By ANKUR SAXENA , Investment Director, TDK Ventures As AI moves from model development to widespread deployment, the definition of a good AI chip is changing. Inference runs continuously across millions of devices under tight limits on latency, power, cost, memory and privacy, writes Ankur Saxena of TDK Ventures, and raw compute output is no longer the main benchmark. Saxena lays out five linked factors that will define winning inference architectures: performance beyond TOPS, price measured as cost per useful inference, power, privacy as intelligence moves to the edge, and programmability through compilers and software. He points to Groq, Sagence AI, EdgeCortix and Mixx Technologies as examples of very different approaches to these constraints. The biggest misconception about inference, he argues, is that it will produce another single dominant architecture. Because data centers, interactive applications, industrial systems, robots and defense applications each pose a different optimization problem, the question shifts from whether a chip is faster to which constraints an architecture removes and what that unlocks. Click here to read the full article in Semiconductor Digest magazine. The post The Five Factors Defining the Next Generation of AI Inference Chips appeared first on Semiconductor Digest .