Tag: #triton

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• GPU Compiler and Framework Landscape — One Problem, Turning a Graph into a Kernel, a Different Answer at Every Layer ♪ Listenable

  This post puts NVCC and PTX, LLVM, MLIR, Triton, torch.compile, XLA, IREE, and TVM on one map. Different names, different owners, but they all solve the same problem: turning a computation graph into an executable kernel

  2026-08-02 · 21 min read #gpu#compiler#mlir#triton#pytorch

• Three Layers of Writing a Kernel — Comparing CUDA C++, Triton, and CUTLASS on the Same Problem

  Compares what changes when you approach the same GPU kernel by hand in CUDA C++, tile-by-tile in Python with Triton, or assembled from templates in CUTLASS and CuTe. We actually write a row-wise softmax in both CUDA C++

  2026-08-02 · 18 min read #triton#cuda#cutlass#gpu-kernel#compiler

• Triton Gluon: The Language That Hand-Writes the Layouts the Compiler Used to Hide ♪ Listenable

  Gluon is a low-level GPU language built on the same compiler stack as Triton — it hands kernel authors the layouts, shared memory, and warp specialization that Triton normally keeps hidden. Its reason for existing is cle

  2026-07-16 · 19 min read #gpu#triton#kernel#compiler#performance

• Reverse Engineering Tools 2026 — Ghidra / IDA Pro / Binary Ninja / radare2 / Frida / x64dbg / angr Deep Dive ♪ Listenable

  A precise breakdown of the 2026 reverse engineering tool landscape across disassemblers (Ghidra / IDA Pro / Binary Ninja / radare2+Cutter / Hopper), debuggers (x64dbg / OllyDbg / WinDbg / Pwndbg / GEF), dynamic instrumen

  2026-05-16 · 23 min read #reverse-engineering#ghidra#ida-pro#binary-ninja#radare2

• MLOps Complete Guide — Model Serving, Feature Store, Drift, A/B Testing, GPU Economics (Season 2 Ep 7, 2025)

  Training a model and running it in production are completely different games. Serving (TorchServe, Triton, vLLM, TGI), Feature Stores (Feast, Tecton), training infra (Ray, Determined), experiment tracking (MLflow, W&B),

  2026-04-15 · 12 min read #mlops#model-serving#feature-store#drift-detection#ab-testing

• Toss Bank ML Engineer (MLOps) Complete Guide: From MLFlow to LLM Platform — Tech Stack Deep Dive ♪ Listenable

  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

• AI 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#triton

• Complete 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#kubernetes

• Advanced 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

• PyTorch Internals & Advanced Optimization: autograd, torch.compile, FSDP, and Triton

  A complete guide to PyTorch internals: autograd engine, torch.compile() with TorchInductor, FSDP distributed training, gradient checkpointing, and custom CUDA operations.

  2026-03-17 · 10 min read #pytorch#torch-compile#fsdp#triton#mixedprecision

• NVIDIA Triton Inference Server Production Guide: GPU Model Serving Optimization Strategies ♪ Listenable

  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-serving

• Kubernetes ML Model Serving: Complete Analysis of KServe and NVIDIA Triton

  A systematic analysis of ML model serving architecture in Kubernetes environments based on KServe and NVIDIA Triton official documentation.

  2026-03-01 · 19 min read #mlops#kubernetes#model-serving#kserve#triton