Virtual Tensors Eliminate Data Movement in DNN
Data movement optimization through virtual tensors: how VTC reduces latency and eliminates unnecessary operations in DNN compilation.
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.
FSM Benchmark for Evaluating Network AI Agents
FSM benchmark network configuration: how NetAgentBench reveals failures of LLM agents in dynamic network scenarios and multi-turn behavior.
Edge-cloud multi-agent with decentralization
Edge-cloud multi-agent architecture with decentralized management: how to reduce latency, traffic, and enhance resilience in mobile automation. –>
CPU-free LLM inference without CPU involvement
CPU-free LLM inference: how to remove the CPU from the critical path and stabilize latency in LLM serving architectures.
Agentic systems without context overload
How an agentic system manages the context window through Journal, Review, and Timeline, reducing latency and improving consistency in multi-agent reasoning.
KV cache optimization for multi-LoRA agents
KV cache optimization in multi-LoRA serving: how ForkKV reduces memory consumption and increases throughput of LLM inference.
P2P Model Distribution in Kubernetes Without Bottlenecks
P2P model distribution in Kubernetes with Dragonfly: how to reduce traffic to the origin and accelerate the delivery of large models from Hugging Face and ModelScope.
ThecoreGrid Radar: GPU-first inference, agentic systems as a platform, and the new economics of distributed
LLM Infrastructure, GPU Inference, Agentic Systems, Distributed Systems, High Performance Computing, HPC, Cloud Native, Data Infrastructure
Agent Reliability Score and Platform Contracts
Agent Reliability Score explains how the platform affects the reliability of AI agents and why context control is critical for production systems.
DWDP for LLM Inference Without Inter-GPU Synchronization
How DWDP optimizes LLM inference by eliminating inter-GPU synchronization and increasing throughput in multi-GPU systems.