Welcome

Welcome to Engineering the Singularity, Efficiently

Conventional wisdom suggests capability requires scale. The prevailing logic? Throw more parameters, more compute, and more data at the problem, and intelligence will follow. For years, this held true.

But a shift is occurring at the other end of the spectrum. Models in the 1-10B parameter range are now executing tasks that seemed to demand 100B+ parameters just two years ago. Advances in quantization-aware training, efficient attention mechanisms, and distillation are proving to be more than just cost optimizations. They reveal that we have been brute-forcing problems that have elegant, efficient solutions.

This blog chronicles that frontier: the techniques, papers, and experiments pushing the limits of small models. I will be focusing on:

• Training efficiency: Extracting more capability per FLOP through improved architectures, curricula, and objectives.

• Inference optimization: Utilizing KV-cache strategies, speculative decoding, and quantization that preserves fidelity.

• Edge deployment: Exploring what is actually achievable on consumer hardware and mobile devices.

• Scaling laws for the rest of us: Delineating when smaller models are sufficient—and when they are not.

Not every problem requires a trillion-parameter model behind an API call. Let’s figure out what does, and what doesn’t.