GKE Agent Sandbox and Hypercluster for AI
GKE Agent Sandbox and hypercluster: how Kubernetes becomes a runtime for AI agents and addresses isolation, scale, and latency.
AI
AI on ThecoreGrid focuses on production-grade engineering for machine learning and LLM systems in highload environments.
We cover how to design scalable AI architectures, build reliable data and feature pipelines, and choose infrastructure for training and inference with predictable latency, cost, and resilience. The content is curated from real BigTech practices: incident post-mortems, MLOps and DevOps patterns, observability, security, and governance for AI-powered products. Instead of hype or beginner tutorials, you get deep technical analysis of real-world implementation: LLM integration into existing services, RAG architecture decisions, orchestration strategies, vector databases, caching, CI/CD for ML, and model quality control in production. The AI tag is built for architects, ML engineers, backend/platform teams, and SREs who deploy AI in critical systems and need robust, maintainable, and scalable solutions.
Multitenant GPU isolation without performance loss
Multitenant GPU isolation in AI infrastructure: how to balance performance, security, and utilization across hardware, fabric, and orchestration layers.
AI compute infrastructure Stargate how to scale 10GW
AI compute infrastructure as the foundation for scaling models. An analysis of Stargate, architecture, partnerships, and growth constraints.
KV cache restoration acceleration through 3D parallelism
KV cache restoration in LLM serving: how 3D parallelism reduces TTFT and eliminates bottlenecks in compute and I/O. –>
SFC for split learning reduces latency
How optimizing split learning through SFC reduces latency in distributed AI by jointly managing placement and routing
ThecoreGrid Radar: Trends in distributed systems, blockchain, and network optimization
A selection of architectural insights and releases we read this week Infrastructure 🔹 DataCenterGym: A physics-informed simulator for multi-objective data center scheduling. The tool allows modeling and optimizing resource allocation in data centers, taking into account physical constraints and multiple objectives, significantly improving management efficiency. Read the release 🔹 Spot-and-Scoot: Investigating spot instance availability. A methodology … Read more
ThecoreGrid Trend Report: AI platforms, Zero Trust, and the end of scaling at any cost
6-12 month IT trend analysis: why AI is becoming a runtime platform, security is shifting to Identity-First, and the industry is choosing efficiency
AI agent memory eliminates stateless limitations
AI agent memory as an architectural layer. How persistent memory eliminates stateless limitations and impacts system scalability
AI code review in CI reduces review latency
How AI code review in CI/CD reduces latency and noise through the orchestration of LLM agents and strict filtering of results
AI self-healing networks in telco cloud
AI-driven self-healing networks in telecom: How Telstra automates incident management and reduces recovery time from hours to minutes in cloud infrastructure