The article walks through a step‑by‑step optimization of a simple elementwise addition kernel (C = A + B) on HyperAI's RTX 5090 cloud instance, covering FP32 baseline, vectorized FP32, several FP16 variants, benchmark methodology, performance results, and the reasoning behind thread‑block sizing.
This article explains how to estimate the GPU memory required for running large language models of 3 B, 7 B, and 13 B parameters, walks through step‑by‑step calculations, shows how hardware limits affect feasibility, and offers practical optimization techniques such as quantization and CPU offloading.
This article explains the fundamental performance indicators of AI chips—TOPS, TFLOPS, and precision formats like FP16, FP32, and INT8—while comparing GPU, ASIC, and TPU architectures, highlighting Tensor Core advantages and TPU's superior efficiency over CPUs and GPUs.
This article explains the essential metrics of AI chips—including TOPS and TFLOPS compute, precision formats like FP16, FP32 and INT8, and the roles of GPUs, ASICs and TPUs—while highlighting how Tensor Cores boost deep‑learning performance and comparing TPU efficiency to CPUs and GPUs.
