Tag: #nvidia
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 36 posts
A Three-Line Config File Killed Four GPUs for a Week — How containerd Drop-in Merging Really Works
The nodes stopped advertising GPUs. The nvidia runtime was written out cleanly in the config file, but it was nowhere to be found in containerd config dump. The culprit was a three-line registry config I had added a week
2026-08-26 · 12 min read #kubernetes#containerd#gpu#nvidia#troubleshootingMIG and Time-Slicing — Two Ways to Share One GPU
There are broadly two ways to put multiple workloads on one GPU: time-slicing, which divides time, and MIG, which divides hardware. Despite sounding similar, their isolation guarantees are nothing alike. This post works
2026-08-12 · 6 min read #gpu#kubernetes#mig#time-slicing#nvidiaDCGM Exporter — GPU Utilization Is Not What You Think It Is
DCGM Exporter is the standard path for exposing GPU telemetry in Prometheus format, but the utilization-style metric that ends up on nearly every dashboard does not measure what people expect. This post reads the default
2026-08-12 · 9 min read #gpu#kubernetes#dcgm#prometheus#observabilityA GPU Troubleshooting Playbook — Fix the Layers, Then Walk Down
The biggest waste in diagnosing GPU problems on Kubernetes is poking around without an order. A pod that will not schedule, a pod that runs but cannot see the GPU, a driver and toolkit version mismatch, memory exhaustion
2026-08-12 · 7 min read #gpu#kubernetes#troubleshooting#nvidia#gpu-operatorNVIDIA GPU Operator — The Six Pieces You Used to Install by Hand
Running GPUs on Kubernetes used to mean matching six pieces on every node by hand: the driver, the NVIDIA Container Toolkit, the device plugin, DCGM, GPU Feature Discovery, and Node Feature Discovery. The NVIDIA GPU Oper
2026-08-12 · 6 min read #gpu#kubernetes#gpu-operator#nvidia#dcgmDevice Plugins and GPU Scheduling — Where nvidia.com/gpu Comes From
Kubernetes does not know what a GPU is. What makes a node advertise GPUs as a resource is a device plugin registered with the kubelet, and the name that falls out of it is the extended resource nvidia.com/gpu. This post
2026-08-12 · 6 min read #gpu#kubernetes#device-plugin#scheduling#nvidiaAMD vs. NVIDIA: What Actually Differs — Why the Stack Is the Problem, Not the Hardware
Breaks down the difference between AMD and NVIDIA GPUs across four layers — hardware architecture, software stack, porting path, and ecosystem maturity — without taking sides. Covers the mapping from SM to CU and from Te
2026-08-02 · 16 min read #amd#rocm#hip#nvidia#cudaRust 1.97 Cut Off Pre-Volta GPUs — Inside the nvptx64 Baseline Bump
Rust 1.97 (July 9, 2026) raised the minimum requirements for the nvptx64-nvidia-cuda target to PTX ISA 7.0 (CUDA 11 driver or newer) and SM 7.0 (Volta or newer). Maxwell- and Pascal-generation GPUs, and CUDA 10 and older
2026-07-16 · 13 min read #rust#cuda#gpu#compiler#nvidiaWhy KubeVirt GPU Passthrough VMs Could Not Be Scheduled for 112 Days — A Real Cluster Postmortem
After a Rust operator delivered the diagnosis that "all 4 GPU nodes are NotReady," I traced the cause of that death all the way to the end on a real 8-node cluster (GPU Operator v25.3.0, KubeVirt v1.7.0). The real reason
2026-07-11 · 5 min read #kubevirt#gpu#kubernetes#nvidia#devopsGPU Operator × KubeVirt Complete Guide — Components, Configuration, Versions, Partial MIG, and Manual MIG
The two pillars of GPU infrastructure on Kubernetes, all on one page. Covers the GPU Operator operands, ClusterPolicy configuration, and versioning scheme, plus a custom config that applies MIG to only some of the GPUs o
