Agentic workloads are reshaping AI infrastructure. NVIDIA Dynamo targets KV cache efficiency, vLLM 0.14.0 ships async scheduling, OpenClaw launches SkillSpector, and LiteLLM adds cosign verification. Here is the state of inference security and MLOps.
From session-aware KV cache orchestration to agent-optimized CLIs, the infrastructure layer is racing to support long-running AI agents. NVIDIA Dynamo 1.0 enters production, vLLM and Ollama ship agent-relevant updates, and Hugging Face rebuilds its CLI for machine consumers.
Inference has overtaken training as the dominant AI workload. Here's how enterprises are rethinking infrastructure for cost, latency, and sovereignty in 2026.
The AI revolution is shifting from training to inference. Explore how vLLM, TensorRT-LLM, and MLOps practices are reshaping computing infrastructure for the inference era.
A comprehensive comparison of vLLM, TensorRT-LLM, TGI, and SGLang—the four inference engines dominating AI infrastructure in 2026. Plus the MLOps tools and hardware trends shaping the serving landscape.
The AI infrastructure landscape of 2026: vLLM dominates inference, AMD and TPUs challenge NVIDIA, vector databases mature for RAG, and AI observability becomes essential for production ML systems.
The CNCF introduces ModelPack, an open standard for packaging and managing AI model artifacts in container registries, bridging the gap between ML pipelines and Kubernetes operations.
CNCF argues the AI stack is converging on Kubernetes—data pipelines, training, inference, and long-running agents. Here’s what’s actually driving the migration, the hidden operational tax it removes, and the platform-level standards teams should lock in before the next wave hits.
