2026 Global AI Infrastructure Spending Report: GPU-as-a-Service on the rise
IDC reports 2026 global AI infrastructure spending is projected to reach $280 billion, with GPUaaS as the fastest-growing segment.
Article
8 results shownAI Local Deployment Hardware Guide: GPU Selection from Entry-Level to Production
What hardware do you need for local AI model deployment? This guide covers hardware selection from entry-level consumer GPUs to enterprise-grade server GPUs.
Huawei Cloud launches AI-native Kubernetes cluster with 2x GPU scheduling efficiency
Huawei Cloud launches AI-native CCE cluster optimized for GPU training and inference, reducing job queue time by 50% and boosting GPU utilization above 85%.
Vultr Kubernetes Engine adds GPU node support, lowering AI inference deployment barrier
Vultr Kubernetes Engine (VKE) now supports GPU nodes, letting users deploy NVIDIA GPU worker nodes in VKE clusters with hourly billing.
Tencent Cloud launches Hunyuan AI Supercomputer with 16K H100 GPU cluster
Tencent Cloud unveils Hunyuan AI Supercomputer with 16,000 NVIDIA H100 GPUs for large model training and inference.
Oracle OCI expands AI infrastructure with bare-metal H200 instances
Oracle Cloud Infrastructure launches NVIDIA H200-based bare-metal instances for high-performance AI training and inference.
OVHcloud launches AI GPU bare-metal servers, European cloud joins AI race
OVHcloud launches GPU bare-metal servers with NVIDIA A100 and H100, targeting European AI training and inference needs.
LLM Inference Optimization: vLLM, Quantization, and Batching
After a large model goes live, inference throughput and latency directly drive cost and user experience. This guide explains continuous batching, PagedAttention, KV cache, quantization, and speculative decoding with vLLM and TensorRT-LLM.