2026-07-09 · 9 min read #kubernetes#gpu#kubevirt#mig#nvidiaMastering the NVIDIA GPU Operator — From Install and Deployment to MIG Partitioning
The era of hand-configuring GPU nodes on Kubernetes is over. How the NVIDIA GPU Operator manages everything from the driver to the device plugin and monitoring via the operator pattern, with Helm install and verification
2026-07-07 · 8 min read #kubernetes#gpu#nvidia#mig#devopsCUDA Architecture Visualized — From Threads to Tensor Cores
A diagram-driven tour of the CUDA execution model and GPU hardware. We walk through the grid-block-warp-thread hierarchy, SM internals, the memory hierarchy, warp scheduling and occupancy, tensor cores, and streams, all
2026-06-27 · 17 min read #cuda#gpu#tensor-core#warp#memory-hierarchyAI Bubble or Revolution: Making Sense of the 2026 Debate
A balanced look at the debate surrounding the 2026 AI stock rally. We weigh concerns over valuations, capex, monetization lag, and circular revenue against the counterargument of real demand and productivity, comparing t
2026-06-18 · 23 min read #ai#investing#bubble#nvidia#valuationThe AI Rally Roller Coaster of June 2026 — Analyzing the Semiconductor Selloff and Rebound
A recap of the sharp selloff and the fast rebound in semiconductor names in early June 2026. Looks in a balanced way at the drivers of the volatility, the bull and bear views, and the principles for handling volatile mar
2026-06-18 · 7 min read #finance#ai-rally#semiconductor#volatility#nvidiaCustom ASIC vs GPU: An Investing Perspective on the Chip War
An investing-focused analysis of the rise of cloud providers in-house chips (custom ASICs) and the shifting inference market. We weigh Nvidia moat and the challenges to it, map the value-chain beneficiaries, and lay out
2026-06-18 · 22 min read #asic#gpu#semiconductor#investing#nvidiaNvidia at 5 Trillion — How Far Along Is the AI Infrastructure Cycle?
Nvidia has crossed a 5 trillion dollar market capitalization for the first time ever. This article uses data to gauge where the AI infrastructure capex cycle stands today, weighing the bull and bear cases, supply-chain b
2026-06-18 · 8 min read #finance#nvidia#ai-infrastructure#capex#semiconductorsThe June 2026 AI Rally Rollercoaster — Anatomy of the Semiconductor Selloff and Rebound
A data-driven walkthrough of the early June 2026 semiconductor selloff and its rapid rebound. We examine the drivers of volatility through valuation, earnings, and interest rates, and weigh the bull and bear cases alongs
2026-06-18 · 10 min read #finance#ai-rally#semiconductors#volatility#nvidiaThe AI Semiconductor Supply Chain and Market — Who Actually Makes the Chips (2026)
We trace the value chain behind a single AI chip — design, EDA, IP, foundry, packaging, HBM, and equipment. From TSMC and Samsung to ASML EUV, the CoWoS bottleneck, geopolitics and export controls, the rise of in-house c
2026-06-16 · 19 min read #gpu-cuda#ai-hardware#semiconductor#supply-chain#tsmcRobotics Development ROS 2 2026 — Nav2 / MoveIt / Gazebo / Isaac Sim / MuJoCo / LeRobot / GR00T Deep Dive
Robotics development in 2026 is no longer the world of ROS 1 and Gazebo Classic. ROS 2 Jazzy / Kilted / Lyrical have settled into a yearly LTS cadence, Nav2 and MoveIt 2 are de facto standards, and Gazebo Harmonic has re
2026-05-15 · 30 min read #robotics#ros2#nav2#moveit#gazeboCUDA GPU Programming Model Deep Dive — SIMT, Memory Hierarchy, Tensor Core, Kernel Optimization (2025)
The engine behind ChatGPT, Stable Diffusion, and Sora — NVIDIA GPUs and CUDA. This post dissects the CUDA programming model from scratch: GPU hardware architecture (SM, Warp, CUDA Core), SIMT vs SIMD, thread hierarchy (G
2026-04-15 · 15 min read #cuda#gpu#nvidia#ai#machine-learning