Tag: #gpu
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 69 posts
GPU 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#nvidiaMulti-GPU, Multi-Node Training Platforms: The Complete Map — from the Framework Ecosystem to Hands-On Slurm and Kubeflow Guides
A one-page map of the full landscape of training models across multiple GPUs and multiple nodes. The AI library and framework ecosystem map (PyTorch, JAX, HuggingFace, DeepSpeed, Ray), when to pick which parallelization
2026-07-09 · 8 min read #ai#ml#distributed-training#slurm#kubeflowMastering 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-hierarchyCustom 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#nvidiaMaking Inference Fast — Quantization, Sparsity, and Dataflow from a Hardware Lens
We break down the cost structure of inference through the memory-wall lens, then connect quantization (INT8/FP8/FP4), structured sparsity (2:4), dataflow architectures, operator fusion, batching and KV caching into one p
2026-06-16 · 18 min read #inference#quantization#sparsity#dataflow#gpuGPU vs TPU vs ASIC — The 2026 Inference War
A comparison of the GPU, TPU, and ASIC competition over 2026 inference workloads. We cover Google TPU v6 Trillium and Ironwood, the fast-growing cloud in-house inference ASICs, the throughput/latency/cost/power trade-off
2026-06-16 · 23 min read #gpu#tpu#asic#inference#ai-hardwareThe 2026 AI Accelerator Landscape — From Blackwell to Vera Rubin
A developer-oriented map of the 2026 AI accelerator market. We cover NVIDIA Blackwell and the next-generation Vera Rubin, AMD MI350X, the moment inference capex first overtook training, and how to choose a chip per workl
2026-06-16 · 21 min read #ai-hardware#nvidia-blackwell#vera-rubin#inference#gpuLocal LLM Inference Optimization — From Quantization to Breaking the VRAM Ceiling
Privacy concerns, cost pressure, and big-tech fatigue are driving a local LLM revival. We map the entire landscape of local inference optimization: VRAM-first hardware thinking, GGUF and AWQ quantization, llama.cpp vs vL
2026-06-12 · 16 min read #llm#inference#quantization#llama-cpp#vllmHacking the Linux Memory Hierarchy — swap, zram, and the Wild Idea of Swapping to VRAM
The nbd-vram project, which exposes GPU VRAM as an NBD block device to use as swap, made waves on Hacker News. Starting from there, this post covers Linux memory management fundamentals, swappiness and zram/zswap tuning,
2026-06-12 · 16 min read #linux#memory#swap#zram#kernelIs AI Really Slowing Down — Reading the 2026 AI Bubble Debate Through Datacenter Economics
A breakdown of the AI is slowing down debate that dominated the first half of 2026, viewed through datacenter economics. We cover the claim that 2 trillion dollars of annual revenue is needed by 2030, the arithmetic of 1
2026-06-12 · 20 min read #ai#economics#datacenter#gpu#infrastructureAI Hardware Accelerators 2026 — NVIDIA Blackwell / AMD Instinct MI400 / Google TPU Trillium / Cerebras WSE-3 / Groq LPU / Tenstorrent / Etched Sohu / Furiosa / Rebellions Deep Dive
In 2026 AI hardware is no longer an NVIDIA-only story. Blackwell (B100/B200/GB200 NVL72/B300) landed at GTC 2024, Rubin is queued for September 2026, AMD Instinct has marched from MI300X through MI355X to MI400 Helios, I
2026-05-16 · 20 min read #ai-hardware#gpu#accelerator#nvidia-blackwell#b100A Modern Understanding of the Operating System — io_uring, cgroups/namespaces, eBPF, NUMA, GPU UVM, EEVDF, Zero-Copy Complete Guide (2025)
iouring as the successor to epoll, the cgroups + namespaces that made Docker, the eBPF that injects code into the kernel safely, the hidden cost NUMA imposes, GPU drivers and UVM, the EEVDF scheduler that landed in Linux
2026-04-15 · 13 min read #operating-systems#linux#io-uring#cgroups#namespacesCUDA 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-learningModern Computer Architecture — CPU Pipelines, Out-of-Order, Caches, Branch Prediction, Meltdown, Apple Silicon, ARM, RISC-V, SIMD, GPU Deep Dive (2025)
Why arrays beat linked lists, why a branch can be 10x slower, how M1 beat Intel. CPU pipelines, Out-of-Order, L1 to L3 caches, branch prediction, Meltdown/Spectre, Apple Silicon, ARM/x86/RISC-V, SIMD, GPU SM/Warp, HBM/CX
2026-04-15 · 15 min read #computer-architecture#cpu#cache#branch-prediction#apple-silicon2026 AI & Tech Trends -- Agentic AI, GPUaaS, Sovereign AI, and Quantum Computing
A comprehensive overview of the defining tech trends of 2026: Agentic AI, GPU as a Service, Sovereign AI, quantum-safe cryptography, AI chip competition, and hiring trends.
2026-04-11 · 13 min read #ai#trends#ai-agent#gpu#quantumSemiconductor Deep Dive -- Complete Guide to CPU, GPU, RAM, ASIC, and CUDA Architecture
How does a CPU execute instructions, how does RAM store data, and why are GPU/CUDA essential for AI? A deep dive from semiconductor fundamentals to ASIC custom chips.
2026-04-10 · 22 min read #ai#semiconductor#cpu#gpu#cudaWEKA High-Performance Storage Complete Guide 2025: Parallel File System for AI/HPC Workloads
Everything about WEKA! Parallel file system architecture, NVMe tiering, GPU Direct Storage, AI/ML workload optimization, cloud integration (AWS/Azure/GCP), vs Ceph/Lustre/GPFS, data pipelines, performance benchmarks.
2026-03-25 · 22 min read #weka#wekafs#storage#parallel-filesystem#ai-infrastructureGPU Software Engineer Complete Guide: From CUDA Architecture to vGPU/MIG, InfiniBand, and K8s GPU Scheduling — System Optimization Mastery
A complete analysis of the LG Uplus GPU Software Engineer JD. From GPU compute architecture, memory hierarchy, CUDA programming, vGPU/MIG virtualization, InfiniBand/RDMA networking, K8s GPU Device Plugin, to large-scale
2026-03-23 · 50 min read #gpu#cuda#system-software#virtualization#vgpuAI Hardware War 2025: NVIDIA Blackwell vs AMD MI350 vs Cerebras WSE-3 vs Google TPU v7
NVIDIA B200 with 208B transistors, Samsung HBM4 first to mass produce, Cerebras 4T transistor wafer, AMD MI350 with 288GB — a complete comparison of every player in the AI chip war by specs, benchmarks, and roadmaps.
2026-03-22 · 26 min read #nvidia#samsung#cerebras#amd#intel