ai hardware

AI hardware lands on Linux first, but drivers, kernels and runtimes are fragmented. This guide shows why Linux is the hub for GPUs/NPUs and how to prepare with Bash: inventory hardware, set up compilers/Python/containers, install/verify CUDA, ROCm and OpenVINO, enable GPU access in Docker/Podman, monitor/benchmark, and stay current with kernels—so your workloads port smoothly to new accelerators.

Practical, Linux-first guide showing how the right hardware, not just FLOPs, cuts latency, cost, and power. Through three copy-paste case studies (CPU ONNX Runtime with NUMA/thread tuning, single-GPU PyTorch LoRA with 4/8-bit, and portable llama.cpp C++ inference), it gives install commands, monitoring tips (sensors, nvtop), repeatable benchmarks, and advice on topology, quantization, and clean environments.

A practical, Bash-first Linux checklist to quickly validate AI hardware before training: inventory CPU/NUMA/BIOS, confirm GPU drivers and PCIe link width/speed, sanity-check RAM, NVMe health and disk I/O, verify CPU instruction sets and thermals, and test containers (incl. GPU). Includes copy-paste commands, a one-shot snapshot script, what 'good' looks like, and common fixes, preventing slowdowns, driver mismatches, and I/O bottlenecks.

Practical, bash-first guide to reproducible AI hardware benchmarking on Linux: what to measure (throughput, latency, power/efficiency, memory, accuracy), how to normalize runs, install prerequisites across distros, prep systems, run llama.cpp kernel and ONNX Runtime inference tests, log telemetry with nvidia-smi/turbostat/perf, and compare results for real workloads (low-latency, offline, LLM serving), with MLPerf as context and clear next steps.