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

  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

  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

  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

  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

  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