> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> OpenTelemetry-native GPU and host metrics collector for NVIDIA, AMD, and Intel GPUs

The **OpenTelemetry GPU Collector** is a lightweight, single-binary metrics collector written in Go. It exports GPU hardware telemetry, host system metrics, and process metrics via OpenTelemetry (OTLP) — with no Python dependencies, no DCGM daemon, and no vendor-specific agents.

It is fully configured via standard OpenTelemetry environment variables and follows the [OTel semantic conventions for hardware metrics](https://opentelemetry.io/docs/specs/semconv/hardware/gpu/).

## Goals

* **OpenTelemetry-native** — uses standard `OTEL_*` env vars, exports via OTLP gRPC or HTTP to any OTel-compatible backend
* **Cross-vendor GPU support** — NVIDIA (NVML), AMD (sysfs/hwmon), and Intel (i915/Xe sysfs) from a single binary
* **OTel semantic conventions** — `hw.gpu.*` metric names, `hw.id` / `hw.name` / `hw.vendor` attributes per spec
* **Zero dependencies** — no DCGM, no Python, no CUDA toolkit needed at runtime for hardware metrics
* **Resilient** — continues exporting host metrics even when no GPUs are present; retries GPU discovery every 30s

## What it collects

<CardGroup cols={3}>
  <Card title="GPU Hardware Telemetry" icon="microchip">
    Utilization, memory, temperature, power draw, energy, clock speeds, ECC errors, PCIe errors — for NVIDIA, AMD, and Intel GPUs
  </Card>

  <Card title="Host System Metrics" icon="server">
    CPU utilization, memory usage, disk I/O, filesystem usage, and network I/O — on Linux, macOS, and Windows
  </Card>

  <Card title="eBPF CUDA Tracing" icon="code">
    Kernel launch counts, grid/block sizes, memory allocations, and memory copies — via uprobes on libcudart.so (opt-in, Linux only)
  </Card>
</CardGroup>

## GPU vendor support

| Vendor     | Backend                                                                                    | Metrics available                                                                           |
| ---------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------- |
| **NVIDIA** | NVML via [go-nvml](https://github.com/NVIDIA/go-nvml) — loads `libnvidia-ml.so` at runtime | Utilization, memory, temperature, power, energy, clocks, ECC errors, PCIe errors, fan speed |
| **AMD**    | sysfs + hwmon — zero external dependencies                                                 | Utilization, memory, temperature, power, energy, fan speed                                  |
| **Intel**  | sysfs + hwmon + DRM (i915/Xe driver)                                                       | Temperature, power draw/limit, cumulative energy, graphics clock, fan speed (kernel 6.16+)  |

## Platform support

| Feature                                     | Linux | macOS | Windows |
| ------------------------------------------- | :---: | :---: | :-----: |
| System metrics (CPU, memory, disk, network) |  Yes  |  Yes  |   Yes   |
| Process metrics (CPU, memory, threads, FDs) |  Yes  |  Yes  |   Yes   |
| GPU metrics (NVIDIA, AMD, Intel)            |  Yes  |   —   |    —    |
| eBPF CUDA tracing                           |  Yes  |   —   |    —    |

## How it works

```
Host Metrics (all platforms via gopsutil)
    +-- CPU utilization, memory, disk I/O, filesystem, network
    +-- Process: self CPU, memory, threads, FDs, Go runtime

GPU Metrics (Linux only)
    +-- PCI Bus Scan (/sys/bus/pci/devices/)
    |     +-- NVIDIA (0x10de) --> NVML backend
    |     +-- AMD    (0x1002) --> sysfs/hwmon backend
    |     +-- Intel  (0x8086) --> sysfs/hwmon + DRM backend
    |
    +-- [Optional: eBPF CUDA tracing via uprobes on libcudart.so]

Export
    +-- OTel SDK --> OTLP gRPC/HTTP --> your OTel collector / backend
```

<Steps>
  <Step title="Scans PCI bus">
    On startup, the collector scans `/sys/bus/pci/devices/` for PCI class codes `0x0300` (VGA), `0x0302` (3D controller), and `0x0380` (display controller), identifying GPU vendor from the PCI vendor ID.
  </Step>

  <Step title="Initialises vendor backend">
    Each detected GPU is handed to its vendor-specific backend. NVIDIA loads `libnvidia-ml.so` via NVML. AMD and Intel read directly from kernel sysfs/hwmon — no additional libraries needed.
  </Step>

  <Step title="Registers OTel instruments">
    Observable gauge and counter instruments are registered with the OTel SDK meter. On each collection tick, the SDK calls back into the collector to read fresh values from each GPU.
  </Step>

  <Step title="Exports via OTLP">
    Metrics are exported via OTLP to any compatible backend — OpenLIT, Grafana, Datadog, New Relic, or a standard OTel Collector.
  </Step>
</Steps>

***

<CardGroup cols={2}>
  <Card title="Quickstart" href="/latest/gpu-collector/quickstart" icon="bolt">
    Get the collector running in under 5 minutes with Docker
  </Card>

  <Card title="Configuration" href="/latest/gpu-collector/configuration" icon="sliders">
    Full reference for all environment variables
  </Card>
</CardGroup>