Llmops

A heatmap-style illustration of GPU SM utilization across batched transformer layers.

Scaling TensorRT-LLM: Kernel Fusion and Inflight Batching in Production

How NVIDIA’s TensorRT-LLM squeezes maximum throughput from a single GPU — and across a fleet — by fusing kernels, paged KV-cache, and continuously batching requests.

Diagram of an LLM production system showing evaluation, retrieval, guardrails, and observability layers around a central model gateway.

LLMOps and GenAIOps: Production Patterns for Reliable LLM Systems

A working engineer’s guide to LLMOps and GenAIOps: the pipelines, evaluations, guardrails, and observability patterns that turn LLM prototypes into reliable production services.

Abstract illustration of a neural network split across multiple GPU nodes connected by high-bandwidth links.

Distributed Inference: How Modern AI Systems Scale Beyond a Single GPU

How modern AI serving systems split a single model across many accelerators. Covers tensor, pipeline, and expert parallelism, KV-cache sharding, and production patterns from vLLM, TensorRT-LLM, and DeepSeek.

A control-plane diagram with arrows from a single gateway fanning out to multiple AI model providers.

LLM Gateway: One Layer to Manage Multiple AI Models

An LLM gateway centralizes routing, auth, caching, and observability across providers like OpenAI, Anthropic, and self-hosted models. Here’s how it works in production and why most teams end up building one.

The Shift to Agentic RAG: Orchestrating Autonomous Knowledge Retrieval in Production Environments

Table of Contents Introduction RAG 101: Foundations of Retrieval‑Augmented Generation Why Classic RAG Falls Short in Production Enter Agentic RAG: The Next Evolution Core Architecture of an Agentic RAG System 5.1 Retriever Layer 5.2 Planner / Orchestrator 5.3 Executor LLM 5.4 Memory & Knowledge Store Designing Autonomous Retrieval Loops Practical Implementation with LangChain & LlamaIndex Scaling Agentic RAG for Production 8.1 Observability & Monitoring 8.2 Latency & Throughput Strategies 8.3 Cost Management 8.4 Security, Privacy, and Compliance Real‑World Deployments 9.1 Customer‑Support Knowledge Assistant 9.2 Enterprise Document Search 9.3 Financial Data Analysis & Reporting Best Practices, Common Pitfalls, and Mitigation Strategies Future Directions: Towards Self‑Improving Agentic RAG Conclusion Resources Introduction Retrieval‑augmented generation (RAG) has become a cornerstone technique for building LLM‑powered applications that need up‑to‑date, factual information. By coupling a retriever (often a dense vector search over a knowledge base) with a generator (a large language model), developers can produce answers that are both fluent and grounded in external data. ...