Is a Large Language Model (LLM)?
A large language model (LLM) is an artificial intelligence system trained on vast quantities of text to understand, generate, and reason about human language. By learning deep statistical patterns across enormous corpora, an LLM can answer complex questions, summarize lengthy documents, translate content, write and review code, and support a wide range of knowledge-intensive workflows. The quality of that capability, however, is tightly coupled to how well the model's training data and tokenizer reflect the target language. Most general-purpose LLMs are built around high-resource languages, leaving languages with distinctive morphological structures — such as Azerbaijani — significantly underserved. When a model lacks a purpose-built vocabulary for a language, it fragments words incorrectly, misses grammatical nuance, and produces outputs that are less accurate and far less efficient.
Why a Native Azerbaijani LLM Matters
Genuine linguistic accuracy: the native tokenizer correctly handles the ə character and the agglutinative suffixes that define Azerbaijani morphology, eliminating the token fragmentation and grammatical errors that degrade output quality in general-purpose models.
Proven processing efficiency: by aligning tokenizer design and training data with Azerbaijani linguistic structure, the model processes Azerbaijani text 4.6× more efficiently than non-native alternatives, directly reducing compute costs and response latency at scale.
Complete data sovereignty: every inference request runs entirely within your own data center or private cloud — no queries, documents, or outputs are ever transmitted to external servers, satisfying strict data-residency and compliance requirements.
Flexible, right-sized deployment: three parameter configurations — 587B, 99B, and 39B — let organizations match model capability to available hardware, budget, and latency targets, with the freedom to scale up or down as needs evolve.
Benchmark-validated quality: performance is measured against TUMLU, a rigorous and reproducible benchmark of 38,139 native Azerbaijani questions spanning 11 academic and professional disciplines, providing transparent evidence of real-world language understanding.
Purpose-built, curated training data: the model was trained exclusively on 651M+ carefully curated Azerbaijani words — not on machine-translated corpora or scraped multilingual datasets — ensuring accurate representation of domain vocabulary, idiomatic expressions, and formal registers.
Key Features of the Allmaz LLM
Native Azerbaijani Tokenizer
The tokenizer is engineered specifically for Azerbaijani, correctly segmenting the ə character and the language's agglutinative word forms. This prevents the token fragmentation that causes quality degradation in general-purpose models when processing Azerbaijani text.
Three Parameter Sizes
Available in 587B, 99B, and 39B parameter configurations, the model family lets organizations match capability to infrastructure. Smaller sizes suit edge or cost-constrained deployments; the largest size targets maximum reasoning depth.
Fully On-Premise Deployment
Every inference request is processed within your own data center or private cloud. No data is transmitted to external servers, making the model suitable for regulated industries and government use cases where data residency is mandatory.
651M+ Curated Training Words
The model was trained on a carefully curated corpus of over 651 million Azerbaijani words, ensuring that domain vocabulary, idiomatic expressions, and formal registers are well represented.
TUMLU Benchmark Validation
Model quality is measured against TUMLU, a benchmark containing 38,139 native Azerbaijani questions across 11 academic and professional disciplines, providing a transparent and reproducible quality signal.
4.6× Azerbaijani Text Efficiency
Because the tokenizer and training data are aligned with Azerbaijani linguistic structure, the model requires significantly fewer tokens to represent the same content compared to non-native models, translating directly into faster responses and lower operational costs.
How the Allmaz LLM Works
Frequently Asked Questions
What makes this LLM different from using a general-purpose multilingual model for Azerbaijani?
General-purpose multilingual models are trained primarily on high-resource languages and typically lack a tokenizer designed for Azerbaijani morphology. This leads to poor handling of agglutinative word forms and the ə character, resulting in fragmented tokens, reduced accuracy, and higher compute overhead. The Allmaz LLM was built natively for Azerbaijani from the ground up — trained on 651M+ curated Azerbaijani words with a purpose-built tokenizer — delivering 4.6× greater efficiency on Azerbaijani text and verified quality on the TUMLU benchmark.
Can the model be used in regulated industries where data cannot leave our premises?
Yes. The model is designed exclusively for on-premise deployment, meaning all inference happens within your own network perimeter. No queries, documents, intermediate states, or outputs are ever transmitted to external servers or third-party services, making it suitable for government agencies, financial institutions, healthcare providers, and any organization subject to strict data-residency or confidentiality obligations.
How do I choose between the 587B, 99B, and 39B parameter sizes?
The right choice depends on your accuracy requirements, available hardware, and acceptable latency. The 587B model delivers the greatest reasoning depth and is best suited to complex, knowledge-intensive tasks. The 39B model is optimized for resource-constrained environments or latency-sensitive applications. The 99B model offers a balanced middle ground for organizations that need strong performance without the infrastructure demands of the largest configuration. All three sizes can be evaluated and switched as your needs change.
What is the TUMLU benchmark and why does it matter?
TUMLU is a standardized evaluation benchmark comprising 38,139 native Azerbaijani questions across 11 academic and professional disciplines. Unlike simple translation quality metrics, TUMLU tests genuine language comprehension, domain knowledge, and reasoning ability in Azerbaijani. Validating the Allmaz LLM against TUMLU provides a transparent, reproducible quality signal that organizations can use to make informed deployment decisions.
What types of organizations benefit most from a native Azerbaijani LLM?
Any organization that processes Azerbaijani-language content and requires linguistic accuracy, data privacy, or both will see meaningful advantages. This includes government agencies handling sensitive citizen data, financial institutions and insurers with compliance obligations, healthcare providers managing confidential records, media and publishing companies producing Azerbaijani content at scale, and enterprises operating in Azerbaijan or serving Azerbaijani-speaking audiences globally. The on-premise deployment model and native language quality make the Allmaz LLM particularly valuable wherever data sovereignty and output reliability are non-negotiable.
Ready to Deploy a Native Azerbaijani LLM?
Explore how Allmaz's on-premise LLM can bring accurate, efficient, and sovereign AI capabilities to your organization. Contact our team to discuss which parameter size fits your infrastructure and use case.
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