Agent Access Model Tightens Control Over Actions
The Agent Access Model transforms access control for AI agents, mitigating risks through task-scoped tokens and the verification of every action
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.
ThecoreGrid Radar: This Week’s Trends in High-Performance Computing and Architecture
A curated selection of architecture insights and research releases we explored this week. Infrastructure 🔹 Spanergy: Energy-Aware Distributed Tracing for MicroservicesIntroduces an energy-efficient approach to microservice monitoring that reduces the power overhead of distributed tracing, making it particularly relevant for cloud-native architectures.Read the paper (EN) 🔹 ProFlow: RL-Based Proactive Flow PlacementApplies reinforcement learning to optimize … Read more
LLM serving platform vLLM and Triton in production
LLM serving platform based on vLLM and Triton: architecture, trade-offs, and bottlenecks in production when scaling inference
Orbital AI Data Centers Hit Network Limits
Orbital AI data centers promise cheap energy and cooling, but network bandwidth, latency, and topology may limit large-scale LLM training in space
LLM-MAS protection against dual drift attacks
Securing LLM-MAS via continual learning and OOD detection: An analysis of OpenEvoShield and its architecture against dynamic attacks
MoE routing acceleration without dynamic networking
MoE routing in direct topologies is limited by unpredictable traffic. An analysis of MoX shows how static routing reduces congestion and approaches switch performance. In classic ML clusters, the network is designed for regular collective operations. This works as long as the traffic is predictable. MoE routing breaks this assumption. Each token selects top-K experts, … Read more
LLM Serving Workload Analysis FineServe Without Illusions
LLM serving workload turns out to be significantly more complex than is typically modeled. FineServe demonstrates how real workloads break simplified assumptions and impact architecture. Modern LLM platforms operate as always-on services with strict requirements for latency and throughput. The main issue is the unstable and heterogeneous LLM serving workload. Most studies have relied on … Read more
ThecoreGrid Radar: Three Key Technology Trends of the Week
A weekly roundup of the architecture insights and releases we’ve been reading. Infrastructure 🔹 FSZ: Breaking the Prediction-Throughput Trade-off in GPU Lossy Compression A new approach to lossy GPU compression increases throughput while preserving prediction quality. The work shows how compression can become a tool for optimizing compute infrastructure, rather than simply a way to … Read more
Primitive-level synchronization accelerates PBNR
Primitive-level synchronization in distributed PBNR training: how to eliminate global barriers and accelerate training without sacrificing quality
Mixture-of-Experts acceleration through overlap
Compute-communication overlap in MoE reduces latency through tiled scheduling and signaling. This directly impacts throughput and GPU utilization. Modern Mixture-of-Experts (MoE) systems are limited not by compute, but by communication. In distributed execution, each layer requires two all-to-all operations, and the second—returning results—falls into the critical path. The classical scheme only initiates it after the … Read more