3 articles
AI inference refers to the execution of already trained AI models to generate predictions, classifications, or content. Unlike training, model weights are not continuously updated during inference. Instead, AI inference systems must process requests at defined throughput levels and within specified latency limits. For IT architects deploying LLM serving platforms, real-time recommendation engines, or computer […]
MI455X vs. Rubin: Who plans AI training, inference or HPC infrastructure with HBM4 accelerators must weigh different GPU and rack-scale architectures. Both are designed for liquid-cooled rack systems. AMD positions MI455X for AI inference, training and fine-tuning; NVIDIA positions Rubin for training, post-training and inference and for scientific computing. At first glance the published specifications […]
High Bandwidth Memory is one of the central specifications of an accelerator for AI and machine learning workloads, because the model and its working data have to fit into it. JEDEC published the HBM4 standard in April 2025, and the first commercial HBM4 shipments were announced in February 2026. This article explains what HBM4 changes […]
