Reading nvidia-smi Like a Pro
Inspect your GPU, memory, and utilization.
Reading nvidia-smi Like a Pro is a free CUDA Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the CUDA Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Your GPU Dashboard
Think of nvidia-smi as the dashboard for your GPU. One command shows the model, memory, temperature, and what is running right now. 📊
nvidia-smiThe Top Header
The very top line shows your driver version and the maximum CUDA it supports. Check here first whenever a version error appears.
Naming Your GPU
The first table row lists each GPU's name and an index number. With several cards, those indices are how you pick which device to use.
Memory Usage
The Memory-Usage column shows used versus total VRAM. If it is nearly full, your next allocation may fail with an out-of-memory error.
GPU Utilization
The GPU-Util percentage tells how busy the cores are. A stuck-at-zero number while your kernel runs usually means the work never reached the device.
Temperature and Power
The Temp and power columns reveal thermal health. Sustained high temperatures can cause the GPU to throttle and quietly slow your kernels.
The Process Table
The bottom Processes table lists every program currently using the GPU, with its PID and memory footprint. Great for spotting a forgotten job.
Watch It Live
Add a flag to refresh the dashboard every second, so you can watch memory and utilization change while your program runs.
nvidia-smi -l 1Pick the Columns You Want
For scripts, query mode prints just the fields you ask for as clean, parseable text instead of the full visual table.
nvidia-smi --query-gpu=name,memory.used --format=csvECC and Persistence
On data-center cards nvidia-smi also reports ECC error counts and persistence mode. For laptops and desktops these fields are usually off or absent.
Limit to One GPU
With many cards, the -i flag focuses the report on a single GPU by index. Handy when you only care about the device your job is using.
nvidia-smi -i 0Quick Check
Let us read the dashboard together.
Recap
You can now read nvidia-smi like a pro: driver line up top, memory and utilization in the table, and live processes at the bottom. A glance tells the whole story. ✅
Frequently asked questions
Is the “Reading nvidia-smi Like a Pro” lesson free?
Yes — the full text of “Reading nvidia-smi Like a Pro” is free to read here on the web, and the CUDA Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the CUDA Academy course, upgrade to CoddyKit PRO.
What will I learn in “Reading nvidia-smi Like a Pro”?
Inspect your GPU, memory, and utilization. You practise CUDA Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start CUDA Academy?
No prior experience is required. CUDA Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Reading nvidia-smi Like a Pro” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this CUDA Academy lesson?
Yes. Every CUDA Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Driver, Runtime, and Toolkit Versions
- Reading nvidia-smi Like a Pro
- Compiling with nvcc
- Hello GPU: Your First .cu File