On-prem LLM: AI on your own infrastructure
The first large language model built natively for Azerbaijani, deployed entirely on your own infrastructure. Available in 587B, 99B and 39B sizes.
What Is an On-Premise LLM?
A large language model deployed on-premise runs entirely within your own infrastructure — your servers, your network, your control. Unlike cloud-hosted AI services, an on-premise LLM ensures that no query, document, or generated response ever travels outside your organization's perimeter. This architecture is essential for enterprises, government bodies, and public institutions that operate under strict data-residency regulations or handle sensitive information that cannot be exposed to third-party systems. Every inference request is processed locally, meaning your data sovereignty is absolute and your security posture is never compromised by external dependencies.
Key Benefits of On-Premise LLM Deployment
Complete data privacy: every prompt, document, and model response stays within your own network, eliminating any risk of third-party data exposure or interception.
Regulatory and sovereignty compliance: on-premise deployment directly supports strict data-residency requirements, government security mandates, and sector-specific regulations that prohibit data from leaving controlled environments.
Native Azerbaijani language understanding: trained on 651M+ curated Azerbaijani words with a tokenizer purpose-built for agglutinative morphology and the ə character, delivering natural, accurate outputs that generic models cannot match.
4.6× tokenization efficiency: the native tokenizer processes Azerbaijani text far more efficiently than generic multilingual approaches, reducing compute cost and latency for every inference request.
Flexible parameter tiers for any workload: choose the 39B model for focused, resource-efficient applications, the 99B model for balanced enterprise tasks, or the 587B model for the most demanding multi-domain workloads — all within the same deployment architecture.
Transparent, benchmark-validated quality: performance is objectively measured on TUMLU, a rigorous benchmark of 38,139 native Azerbaijani questions across 11 academic and professional disciplines, giving you reproducible evidence of capability rather than marketing claims.
Core Features of the Allmaz On-Premise LLM
Native Azerbaijani Architecture
The model is built from the ground up for Azerbaijani, not adapted from a generic multilingual base. Its tokenizer correctly handles the ə character and the language's agglutinative morphology, producing more accurate and natural outputs across formal, technical, and everyday language registers.
Three Parameter Tiers
Available in 39B, 99B, and 587B parameter configurations, allowing organizations to balance computational cost against task complexity — from lightweight internal tools to the most demanding enterprise workloads — without changing the underlying deployment architecture.
Fully Air-Gapped Deployment
The entire model stack runs on your own hardware. Data processed by the model stays within your network perimeter at all times, supporting the most stringent security, compliance, and data-sovereignty policies your organization requires.
TUMLU Benchmark Validation
Model quality is verified against TUMLU, a rigorous benchmark comprising 38,139 native Azerbaijani questions spanning 11 academic and professional disciplines, providing transparent and reproducible evidence of real-world capability in the Azerbaijani language.
Large Curated Training Corpus
Trained on more than 651 million carefully curated Azerbaijani words, the model achieves broad vocabulary coverage and deep contextual understanding across formal documents, technical content, and everyday communication.
How On-Premise LLM Deployment Works
Frequently Asked Questions
Why does Azerbaijani need its own dedicated LLM?
Azerbaijani is an agglutinative language with distinctive characters such as ə. Generic multilingual models rely on tokenizers that are not optimized for this linguistic structure, resulting in inefficiency and reduced accuracy. The Allmaz model's native tokenizer and 651M+ word training corpus address these gaps directly, achieving 4.6× greater efficiency on Azerbaijani text and producing outputs that are far more natural and contextually accurate.
What does on-premise deployment mean in practice?
On-premise means the model runs on hardware you own or control — in your own data center or private network environment. No prompts, documents, or generated outputs are ever transmitted to an external server. Every part of the inference process happens inside your perimeter, so your data remains fully under your governance at all times.
How do I choose between the 39B, 99B, and 587B parameter models?
The right tier depends on your available hardware resources and the complexity of your workloads. The 39B model is well suited to focused, resource-efficient applications such as document classification or internal search. The 99B model handles broader enterprise tasks with greater nuance. The 587B model is designed for the most demanding multi-domain use cases where maximum accuracy is the priority. The Allmaz team can help you evaluate which configuration best fits your environment and objectives.
What is the TUMLU benchmark and why does it matter?
TUMLU is a benchmark consisting of 38,139 native Azerbaijani questions covering 11 academic and professional disciplines. It provides an objective, reproducible method for measuring how well a model understands and reasons in Azerbaijani — giving you verifiable evidence of quality that is specific to your language, rather than relying on general-purpose benchmarks designed for other languages that may not reflect real-world Azerbaijani performance.
Can the on-premise model integrate with our existing internal systems?
Yes. The on-premise deployment exposes standard APIs that connect to document management platforms, enterprise applications, and custom internal workflows. Because the entire model stack runs inside your network, integration never requires opening external data channels or granting any third party access to your information.
Ready to Deploy Sovereign AI on Your Own Infrastructure?
Contact the Allmaz team to discuss your requirements, identify the right parameter tier for your organization's workloads, and take the first step toward fully sovereign, Azerbaijani-native AI that never leaves your network.
Request a demo