Kubernetes 1.34 introduced Dynamic Resource Allocation (DRA), which enables GPU workloads to express detailed hardware requirements—such as memory thresholds, GPU generation, MIG profiles, and NVLink topology—rather than requesting generic GPU units. Previously, Kubernetes treated all GPUs identically, forcing teams to use node labels, separate node pools, and custom scheduling scripts (like the 200-line Bash script mentioned) to manage heterogeneous clusters of H100s, B200s, and B300s. With DRA, workloads can specify flexible fallback logic ('use a small MIG slice if available, medium if not, or a full GPU if necessary'), eliminating the need to hardcode hardware assumptions into manifests and simplifying cluster management as new GPU generations are added.
The UK raised €18.7 billion across 423 technology deals in H1 2026, with cloud infrastructure and AI companies dominating funding flows. The top ten companies captured €12.3 billion (66% of total), led by Nscale at €3.57 billion for GPU cloud platforms and Pure Data Centres at $2.7 billion for hyperscale data centres. Concentration of capital in AI infrastructure and cloud services reflects enterprise demand for computing capacity to support large-scale AI deployment.
ChangXin Memory Technologies, China's largest memory chipmaker, raised $8.6 billion in a Shanghai IPO, with shares surging 466% on the first day of trading. The company generated 50.8 billion yuan ($7.5 billion) in revenue during the first three months of 2026, a 700% year-over-year increase driven by AI demand. CXMT aims to reduce China's dependence on foreign memory chips while facing supply chain constraints and restrictions on access to advanced chipmaking tools.
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