MIG servers

You can install the most powerful GPU on the market into a server, load a large language model (LLM), and still experience severe performance bottlenecks. A dedicated server for AI inference can have a high-end accelerator and still perform poorly because of insufficient VRAM, KV-cache pressure, weak CPU resources, slow storage, or PCIe limitations. A GPU alone does not determine AI inference performance. AI inference is fundamentally a system-level workload. While the GPU is critically important, your CPU, system RAM, NVMe storage, networking, and interconnects must be perfectly balanced around the specific model and workload you are deploying. What Is AI Inference and Why Does Infrastructure Matter? To properly size an AI inference server, you must separate inference from training. AI training is a massive, highly parallel batch process that calculates and adjusts billions of parameters over weeks or months. AI inference—whether it is real-time generative AI, API mo...

A dedicated server can have a powerful CPU, plenty of RAM, and a high-speed network port, yet a DDoS attack can still make the hosted services slow or entirely unreachable. The reason is simple: depending on the attack type and mitigation architecture, malicious traffic can overwhelm network capacity, connection-handling resources, or application resources before legitimate traffic can be processed normally. When discussing DDoS attacks on dedicated servers, the impact can occur at the network, connection, or application level rather than being limited to hardware exhaustion. The resulting traffic flood can contribute to bandwidth saturation, increased network latency, packet loss, resource exhaustion, and reduced service availability. In this guide, we will explore what happens at both the network and server levels during an attack, and explain why appropriate upstream protection can help maintain application availability. What Is a DDoS Attack? A DDoS (Distributed Denial-of-Service) ...