NVIDIA GPU Status Overview | Generated by AI
Overview
This is the output from the nvidia-smi command, a tool for monitoring and managing NVIDIA GPUs on Linux systems (here, it looks like Ubuntu or a similar distro, based on the paths). It provides a snapshot of your GPU’s status, including hardware details, utilization, power consumption, memory usage, and active processes. The command was run on October 7, 2025, at 3:16:58 AM local time, in a directory related to the nanoGPT project (a popular PyTorch-based GPT training repo).
The output is divided into three main sections: a header with software versions, a table summarizing the GPU’s current state, and a table of processes using the GPU. I’ll break it down step by step.
Header
Tue Oct 7 03:16:58 2025
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.247.01 Driver Version: 535.247.01 CUDA Version: 12.2 |
- Timestamp: When the command was executed.
- NVIDIA-SMI Version: 535.247.01 (the tool itself).
- Driver Version: 535.247.01 (the NVIDIA kernel driver installed on your system).
- CUDA Version: 12.2 (the CUDA toolkit version, used for GPU-accelerated computing like in PyTorch or TensorFlow).
This setup is compatible with modern ML workloads, like training models in nanoGPT.
GPU Status Table
This table shows details for your single detected GPU (index 0). It’s formatted with columns for hardware ID, display status, error correction, and real-time metrics.
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4070 On | 00000000:01:00.0 On | N/A |
| 32% 47C P2 74W / 215W | 3144MiB / 12282MiB | 2% Default |
| | | N/A |
- GPU 0: The first (and only) GPU.
- Name: NVIDIA GeForce RTX 4070 (a consumer-grade GPU with 12GB GDDR6X VRAM, great for gaming and ML training).
- Persistence-M: “On” means the GPU driver stays loaded even when no apps are using it (reduces startup latency for apps).
- Bus-Id: 00000000:01:00.0 (PCIe slot address; useful for troubleshooting multi-GPU setups).
- Disp.A: “On” means the GPU is driving a display (e.g., your monitor).
- Volatile Uncorr. ECC: N/A (Error-Correcting Code for memory; not supported/enabled on consumer GPUs like the 4070).
- Fan: 32% speed (cooling fan running moderately).
- Temp: 47°C (current temperature; safe, as RTX 4070 can handle up to ~90°C).
- Perf: P2 (performance state; P0 is max boost, P8 is idle—P2 is a balanced mid-state).
- Pwr:Usage/Cap: 74W current draw out of 215W max (low power use, indicating light load).
- Memory-Usage: 3144MiB used out of 12282MiB total (~3GB/12GB; about 26% full—room for larger models).
- GPU-Util: 2% (core utilization; very low, so the GPU is mostly idle).
- Compute M.: Default (compute mode; allows multiple processes to share the GPU).
- MIG M.: N/A (Multi-Instance GPU partitioning; not available on this consumer card).
Overall, your GPU is healthy and under light load—probably just handling desktop graphics with some background tasks.
Processes Table
This lists all processes currently using GPU memory or compute resources. Columns include GPU index, process IDs (GI/CI are N/A here, as they’re for advanced multi-instance tracking), PID (process ID), Type (G=Graphics like rendering, C=Compute like ML training), process name, and memory usage.
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 2927 G /usr/lib/xorg/Xorg 814MiB |
| 0 N/A N/A 3072 G /usr/bin/gnome-shell 158MiB |
| 0 N/A N/A 24177 G firefox 235MiB |
| 0 N/A N/A 213795 G /proc/self/exe 112MiB |
| 0 N/A N/A 213796 G ...erProcess --variations-seed-version 96MiB |
| 0 N/A N/A 232689 C python3.10 1708MiB |
- 2927 G /usr/lib/xorg/Xorg: 814MiB—X11 display server (handles your graphical desktop; big user due to rendering).
- 3072 G /usr/bin/gnome-shell: 158MiB—GNOME desktop environment (UI effects and window management).
- 24177 G firefox: 235MiB—Firefox browser (GPU acceleration for web rendering/videos).
- 213795 G /proc/self/exe: 112MiB—Likely a browser renderer process (self-referential path common in sandboxed apps like Chrome).
- 213796 G …erProcess –variations-seed-version: 96MiB—Probably a Chrome renderer subprocess (the ellipsis hides the full path; flags relate to feature experiments).
- 232689 C python3.10: 1708MiB—Python 3.10 process in compute mode (this is the big one—likely your nanoGPT training script or Jupyter session using PyTorch/CUDA; it’s hogging ~1.7GB VRAM).
Total GPU memory adds up to ~3.1GB, matching the earlier usage stat. The Python process is the main compute workload here.
Quick Tips
- If you’re training models in nanoGPT, watch for high GPU-Util (>80%) and memory nearing 12GB—consider batch size tweaks if it OOMs.
- Run
nvidia-smi -l 1for live updates every second. - To kill a process:
kill <PID>(e.g., if Python hangs).
This snapshot shows a stable, low-load system ready for GPU tasks!