Tag: #2026-03
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 765 posts
Engineering Math Series 15: Vectors, Geometry, and Vector Fields
Understand vectors not as mere lists of coordinates but as geometric objects with direction and magnitude, and introduce what vector fields mean in engineering at the introductory level.
2026-03-19 · 3 min read #engineering-math#vector-calculus#vectors#vector-fields#2026-03Engineering Math Series 12: Elimination, Inverse Matrices, and Determinants
Organizes the three core perspectives for solving linear systems -- Gaussian elimination, inverse matrices, and determinants -- at the beginner level.
2026-03-19 · 3 min read #engineering-math#linear-algebra#determinants#matrix-inverse#2026-03Engineering Math Series 9: Intuition Behind the Laplace Transform
Explains intuitively why the Laplace transform turns differential equations into algebraic problems, and the difference between the time domain and the s-domain.
2026-03-19 · 3 min read #engineering-math#laplace-transform#ode#systems#2026-03Engineering Math Series 6: Higher-Order ODEs and Initial Value Problems
Covers the basic structure of third-order and higher ODEs and the meaning of initial value problems, explaining why the number of conditions is tied to the order.
2026-03-19 · 3 min read #engineering-math#ode#higher-order-ode#initial-value-problem#2026-03Engineering Math Series 3: Separation of Variables, Integrating Factor, Exact Equations
Covers the three essential solution methods for first-order ODEs -- separation of variables, integrating factor, and exact equations -- with intuition and worked examples.
2026-03-19 · 4 min read #engineering-math#ode#first-order-ode#integrating-factor#2026-03MLOps Complete Guide: From ML Pipeline to Production Deployment
The complete guide to MLOps. Master ML pipeline design, experiment tracking (MLflow, W&B), model registry, CI/CD, model serving, and monitoring with real-world examples.
2026-03-17 · 22 min read #mlops#ml-pipeline#kubeflow#mlflow#wandbAI Model Serving and Inference Optimization Complete Guide: vLLM, TensorRT, Triton, Ollama
The complete guide to efficiently serving AI models in production. Master vLLM, TensorRT, NVIDIA Triton Inference Server, Ollama, quantization (INT8/INT4), batch processing, and latency optimization with real-world examp
2026-03-17 · 19 min read #mlops#model-serving#vllm#tensorrt#tritonComplete Guide to AI Model Deployment & Serving: Triton, vLLM, BentoML, and Kubernetes
A production-ready guide covering Docker GPU containers, Kubernetes HPA, NVIDIA Triton, vLLM LLM serving, BentoML, and Ray Serve for deploying AI models at scale.
2026-03-17 · 12 min read #modelserving#triton#vllm#bentoml#kubernetesFreeBSD Jails, ZFS, and Practical Server Operations
A practical guide to running FreeBSD in production, covering why teams still choose it, how jails differ from containers, how ZFS changes operations, and which runbooks matter most for long-lived servers.
2026-03-17 · 4 min read #freebsd#zfs#jails#server-operations#linuxPrometheus in Production: TSDB, Cardinality, Recording Rules, Federation, and Remote Write
A practical Prometheus operations guide covering TSDB retention, high-cardinality control, recording and alerting rule design, and when to choose federation versus remote write.
2026-03-17 · 5 min read #prometheus#observability#tsdb#cardinality#recording-rulesComplete Guide to Open Source LLMs: Llama 3, Mistral, DeepSeek, Qwen, and Gemma
A comprehensive overview of the open source LLM landscape covering Llama 3, Mistral, DeepSeek, Qwen, and Gemma.
2026-03-17 · 14 min read #llm#llama#mistral#deepseek#qwenComplete Guide to LLM Evaluation and Benchmarking: MMLU, MT-Bench, RAGAS, LM-Eval
A complete guide to evaluating LLMs correctly. Covers standard benchmarks such as MMLU, MT-Bench, and HumanEval, evaluating RAG systems with RAGAS, practical use of LM-Evaluation-Harness, and building production LLM eval
2026-03-17 · 17 min read #llm#evaluation#benchmark#ragas#lm-evalBuilding LLM from Scratch: Complete Guide to Understanding GPT through Code
A complete guide to building and understanding Large Language Models from scratch. Implement a full GPT architecture in PyTorch — from tokenizer to Transformer, pretraining, and fine-tuning.
2026-03-17 · 21 min read #llm#gpt#transformer#from-scratch#deep-learningLLM Fine-tuning Complete Guide: Master LoRA, QLoRA, RLHF, and DPO
A complete guide covering every LLM fine-tuning technique. From Full Fine-tuning to LoRA, QLoRA, RLHF, DPO, and Instruction Tuning — master them all with HuggingFace PEFT and hands-on examples.
2026-03-17 · 21 min read #llm#fine-tuning#lora#qlora#rlhfComplete Guide to Korean NLP and LLM: Mastering KoBERT, KLUE, HyperCLOVA, and EXAONE
A comprehensive guide to Korean NLP and LLM covering KoBERT, KLUE, HyperCLOVA, and EXAONE.
2026-03-17 · 27 min read #korean-nlp#kobert#klue#hyperclova#exaoneLLM Pretraining & Scaling Laws: From Chinchilla to Flash Attention and MoE
A complete guide to LLM pretraining: Chinchilla scaling laws, Common Crawl data pipelines, Flash Attention 2, GQA, MoE architectures, and the latest pretraining recipes from DeepSeek-V3, Llama 3.1, and phi-4.
2026-03-17 · 14 min read #llmpretraining#scalinglaws#chinchilla#flash-attention#mixtralmoeGPU 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-cudaAI Hardware Accelerators Complete Guide: H100, TPU, Cerebras, and Edge AI Chips Compared
A comprehensive comparison guide covering NVIDIA H100 Tensor Core, Google TPU v5 systolic array, Cerebras WSE-3, AWS Inferentia 2, and Apple Neural Engine for AI hardware accelerators.
2026-03-17 · 17 min read #ai-hardware#h100#tpu#cerebras#edge-aiGPU Memory Management & LLM Inference Optimization: vLLM, PagedAttention, GPTQ, TensorRT-LLM
A complete guide to LLM inference optimization covering HBM memory hierarchy, KV cache calculations, PagedAttention, GPTQ/AWQ quantization, continuous batching, and vLLM vs TensorRT-LLM comparison.
2026-03-17 · 16 min read #gpumemory#llminference#vllm#paged-attention#gptqAdvanced CUDA GPU Programming: Warp Optimization, Tensor Cores, and Triton Kernels
A comprehensive deep dive into CUDA memory hierarchy, Warp optimization, Tensor Core WMMA API, Flash Attention implementation, and Triton custom kernel authoring for accelerating AI model training.
2026-03-17 · 19 min read #cuda#gpuprogramming#tensorcore#triton#flash-attention