Building AI Inference Silicon in the Age of Hyperscale GPUs
Why start a semiconductor company for AI inference now, when GPU supply dominates the conversation? Because the customers who need something different aren't hyperscalers.
Blog
Notes on purpose-built inference.
Architecture, precision, memory, and the economics of running AI in production, written as we build BERNIONE X1.
From Training to Serving: How AI Infrastructure Priorities Shift
Once a model ships, the infrastructure conversation changes completely. What mattered during training rarely matters most once you're serving real traffic.
Private AI and On-Premise LLMs: Rethinking Enterprise Infrastructure
More enterprises want AI running inside their own infrastructure, not just behind someone else's API. That shift changes what accelerator architecture needs to optimize for.
Why Silicon Alone Doesn't Win the Inference Race
The best accelerator in the world underperforms without software that knows how to use it. Why hardware-software co-design, not silicon alone, determines real-world performance.
Tokens per Dollar: Why Inference Economics Beat Peak FLOPS
Peak theoretical performance makes for a good spec sheet. Tokens per dollar is what actually determines whether an AI product is viable to run in production.
Tokens per Watt: The Efficiency Metric AI Infrastructure Can't Ignore
Power isn't a line item you deal with later. In AI inference, it's often the constraint that decides how much capacity you can actually deploy.
What Makes Transformer Inference Different from Training
Training and inference both run on transformers, but they stress compute in almost opposite ways. Conflating the two is how infrastructure decisions go wrong.
INT8 and INT4: How Lower Precision Unlocks Inference Efficiency
Lower-precision math sounds like a compromise until you look at what it actually costs in accuracy versus what it buys back in speed, power, and memory.
The Memory Wall: Why Bandwidth Decides Inference Performance
Compute gets the marketing slides, but in production inference, memory bandwidth is usually the real ceiling. Here's why the memory wall matters more than peak FLOPS.
Why Purpose-Built Beats General-Purpose for AI Inference
General-purpose GPUs were built to do everything reasonably well. AI inference rewards architecture that does one thing exceptionally well. Here's why that distinction matters.