gpu-host-tuning

You can audit a Linux GPU inference host by running a read-only collection script to surface configuration gaps across CPU power states, NUMA topology, PCIe, NICs, and NVIDIA driver state. This generates a baseline snapshot and structured INDEX.md for review.

The best way to tune NVIDIA GPU deployments is applying a cheat-sheet of BIOS, kernel, driver, and NIC settings specifically for production inference workloads. This follows a phase-driven flow of auditing, benching, and tuning to establish reproducible baselines.

Yes, GPU host tuning applies to both bare-metal and cloud Linux deployments. It supports multiple chassis configurations including Dell XE9680, XE9780, and DGX-like setups, capturing structured probes to validate hardware state across diverse environments.

You measure pinned-host to GPU memcpy ceilings by running bench scripts that establish ground-truth baselines for KV offload paths. This captures actual throughput metrics to validate memory bandwidth and identify performance limitations.

Checking PCIe topology and NUMA affinity is critical because misconfigurations across these subsystems create severe performance bottlenecks. Auditing them validates NVIDIA driver state and ensures CPU, memory, and GPU resources are correctly allocated for inference.

Profiling CPU power states and GPU bandwidth requires Linux tools like turbostat, cpupower, nvidia-smi, dcgmi, nvbandwidth, numactl, and lspci. These dependencies capture hardware telemetry to surface misconfigurations and validate driver state.