Samsung just pulled back the curtain on breakthrough compression technology that's reshaping how AI runs on devices. The company's Research AI Center has achieved a stunning 5x memory reduction, successfully running 30-billion-parameter models - typically requiring over 16 GB - in less than 3 GB of memory. This isn't just incremental progress; it's the kind of efficiency leap that could bring cloud-level AI performance directly to smartphones and home appliances.
Samsung is quietly rewriting the rules of on-device AI with compression breakthroughs that sound almost too good to be true. The company's Research AI Center has cracked the code on running massive language models locally, achieving what Dr. MyungJoo Ham calls "cloud-level performance directly on the device."
The numbers tell the story. Samsung can now squeeze a 30-billion-parameter generative model - normally over 16 GB in size - into less than 3 GB of memory. That's not just impressive; it's the kind of efficiency gain that could fundamentally change how we think about AI on mobile devices.
"Running a highly advanced model that performs billions of computations directly on a smartphone or laptop would quickly drain the battery, increase heat and slow response times," Dr. Ham explained in an exclusive Samsung Newsroom interview. The solution? Model compression technology that emerged specifically to address these constraints.
The breakthrough centers on quantization - a process that converts complex 32-bit floating-point calculations into much simpler 8-bit or even 4-bit integers. "It's like compressing a high-resolution photo so the file size shrinks but the visual quality remains nearly the same," Dr. Ham said. But here's where Samsung's approach gets clever: instead of applying uniform compression, their algorithms analyze each model weight's importance, preserving critical weights with higher precision while aggressively compressing less important ones.
This selective approach addresses the biggest challenge in model compression - maintaining accuracy while shrinking size. "The goal isn't just to make the model smaller; it's to keep it fast and accurate," Dr. Ham noted. Samsung Research developed specialized algorithms that analyze the model's loss function during compression and retrain it until outputs stay close to the original.
But compression is only half the story. Samsung's AI runtime engine acts as what Dr. Ham calls "the model's engine control unit," automatically distributing operations across multiple processors - CPU, GPU, and NPU - while minimizing memory access. The result? Larger, more sophisticated models can run at the same speed on the same device.