LLM Multi-Agent System Holos and Agentic Web Architecture
How the LLM multi-agent system Holos is structured: Agentic Web architecture, agent coordination, economic model, and scaling to millions of agents.
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.
Reverse Address Translation in multi-GPU systems
How Reverse Address Translation affects latency in multi-GPU systems and why TLB misses hinder All-to-All operations in ML workloads.
Slice spraying in GPU clusters without blocking
Slice spraying in GPU clusters: how TENT reduces latency and increases throughput in LLM serving through dynamic data movement –>
Multi-path GPU Traffic Balancing Without Bottlenecks
Multi-path GPU balancing eliminates network bottlenecks in clusters. An analysis of NIMBLE and its impact on throughput and latency. –>
ThecoreGrid Radar: LLM infrastructure moves into disaggregation, networks become programmable, and distributed systems formally verifiable
LLM Infrastructure, Disaggregation, Distributed Systems, GPU Clusters, Network Anomalies, Serverless, AI Agents
LLM evaluation at scale on Apache Spark
LLM evaluation at scale on Apache Spark: how the distributed architecture, caching, and statistical validation of models are structured.
Scaling Uber: Systems, Teams, and AI Engineering
Ex‑Uber CTO Thuan Pham on scaling systems, microservices, platform teams, and using AI to transform software engineering.
MoE Expert Replication Without Excess Memory
How to optimize MoE expert replication: an analysis of CRAFT, load balancing, and throughput growth without overspending GPU memory.
SageMaker AI Accelerates ML Pipeline for Edge Robots
How an ML pipeline based on Amazon SageMaker accelerates training and reduces labeling costs in edge robots and distributed systems
LLM Agents for B2G Co-Simulation of Energy Systems
How LLM agents automate building-grid co-simulation through DAG and multi-agent orchestration, reducing errors and complexity in pipelines.