Abu Dhabi’s Technology Innovation Institute (TII) recently announced that Falcon-H1, its next-generation, hybrid-architecture large language model, will be available as an NVIDIA NIM microservice. This announcement coincided with NVIDIA’s GTC Paris conference, positioning Falcon-H1 for seamless enterprise deployment across cloud, on-premise, or hybrid environments.
TII added that developers can soon access and scale the model with production-grade performance, without the engineering overhead typically required to adapt open-source models for real-world applications.
“Falcon-H1’s availability on NVIDIA NIM reflects our ongoing leadership in shaping the future of open, sovereign and cross-domain deployment ready AI. It demonstrates that breakthrough innovation from our region is not only competitive on the global stage – it’s setting new benchmarks for scalable, secure and enterprise-ready AI,” stated Dr. Najwa Aaraj, CEO of TII.
Falcon-H1 outperforms models in its category
The Falcon-H1 has a novel hybrid Transformer–Mamba architecture, combining the efficiency of state space models (SSMs) with the expressiveness of Transformer networks. Designed in-house by TII researchers, the architecture supports context windows of up to 256k tokens, an order-of-magnitude leap in long-context reasoning, while preserving high-speed inference and reduced memory demands.
Multilingual by design, Falcon-H1 delivers robust performance ahead of models in its category, across both high- and low-resource languages, making it suited for global-scale applications.
Supported soon for deployment via the universal LLM NIM microservice, Falcon-H1 becomes a plug-and-play asset for enterprises building agentic systems, retrieval-augmented generation (RAG) workflows, or domain-specific assistants.
Whether running with NVIDIA TensorRT-LLM, vLLM, or SGLang, NIM abstracts away the underlying inference stack, enabling developers to deploy it in minutes using standard tools such as Docker and Hugging Face, with automated hardware optimization and enterprise-grade SLAs.
“Falcon-H1’s availability on NVIDIA NIM bridges the gap between cutting-edge model design and real-world operability. It combines our hybrid architecture with the performance and reliability of NVIDIA microservices. Developers can integrate Falcon-H1, optimized for long-context reasoning, multilingual versatility, and real-world applications”, explained Dr. Hakim Hacid, chief AI researcher at TII.
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Falcon series marks over 55 million downloads to date
The release also marks Falcon-H1’s integration with NVIDIA NeMo microservices and NVIDIA AI Blueprints, giving developers access to full lifecycle tooling, from data curation and guardrailing to continuous evaluation and post-deployment tuning. This makes Falcon-H1 viable in regulated, latency-sensitive and sovereign AI contexts, with full-stack NVIDIA support.
With over 55 million downloads to date, the Falcon series has become one of the most widely adopted open-source models from the Middle East region. Falcon-H1’s smaller variants routinely outperform larger peers on reasoning and mathematical tasks, while the 34B model now leads several industry benchmarks.
TII’s strategic alignment with NVIDIA’s validated deployment framework reflects that open-source models are production-ready assets. Falcon-H1’s availability on NIM cements its place among them as a sovereign, scalable and secure alternative to closed-weight incumbents.
