Artificial intelligence is fast becoming the defining technology of our time. From powering chatbots and translation apps to helping businesses make faster decisions, AI is everywhere. But there is one catch: the most powerful AI systems today are often too big and heavy to run on everyday devices like smartphones or wearable gadgets.
That is exactly the problem Himanshu Kumar, a data scientist and researcher based in Chicago, set out to solve. His recent study explores how large language models (LLMs), the engines behind conversational AI and generative tools, can be made lighter, faster, and efficient enough to run on ordinary devices.
Why It Matters
Most advanced AI models contain billions of “parameters,” making them massive in size. They usually live on giant cloud servers owned by tech companies. This means users must constantly connect to the internet, raising concerns about cost, privacy, and access.
Himanshu explains the challenge with a simple comparison: “It’s like trying to fit an airplane engine into a scooter. Unless you redesign it, it just won’t work.”
By making these models efficient enough to run on edge devices, like phones, watches, or IoT sensors, AI could become truly universal. Imagine a health wearable that can analyze data instantly without sending private information to the cloud, or a smartphone that offers language translation on the go, even with no network coverage.
The Breakthrough
Himanshu’s research, co-authored with colleagues, shows how three methods can shrink these giant AI systems without making them “dumb.”
Pruning: Cutting out the unnecessary parts of the model, much like trimming a tree. The team showed that this alone could reduce the model size by more than half while keeping accuracy almost intact.
Quantization: Making the math inside the model more compact, like switching from large water pipes to smaller ones without reducing the flow. This reduced memory needs dramatically, making models faster and lighter.
Distillation: Teaching a smaller “student” model to learn from a bigger “teacher” model, so the smaller one performs nearly as well. This cut training time in half while still keeping strong results.
In plain words, Himanshu and his team proved that smart compression can make even the most advanced AI models small enough to run where it matters most, on the devices people already use every day.
What Experts Say
The work has attracted interest from AI specialists. Prof. Song Han of MIT, known for his pioneering work on efficient AI, said that bringing large models to edge devices is “one of the most important challenges in making AI practical.” He noted that approaches like Himanshu’s could “unlock a future where everyone can carry powerful AI in their pocket.”
Prof. Zico Kolter of Carnegie Mellon University, who researches sustainable and efficient machine learning, agreed: “The conversation in AI is shifting from ‘how big can we go?’ to ‘how useful can we make it?’ Studies like this one are a sign that we’re heading in the right direction.”
From Curiosity to Impact
For Himanshu Kumar, the journey began with curiosity. While working in the data science industry, he noticed that companies were racing to build ever-larger models but few were asking how ordinary people could actually use them.
“I want AI to be more inclusive,” he says. “If it only lives on expensive servers, it only helps a few. But if it can run on the devices we already carry, then it helps everyone.”
Colleagues describe him as someone who bridges the gap between research and real-world application. “He doesn’t stop at theory,” one of them notes. “He wants to see technology in people’s hands, making a difference.”
The Road Ahead
The research is only a first step. Challenges remain in balancing efficiency with accuracy, and in tailoring models for different hardware. But Himanshu is already looking ahead. His next focus is on combining efficiency with federated learning, which allows devices to train together without sharing sensitive data.
This could open new doors for privacy-preserving AI in healthcare, education, and finance, fields where data security is as important as intelligence.