Ninad Ingale

Thanks for your reply, Karthik. Here is my response to this: When you're trying to decide whether to run your LLM inference on TPUs or GPUs, it really comes down to a few practical trade-offs: how predictable your batches are, your model's shape, and the distinct math behind the prefill and decode phases. If your workload is built around steady, high-volume traffic with fixed batch sizes and uniform sequence lengths, TPUs are absolute powerhouses. Thanks to their structured 2D or 3D mesh interconnects, they can crunch matrices at scale with incredible cost-efficiency for closed ecosystems. On the flip side, if you're dealing with chaotic chat traffic, ragged batching, and dynamic sequence lengths, GPUs give you the adaptability you need. Their flexible NVLink and NVSwitch setups make it much easier to handle custom attention mechanisms and shifting model shapes without hitting performance bottlenecks. While both architectures do great during the heavy lifting of the compute-bound prefill phase, the memory-bandwidth crunch during decode shifts the focus entirely to memory capacity per dollar and your serving stack. Ultimately, predictable, massive-scale production traffic makes TPUs a clear winner for your wallet, while rapid iteration and unpredictable request patterns make the flexibility of GPUs well worth it. Are you currently trying to optimise an inference pipeline for a specific model size, or looking at this from a cost-modelling perspective?

Thank you LUBEGA TOM!

An amazing article Jack Roper on Which Cloud Deployment Model Is Right for Your Business? ... simple, easy to understand and most of very knowledgeable! Must recommend article to read... just enjoy this article!

Thank you Manoj Ingale❤️

Thank you so much Edidiong Asikpo Mam.

Any app would do right? There isn't any theme to make an app ain't it?

Thank you so much Abiola 😃

Thank you so much Andrew Baisden 😃

Thank you so much Edidiong Asikpo😄

Hope I can stand somewhere in the Top 50😄.