NVIDIA

Architecting NVIDIA MIG: Virtualizing GPU Resources in Containerized Environments

NVIDIA MIG lets you slice a single GPU into multiple isolated instances, each with dedicated memory and compute. This post covers the architecture, Kubernetes integration, and production patterns for deploying MIG in containerized environments.

Deep Dive into NVIDIA NVLink: Designing High-Bandwidth GPU Topologies for Clusters

A technical guide to NVLink and NVSwitch, covering topology design, bandwidth scaling, and production patterns for GPU clusters.

NVIDIA Triton Inference Server dashboard showing GPU utilization and model metrics

Deep Dive into NVIDIA Triton Inference Server: Scaling Multi‑Model GPU Workloads

NVIDIA Triton Inference Server is a critical tool for scaling AI in production. This deep dive explores its architecture, multi-model concurrency, and deployment patterns.

Diagram of a distributed LLM inference cluster with separate prefill and decode workers linked by a KV transfer bus.

NVIDIA Dynamo: A Practical Guide to the Open-Source Inference Serving Framework

Dynamo disaggregates prefill and decode, distributes KV cache across nodes, and routes traffic intelligently. Here’s what it is, how it works, and when to reach for it in production.

Abstract visualization of a transformer model being compressed into an optimized form.

NVIDIA Model Optimizer: The Compiler-Style Playbook for Production LLM Inference

A deep dive into NVIDIA’s Model Optimizer toolkit and how its compilation-style approach reshapes the production LLM inference stack, from quantization recipes to KV-cache strategies.