Demystifying Google TPU SparseCore: Accelerating Recommendation Systems
How Google TPU SparseCore solves embedding lookup bottlenecks in recommender models. Learn the co-designed architecture of Trillium's SparseCores.
Tag: TPU
HBM-Aware Load Balancing with libtpu and GKE
CPU load is a trailing indicator for AI inference. Discover how to use libtpu metrics and the GKE Gateway API to build high-density, memory-aware traffic routing for TPUs.
The Compute-to-Cashflow Gap
The AI industry is shifting from celebrating large compute budgets to hunting for efficiency. Your competitive advantage is no longer your GPU count, but your cost-per-inference.
Not All Zeros Are the Same - Sparsity Explained
Demystifying hardware acceleration and the competing sparsity philosophies of Google TPUs and Nvidia. This post connects novel architectures, like Mixture-of-Experts, to hardware design strategy and its impact on performance, cost, and developer ecosystem trade-offs.
Switching Technologies in AI Accelerators
This post contrasts the switching technologies of NVIDIA and Google's TPUs. Understanding their different approaches is key to matching modern AI workloads, which demand heavy data movement, to the optimal hardware.
Generality vs. Specialization - The Real Difference Between GPUs and TPUs
It's not just about specs. This post breaks down the core trade-off between the GPU's versatile power and the TPU's hyper-efficient, specialized design for AI workloads.