Tag: #gpu
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 69 posts
Toss Bank ML Engineer (MLOps) Complete Guide: From MLFlow to LLM Platform — Tech Stack Deep Dive
Complete analysis of Toss Bank ML Platform Team MLOps Engineer JD. Deep dive into MLFlow, Airflow, JupyterHub, Kubeflow, Triton Inference Server, ScyllaDB Feature Store, and LLM platform — with 30 interview questions and
2026-03-21 · 38 min read #mlops#ml-platform#tossbank#kubernetes#mlflow[Virtualization] 08. KubeVirt + GPU: Leveraging GPU Acceleration in VMs
Learn how to use GPU passthrough and vGPU with KubeVirt VMs. Covers GPU Operator integration, VFIO setup, Sandbox Device Plugin, and guest OS driver installation.
2026-03-20 · 9 min read #virtualization#kubernetes#gpu[Virtualization] 10. The Future of Virtualization: From Confidential Computing to GPU Disaggregation
A comprehensive look at the future of virtualization technology, covering confidential computing, ARM virtualization, GPU disaggregation, WebAssembly, Kata Containers, and unikernels.
2026-03-20 · 11 min read #virtualization#kubernetes#gpu[Virtualization] 07. NVIDIA GPU Operator: Automating GPU Management on Kubernetes
A detailed guide on using NVIDIA GPU Operator to automatically manage GPU drivers, runtime, device plugins, and monitoring on Kubernetes.
2026-03-20 · 8 min read #virtualization#kubernetes#gpu[Virtualization] 09. Virtualization Platform Comparison: QEMU vs VirtualBox vs VMware vs KubeVirt
A comprehensive comparison of four major virtualization platforms across architecture, GPU support, performance, licensing, and use cases. A guide to choosing the optimal platform.
2026-03-20 · 8 min read #virtualization#kubernetes#gpu[Virtualization] 06. KubeVirt: Running VMs on Kubernetes
Learn how to run virtual machines natively on Kubernetes clusters with KubeVirt. A practical guide covering CRDs, architecture, CDI, networking, and live migration.
2026-03-20 · 8 min read #virtualization#kubernetes#gpuNVIDIA GPU and CUDA Architecture Deep Dive: Why GPUs Dominate AI
From H100 hardware specs to Tensor Core WMMA API. A complete breakdown of the SIMT execution model, shared memory tiling, warp divergence, and why GPUs are purpose-built for AI workloads.
2026-03-18 · 14 min read #cuda#gpu#nvidia#matrix-multiplication#parallel-computingcuDNN Internals: Why Deep Learning Operations Fly on GPU
From im2col transformation to Winograd algorithm, FlashAttention tiling, and TensorRT INT8 quantization. A complete breakdown of how cuDNN makes deep learning operations 10-100x faster than naive CUDA.
2026-03-18 · 13 min read #cudnn#gpu#deep-learning-optimization#convolution#model-servingHow Matrices Fly on GPU: Complete Deep Dive from GEMM to FlashAttention
Matrix multiplication accounts for over 80% of deep learning compute. This post dissects every layer of optimization from naive O(n³) code to cache blocking, cuBLAS, Tensor Cores, and the IO-aware revolution of FlashAtte
2026-03-18 · 15 min read #gemm#matrix-multiplication#flash-attention#gpu#parallel-computingAMD GPU & ROCm Deep Dive: Can It Challenge CUDA for LLM Inference?
A thorough technical analysis of AMD MI300X with 192GB HBM3, the ROCm software stack, and HIP programming model. Includes real LLM serving benchmarks with vLLM and llama.cpp, and an honest assessment of strengths and wea
2026-03-18 · 14 min read #amd#rocm#gpu#mi300x#model-servingGPU Hardware Complete Guide for AI: From Architecture to Selection Criteria
A comprehensive guide to GPU hardware for AI research and training. Covers NVIDIA GPU architectures (Hopper, Blackwell), Tensor Core, NVLink, HBM memory, A100/H100/H200/B200 comparisons, and cloud GPU options in detail.
2026-03-17 · 23 min read #gpu#hardware#nvidia#cuda#gpu-cudaCUDA Programming Complete Guide: GPU Parallel Computing Zero to Hero
A comprehensive guide to mastering CUDA programming from fundamentals to advanced optimization. Covers GPU architecture, kernel writing, memory optimization, mixed precision training, and cuDNN/cuBLAS usage with practica
2026-03-17 · 30 min read #cuda#gpu#gpu-cuda#parallel-computing#nvidiaComputer Architecture Complete Guide: From ISA to GPU Parallel Architecture
A comprehensive guide to computer architecture covering ISA, datapath, pipelining, cache memory, virtual memory, RISC-V, and GPU parallel architecture with code examples.
2026-03-17 · 23 min read #computer-architecture#cpu#gpu#pipeline#cacheAI Development Environment Complete Guide: From GPU Server Setup to Jupyter, VS Code, Docker
A complete guide to AI research and development environments. Step-by-step coverage of CUDA driver installation, virtual environment management, advanced JupyterLab usage, VS Code AI extensions, Docker GPU containers, an
2026-03-17 · 20 min read #development-environment#jupyter#vscode#docker#gpuLLM Inference Serving Framework Comparison: TensorRT-LLM vs vLLM vs SGLang Production Deployment Strategy
A comparison of LLM inference serving frameworks — TensorRT-LLM, vLLM, and SGLang — with production deployment strategies.
2026-03-12 · 21 min read #llm#inference#tensorrt-llm#vllm#sglangNVIDIA Triton Inference Server Production Guide: GPU Model Serving Optimization Strategies
A guide to GPU model serving optimization with NVIDIA Triton Inference Server. Covers Dynamic Batching, Model Ensemble, TensorRT integration, multi-model serving, Kubernetes deployment, performance profiling, and product
2026-03-08 · 29 min read #ai-platform#triton#inference-server#gpu#model-servingKubernetes Dynamic Resource Allocation and GPU Scheduling
A guide to Kubernetes Dynamic Resource Allocation (DRA) and GPU scheduling strategies for AI/ML workloads.
2026-03-06 · 20 min read #kubernetes#dra#gpu#dynamic-resource-allocation#schedulingComplete Guide to Building a Linux GPU Server for Deep Learning
A step-by-step guide to building a Linux GPU server for deep learning development, covering everything from NVIDIA driver installation to Docker GPU environments, based on official NVIDIA documentation.
2026-03-01 · 20 min read #linux#gpu#cuda#nvidia-driver#deep-learningNVIDIA GPU Operator Complete Guide: Components, Installation, and KubeVirt GPU Passthrough
A detailed analysis of NVIDIA GPU Operator architecture and the roles of its 7 core components (Driver, Container Toolkit, Device Plugin, DCGM, MIG Manager, Node Feature Discovery, GFD), covering Helm-based installation,
2026-03-01 · 18 min read #gpu-operator#nvidia#kubernetes#kubevirt#gpuComplete Guide to Multi-GPU Distributed Training: DDP, FSDP, DeepSpeed
Systematically analyze the core components of multi-GPU distributed training including DDP, FSDP, and DeepSpeed ZeRO based on PyTorch official documentation, with practical setup instructions.
2026-03-01 · 21 min read #gpu#cuda#distributed-training#deep-learning#pytorch