Linux GPU Testing Tool Equivalents

Linux GPU Testing Tool Equivalents | Generated by AI

Home 2026.07

Question: What are Linux GPU testing tools like FurMark, GPU-Z, 3DMark, etc.? What are the Linux equivalents?

Answer:

On Windows, people usually use:

GPU-Z → hardware information / monitoring
FurMark → extreme GPU stress test (“burn-in”)
3DMark → gaming benchmark
OCCT → stability + power testing

Linux has equivalents, but the ecosystem is more fragmented.

1. GPU information tools (GPU-Z equivalent)

`nvidia-smi` (NVIDIA)

For RTX 3090 / 4070 / A100 etc:

nvidia-smi

Example:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 575.xx       Driver Version: 575.xx      CUDA Version: 12.x      |
| GPU  Name        Memory-Usage  Temp  Power |
| RTX 3090         10000MiB     70C   320W |
+-----------------------------------------------------------------------------+

Monitor continuously:

watch -n 1 nvidia-smi

More detailed:

nvidia-smi -q

Equivalent to GPU-Z:

VRAM usage
temperature
power
clocks
PCIe link
ECC errors (professional cards)

`nvtop` (Linux GPU htop)

Install:

Ubuntu:

sudo apt install nvtop

Run:

nvtop

Looks like:

GPU  RTX 3090
Util: 99%
Mem: 23GB/24GB
Temp: 72C
Power: 350W

Supports:

NVIDIA
AMD
Intel

Very useful for AI workloads.

2. FurMark equivalent (GPU stress test)

FurMark is basically:

“maximize GPU shader load + heat output”

Linux options:

GPU Burn (closest FurMark)

For NVIDIA:

https://github.com/wilicc/gpu-burn

Build:

git clone https://github.com/wilicc/gpu-burn
cd gpu-burn
make

Run:

./gpu_burn 600

Meaning:

600 seconds stress test.

Example:

GPU 0: RTX 3090
Time: 600 sec
Performance: 35000 Gflop/s

During test:

watch nvidia-smi

You check:

temperature
power stability
crashes
driver reset

For used RTX 3090 from Xianyu, this is one of the first tests I would run.

`stress-ng`

CPU/GPU stress:

sudo apt install stress-ng

stress-ng --gpu 1 --timeout 10m

Less GPU-specific.

3. 3DMark equivalent

3DMark measures gaming performance:

DirectX
ray tracing
rasterization
FPS

Linux does not have official 3DMark.

Alternatives:

Unigine Heaven

Classic Linux GPU benchmark.

Unigine Heaven Benchmark

Example:

./heaven

Measures:

FPS
GPU load
stability

Good for comparing:

RTX 3090 vs RX 7900 XT.

Unigine Superposition

More modern:

Unigine Superposition Benchmark

Tests:

Vulkan
OpenGL
heavy scenes

Phoronix Test Suite

Linux benchmark framework:

Phoronix Test Suite

Install:

sudo apt install phoronix-test-suite

Example:

phoronix-test-suite benchmark unigine-superposition

It can benchmark:

GPU
CPU
memory
storage

4. AI workload GPU test (probably more relevant for you)

Since your use case is LLM training/inference, gaming benchmarks are less meaningful.

I would test:

CUDA bandwidth

nvidia-smi --query-gpu=memory.total,memory.free --format=csv

PyTorch CUDA test

import torch

print(torch.cuda.get_device_name())

x=torch.randn(
    (10000,10000),
    device="cuda"
)

for i in range(100):
    y=x@x

print("OK")

This stresses:

CUDA cores
VRAM
memory controller

LLM inference test

Example:

python -m vllm.entrypoints.openai.api_server \
 --model Qwen/Qwen3-30B \
 --tensor-parallel-size 2

Then measure:

tokens/sec
VRAM usage
stability

For your 2×3090 idea, this is much more meaningful than FurMark.

Used GPU checking workflow (RTX 3090 / 7900 XT)

If buying second-hand:

Step 1: Check identity

NVIDIA:

nvidia-smi

AMD:

rocminfo

Step 2: Stress

./gpu_burn 600

Watch:

nvtop

Look for:

temperature < 85°C
no driver crash
stable power

Step 3: VRAM test

NVIDIA:

gpu-burn

or:

memtest_vulkan

VRAM problems are common on used mining GPUs.

Step 4: Real AI workload

Run:

llama.cpp

or:

vllm

Load a large model until VRAM is nearly full.

For your case (buying used RTX 3090 / RX 7900 XT for AI), my priority would be:

nvidia-smi / nvtop → identify card
gpu-burn 10min → thermal + power
VRAM stress test → memory health
LLM inference → practical AI stability

FurMark/3DMark are more for gamers; AI users care more about VRAM correctness + CUDA/ROCm stability under long tensor workloads.

Back Donate