AI Inference Network Design for GPU Servers: RDMA, Segmentation, and Zero-Trust at the Host Layer
Discover how to optimize AI inference on GPU servers by addressing network bottlenecks and implementing effective segmentation and security measures.
Workload Fit Engineering: Choosing the Right Hosting Model for Web Apps, Databases, and AI Inference
Most hosting decisions fail for one reason: teams buy infrastructure by label, not by workload behavior. A fast-growing ecommerce site, an AI inference API, and a compliance-heavy ERP platform may all need more server capacity, but each needs a different blend of CPU cores, memory, storage IOPS, network throughput, isolation, and operational control. Executive answer: […]
Sizing GPUs for 70B-Class LLM Inference: Memory, Throughput, Architecture, and Cost
For most 70B-class dense LLMs, the practical GPU choice is determined less by raw compute than by memory headroom for weights, KV cache, and concurrency. A single 80GB GPU can serve a heavily quantized deployment, but BF16 or FP16 inference usually needs multi-GPU tensor parallelism or a larger-memory accelerator. The correct answer depends on quantization, […]
AI Infrastructure Spending Pushes Data Centers Into a New Build Cycle
Cloud providers, chipmakers, and data-center operators are accelerating AI infrastructure upgrades this month across North America, Europe, and Asia as enterprises move generative AI from pilot projects into production. The shift matters because it is reshaping spending on GPUs, networking, cooling, and security at the same time that power availability, supply-chain constraints, and pricing pressure […]