Tag: #gpu
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 69 posts
Mastering Slurm: A Practical Guide to the HPC/AI Cluster Workload Manager
A comprehensive, hands-on guide to the Slurm workload manager. Covers architecture (slurmctld/slurmd/slurmdbd), core concepts (Partitions/QoS/Fairshare), essential commands (sbatch/srun/salloc), GPU scheduling (GRES/MIG/
2026-03-01 · 15 min read #slurm#hpc#gpu#distributed-training#clusterCUDA Hands-on Complete Guide: Everything About GPU Computing
A comprehensive guide covering NVIDIA CUDA from fundamental concepts to GPU architecture, C/C++ kernel programming, memory optimization, Python integration (PyTorch, Numba, CuPy), Multi-GPU training, profiling, and troub
2026-03-01 · 51 min read #cuda#gpu#nvidia#deep-learning#machine-learningThe Peak of the AI Memory Supercycle: 5 Decisive Moments for HBM That Will Shake the 2026 Semiconductor Market
From HBM4 16-layer stacking technology, the emergence of custom HBM (cHBM), the strategic clash between SK Hynix, Samsung, and Micron, to the memory supercycle market outlook and HBM vs GDDR comparison — an in-depth anal
2026-03-01 · 37 min read #hbm#semiconductor#memory#ai-hardware#sk-hynixThe New Throne Beyond vLLM: 5 Reasons SGLang Is Changing the LLM Inference Landscape
From SGLang's RadixAttention-based KV cache management and 29% throughput advantage through hyper-specialized design, to its 4,000-line Python zero-overhead scheduler, Prefill-Decode Disaggregation, and Compressed FSM-ba
2026-03-01 · 35 min read #sglang#llm-inference#vllm#radix-attention#model-servingKubernetes GPU Workload Management: The Complete NVIDIA GPU Operator Guide
A comprehensive analysis of how to efficiently manage GPU resources and operate AI workloads in Kubernetes clusters, based on the official NVIDIA GPU Operator documentation.
2026-03-01 · 21 min read #kubernetes#gpu#nvidia#gpu-operator#aiFlashAttention: Optimizing Attention Through GPU Memory Hierarchy
A review of the FlashAttention paper with an in-depth analysis of IO-aware attention optimization leveraging the GPU HBM/SRAM memory hierarchy.
2026-03-01 · 18 min read #ai-papers#flash-attention#gpu#optimization#transformerCUDA Programming Fundamentals: Complete Guide to GPU Parallel Computing
Systematically analyze the core concepts of CUDA programming including thread hierarchy, memory model, and kernel development based on NVIDIA official documentation.
2026-03-01 · 24 min read #gpu#cuda#parallel-computing#nvidiaThe Complete Guide to vLLM & Ollama: LLM Serving Engine Setup, Parameters, and Environment Variables
A comprehensive deep dive comparing vLLM PagedAttention architecture and Ollama local LLM runtime environments. Covers everything from installation, server startup, API calls, key CLI arguments, sampling parameters, envi
2026-03-01 · 46 min read #vllm#ollama#model-serving#inference#gpuComplete Guide to GPU Memory Optimization and Mixed Precision Training
Analyze GPU memory components based on NVIDIA official documentation and cover memory optimization techniques including Mixed Precision Training and Gradient Checkpointing.
2026-03-01 · 22 min read #gpu#cuda#mixed-precision#optimization#deep-learning