The Agentic Infrastructure Stack: What Powers AI’s Autonomous Era
Agentic AI is no longer a research curiosity. It is a production reality, and the infrastructure underneath it is evolving faster than most teams can track.…
Tag: ai infrastructure
The Infrastructure Behind the Intelligence: How AI Inference and MLOps Are Reshaping Computing
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.
The Great Inference Engine Showdown: vLLM vs TensorRT-LLM vs TGI vs SGLang in 2026
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 Agentic AI Infrastructure Shift: From Demos to Production in 2026
The AI landscape is shifting from passive models to autonomous agents. Discover how 2026's infrastructure developments—from Salesforce Headless 360 to SAP's 40+ ERP agents—are making production agentic AI a reality for software developers and enterprises.
The State of AI Infrastructure in 2026: Inference Engines, Hardware Evolution, and Production-Ready Systems
The AI infrastructure landscape has undergone a seismic shift in 2026. From vLLM and TGI to NVIDIA Blackwell B200 and agentic systems, explore the technologies defining production-ready AI at scale.
vLLM’s Rise to Dominance: How PagedAttention Became the Foundation of Modern LLM Inference
How vLLM's PagedAttention innovation, multi-hardware support, and distributed parallelism strategies made it the dominant open-source LLM inference engine in 2026, delivering 2-4x throughput improvements.
llm-d: The Intelligent Inference Scheduler That Fixes What More GPUs Can’t
When adding GPUs doesn't reduce latency, the problem isn't capacity—it's routing. Discover how llm-d's cache-aware scheduling delivers 57x faster TTFT and 2x throughput on the same hardware.
Crossplane 2.0 Makes AI Infrastructure Look More Like a Product API
Crossplane 2.0 matters for AI infrastructure because it gives platform teams a declarative way to expose governed, reusable services to agents and developers through one control plane instead of a maze of tickets, scripts, and cloud consoles.
Cloud Native: CNCF’s new India schedule shows where platform engineering and AI operations are colliding next
The KubeCon + CloudNativeCon India 2026 schedule is less interesting as an event announcement than as a demand signal. AI + ML, observability, operations, platform engineering, and security are showing up together because teams no longer get to treat them as separate tracks in production.
Kubernetes v1.35 as an AI Workload Platform: What Actually Changes for Operators
Kubernetes v1.35 continues a trend: clusters are increasingly asked to run mixed AI workloads (training, batch, and latency-sensitive inference) alongside traditional services. Here’s what’s new that matters for platform teams—especially around scheduling, resizing, and safer config workflows.
LiteLLM + llama.cpp on the Same Day: The Emerging ‘LLM Routing Layer’ for Real Production
Two fast-moving projects shipped updates on Feb 20: LiteLLM (API gateway/router) and llama.cpp (local inference runtime). Together they sketch a practical production pattern: route, observe, and govern LLM calls like any other service.
OpenInfra’s ‘Stewardship’ Moment: Digital Sovereignty, OpenStack, and the AI Infrastructure Stack
OpenInfra is increasingly framing OpenStack and adjacent projects as ‘sovereign infrastructure’ in the AI era. Stewardship—not ownership—may be the governance model that keeps these platforms relevant.
Tiny corp’s training box and the ‘own-your-stack’ moment for AI infrastructure
As LLMs turn into infrastructure, the gap between ‘I can run a model’ and ‘I can train one’ is becoming a product category. tiny corp’s training box pitch is a signal: developers want simpler, more open training stacks—even if the first versions are niche.