cuda

Learn how to turbocharge deep-learning inference on NVIDIA GPUs with TensorRT on Linux: why it matters (FP16/INT8, layer fusion, kernel tuning), clean installs via apt/dnf/zypper or containers, verification steps, converting/benchmarking ONNX with trtexec, minimal Python runtime, high-impact tuning tips, real-world deployment patterns, and quick troubleshooting—giving you a reproducible path from trained model to production-grade engine.

A practical, bash-first blueprint for making a Linux workstation AI-ready: choose balanced hardware (GPU/VRAM, CPU, RAM, NVMe), install base dev tools, set up NVIDIA CUDA or AMD ROCm via distro-specific commands, create isolated Python envs and install PyTorch/TensorFlow/JAX, enable reproducible containers (Docker/Podman + NVIDIA toolkit), validate with llama.cpp and vLLM, and use the troubleshooting tips to maximize performance and longevity.

Hands-on guide to enable fast, private local LLMs with Ollama on Linux: install via apt/dnf/zypper, confirm NVIDIA or AMD/ROCm drivers (nvidia-smi/rocminfo), pull and run models, watch GPU activity, tune gpu_layers and num_ctx to fit VRAM, optionally run in Docker (NVIDIA --gpus all, AMD /dev/kfd,/dev/dri), and apply env vars and troubleshooting to avoid OOM, CPU fallback, and container GPU access issues.

Step-by-step guide to reliably install NVIDIA drivers and the CUDA Toolkit on Linux via apt, dnf, or zypper, with distro-specific repo setup, a quick Ubuntu option, and advice on choosing driver/toolkit sources. Covers prerequisites, Secure Boot/nouveau gotchas, PATH setup, verification with nvidia-smi, nvcc and samples, a PyTorch CUDA test, plus performance, monitoring, and troubleshooting tips for smooth AI workloads.

Guide to choosing and installing NVIDIA CUDA or AMD ROCm for AI on Linux: NVIDIA wins for widest framework support, mature multi‑GPU and tooling; AMD offers strong price/VRAM and open stack if your workload is supported. Covers hardware/VRAM needs, drivers vs toolkits, using wheels/containers, sanity checks, distro‑specific installs, ROCm nuances, and quickstarts—then benchmark your real model.

Practical, distro-agnostic guide to stand up reliable, secure AI-ready Linux hosts. Covers baseline setup on Ubuntu/Debian, Fedora/RHEL, and openSUSE; reproducible Python venvs; optional NVIDIA GPU drivers and container toolkit; rootless containers with Podman; operating tips with systemd, cgroups, htop/nvtop, and firewalls; plus a FastAPI model served as a systemd service and real-world automation advice.