How Falcon LLM is reshaping accessibility through model efficiency

As AI capabilities continue to grow in complexity and influence, a central question is emerging: how do we ensure that access to these tools keeps pace with their development? At the 2025 AI for Good Global Summit, Hakim Hacid , Chief Researcher at the Technology Innovation Institute (TII) in Abu Dhabi, presented a compelling response to this challenge, bringing the power of AI to the mass by building models that are smaller, more efficient, and openly accessible.

In his session, Hacid outlined the journey of Falcon LLM, an open-source language model developed at TII that has rapidly evolved from foundational research into a highly optimized suite of tools supporting text, vision, video, and audio understanding. By prioritizing architectural innovation and resource efficiency, Falcon is demonstrating how cutting-edge AI can operate outside of high-end data centers and move closer to everyday devices and real-world applications.

Rethinking scale and architecture

Large language models (LLMs) have transformed how machines understand and generate human language. But their scale – often tens or hundreds of billions of parameters – creates major barriers to access. The infrastructure required to train and deploy these models can be prohibitively expensive, even for well-resourced organizations.

TII’s approach, according to Hacid, is grounded in rethinking what makes an AI model “powerful.” Instead of increasing model size indefinitely, the team behind Falcon LLM has focused on compressing high performance into smaller models, while maintaining or even exceeding the capabilities of much larger systems.

“We have learnt a lot from the initial generation of our language models. We understood that models shouldn’t be big. Having the infrastructure that would support those models is an extremely big challenge,” Hacid said.

The result is a new generation of Falcon models, such as Falcon 3 and Falcon H1, that demonstrate state-of-the-art performance at a fraction of the computational cost. Falcon H1, for instance, includes models with sizes ranging from 500 million to 34 billion parameters, and yet the 34B model outperforms even 70B parameter models from other leading platforms.

These improvements are due in part to architectural innovations. The latest Falcon models combine traditional transformer architectures with state-space modeling elements inspired by frameworks like Mamba. This hybrid approach allows the model to better capture long-term dependencies and reason across multiple modalities, such as text and vision.

Watch the full session here:

Open source by design

TII is part of the Advanced Technology Research Council (ATRC) in the United Arab Emirates, with a commitment to advance strategic technology sectors. According to Hacid, the UAE is positioning itself as a bridge between global AI powers, especially when it comes to open source.

“There is a sort of polarization of the AI where we see on one hand the US that is working extremely hard to keep the momentum and then we have China that is also growing when it comes to this AI. I think the UAE and Abu Dhabi is trying to be in the middle and serve as a sort of a support for the community when it comes to open source,” he explained.

To date, all generations of Falcon LLM, including the newly released Falcon H1, have been made freely available. This commitment to open access is not symbolic. It is an intentional strategy to ensure that innovation is not locked behind institutional or economic barriers.

In the context of the Global Summit, this approach aligns closely with global efforts to democratize AI capabilities and foster inclusive technological development.

Multi-modal performance, minimal footprint

While Falcon began as a text-focused model, it has since grown into a multi-modal framework capable of understanding not just text, but also images, video, charts, scanned documents, and audio. Hacid showcased several use cases that highlight the practical implications of this expansion.

For example, the Falcon Vision model can perform Optical Character Recognition (OCR) on scanned images, interpret charts, and even reason about content in historical newspapers or complex documents. The system can extract meaningful data from these formats and respond to queries in natural language. In the realm of video, Falcon models understand scene transitions and provide contextual answers based on frame sequences, enabling interactive video querying, all processed locally.

“We’re able to run these models on infrastructure that is ten times smaller than the usual infrastructure that we use. That was a big achievement at the time,” Hacid noted.

This achievement, enabled by architectural refinements, has made it possible to deploy models like Falcon Vision on mobile devices, where they can handle video processing directly and operate offline without network connectivity. This kind of offline inference, where AI models can run on phones, surveillance cameras, or IoT devices, has enormous implications for scalability, privacy, and energy efficiency. It shifts the conversation from centralized, cloud-heavy deployments to more distributed and localized intelligence.

Compressing knowledge, sustaining performance

To enable this level of efficiency, the Falcon team employs techniques such as distillation, supervised fine-tuning (SFT), upscaling, and quantization. These processes allow large models to be compressed and optimized for edge deployment while retaining their reasoning ability.

One particularly innovative technique described during the session is quantized inference, which reduces the precision of the model’s numerical representations without significantly sacrificing performance. This makes it possible to deploy LLMs in environments where computational resources are limited, such as embedded devices or remote sensors.

This philosophy underpins TII’s continued exploration of novel model designs. Rather than pushing toward ever-larger models, the focus has shifted to architectural refinement that enhances interpretability, efficiency, and modularity.

Real-world applications

Several real-world applications of Falcon were highlighted during the session. These include:

• Security: Falcon models are being trained to detect vulnerabilities in software, not just from source code, but from compiled binaries. This capability could support automated threat detection and secure coding practices at scale.
• Tactica: A decision-support system that combines satellite imagery with reasoning capabilities to recommend actions for operators in high-stakes environments. The model integrates multiple data sources and delivers actionable insights in real time.
• Robotics integration: TII is exploring how Falcon models can control robotic agents, pointing to the future convergence of AI and robotics as a new application frontier. According to Hacid, this represents “the next move” in applying AI beyond screens and servers.

Looking ahead

As interest in AI agents grows, Hacid emphasized that LLMs remain foundational. While agents may offer new interfaces and automation workflows, their quality depends entirely on the reasoning capabilities of the underlying models.

TII is now focused on enhancing these reasoning layers. Upcoming versions of Falcon will offer improved tool use, multi-step reasoning, and agentic behavior, enabling them to support more sophisticated decision-making across modalities.

“LLMs didn’t reveal all their secrets yet. We still have a lot to do in building models that are helping us in full capacity,” Hacid said.

This statement serves as both a technical insight and a philosophical orientation. AI’s future, according to TII, lies not in scale for scale’s sake, but in models that are efficient, understandable, and usable by all, whether on a supercomputer or a smartphone.

A commitment to accessibility

By open-sourcing Falcon and investing in infrastructure-efficient AI, TII is offering a viable path toward inclusive access to advanced AI systems. The ability to run high-performing models on consumer hardware lowers barriers for researchers, developers, and communities that may otherwise be excluded from the AI economy.

TII’s approach reflects a broader vision in line with AI for Good: ensuring that the benefits of AI innovation are not confined to those with access to the largest machines or the most data. By focusing on smaller architectures, multi-modal capabilities, and open access, Falcon models represent a step toward more equitable and scalable AI deployment.

As AI systems become increasingly central to how we learn, work, and make decisions, efforts like Falcon LLM underscore the importance of designing models that meet users where they are – on devices, in real time, and across a diverse range of modalities.

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This edition of AI for Good Insider was written and curated by AI for Good Communications and Social Media Officer Celia Pizzuto.