Metrics Reference

Complete list of all metrics exported by the OpenTelemetry GPU Collector

All metric names and attributes follow the OpenTelemetry semantic conventions for hardware and system metrics.

GPU Hardware Telemetry

Collected for each detected GPU on Linux. Availability depends on vendor and GPU model.

Metrics

Metric	Type	Unit	Description	NVIDIA	AMD	Intel
hw.gpu.utilization	Gauge	1	GPU compute/encoder/decoder utilization (0.0–1.0)	Yes	Yes	-
hw.gpu.memory.utilization	Gauge	1	Memory controller utilization (0.0–1.0)	Yes	Yes	-
hw.gpu.memory.limit	UpDownCounter	By	Total GPU memory	Yes	Yes	-
hw.gpu.memory.usage	UpDownCounter	By	Used GPU memory	Yes	Yes	-
hw.gpu.memory.free	UpDownCounter	By	Free GPU memory	Yes	Yes	-
hw.gpu.temperature	Gauge	Cel	Die or memory temperature	Yes	Yes	Yes
hw.gpu.fan_speed	Gauge	{rpm}	Fan speed	Yes	Yes	Yes*
hw.gpu.power.draw	Gauge	W	Current power draw	Yes	Yes	Yes
hw.gpu.power.limit	Gauge	W	Power limit/cap	Yes	Yes	Yes
hw.gpu.energy.consumed	Counter	J	Cumulative energy consumed	Yes	Yes	Yes
hw.gpu.clock.graphics	Gauge	MHz	Graphics/SM clock frequency	Yes	Yes	Yes*
hw.gpu.clock.memory	Gauge	MHz	Memory clock frequency	Yes	Yes	-
hw.errors	Counter	{error}	ECC and PCIe error count	Yes	-	-

* Intel fan speed requires Linux kernel 6.16+. Intel graphics clock requires the Xe driver.

Attributes

All GPU metrics carry these base attributes:

Attribute	Description	Example
hw.id	Unique device identifier (required by spec)	GPU-a1b2c3d4-5678-...
hw.name	Product name	NVIDIA A100-SXM4-80GB
hw.vendor	Vendor name	nvidia, amd, intel
gpu.index	Zero-based device index	0, 1
gpu.pci_address	PCI bus address	0000:01:00.0

Additional per-metric attributes:

Metric	Attribute	Values
hw.gpu.utilization	hw.gpu.task	general, encoder, decoder
hw.gpu.temperature	sensor	die, memory
hw.errors	error.type	corrected, uncorrected, pcie_replay
hw.errors	hw.type	gpu

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System Metrics

Collected on all platforms (Linux, macOS, Windows) via gopsutil. Follows the OTel semantic conventions for system metrics.

Metric	Type	Unit	Description	Attributes
system.cpu.utilization	Gauge	1	CPU utilization per logical core (0.0–1.0)	cpu.logical_number
system.cpu.logical.count	UpDownCounter	{cpu}	Number of logical CPU cores
system.memory.usage	UpDownCounter	By	Memory bytes by state	system.memory.state=
system.memory.utilization	Gauge	1	Memory utilization (0.0–1.0)
system.disk.io	Counter	By	Disk I/O bytes	system.device, disk.io.direction=
system.disk.operations	Counter	{operation}	Disk I/O operations	system.device, disk.io.direction=
system.filesystem.usage	UpDownCounter	By	Filesystem space by state	system.device, system.filesystem.mountpoint, system.filesystem.type, system.filesystem.state=
system.filesystem.utilization	Gauge	1	Filesystem utilization (0.0–1.0)	system.device, system.filesystem.mountpoint, system.filesystem.type
system.network.io	Counter	By	Network I/O bytes	network.interface.name, network.io.direction=
system.network.errors	Counter	{error}	Network errors	network.interface.name, network.io.direction=

system.memory.state values cached and buffers are only reported on Linux. Loopback interfaces (lo, lo0) are excluded from network metrics.

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Process Metrics

Self-monitoring of the collector process. Follows the OTel semantic conventions for process metrics.

Metric	Type	Unit	Description	Attributes
process.cpu.time	Counter	s	Cumulative CPU time	cpu.mode=
process.cpu.utilization	Gauge	1	CPU utilization (0.0–1.0)
process.memory.usage	UpDownCounter	By	Resident memory (RSS)
process.memory.virtual	UpDownCounter	By	Virtual memory size
process.thread.count	UpDownCounter	{thread}	OS thread count
process.unix.file_descriptor.count	UpDownCounter	{file_descriptor}	Open file descriptors (Linux/macOS)
process.runtime.go.goroutines	Gauge	{goroutine}	Go goroutine count
process.runtime.go.mem.heap_alloc	Gauge	By	Go heap memory allocated

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eBPF CUDA Metrics (opt-in)

Enable with OTEL_GPU_EBPF_ENABLED=true. Requires Linux, CAP_BPF + CAP_PERFMON (or root), and an NVIDIA CUDA runtime (libcudart.so). Attaches uprobes to cudaLaunchKernel, cudaMalloc, and cudaMemcpy.

Metric	Type	Unit	Description	Attributes
gpu.kernel.launch.calls	Counter	{call}	CUDA kernel launch count	cuda.kernel.name
gpu.kernel.grid.size	Histogram	{thread}	Total threads in grid per launch	cuda.kernel.name
gpu.kernel.block.size	Histogram	{thread}	Threads per block per launch	cuda.kernel.name
gpu.memory.allocations	Counter	By	Bytes allocated via cudaMalloc
gpu.memory.copies	Histogram	By	Bytes per cudaMemcpy call	cuda.memcpy.kind=