NVIDIA Jetson Orin Nano Super — Benchmarks & Specs
Short answer: the NVIDIA Jetson Orin Nano Super generates 12.6 tokens/sec on qwen3:8b (q4_K_M) under llama.cpp in a single cited run, per Turing Pi; there are too few comparable runs on file for a median.
Bottom line: how fast is the NVIDIA Jetson Orin Nano Super?
For local LLM inference it generates 12.6 tokens/sec running qwen3:8b at q4_K_M under llama.cpp, per Turing Pi. In 7-Zip it scores 14,820 MIPS, per CNX Software. Its 8 GB of memory, shared by the CPU and GPU, is the binding constraint for local inference: the largest models on file at 4-bit quantization on it are 8B-class: the qwen3:8b run above and llama3.1:8b (INT4), which generates 19.1 tokens/sec under mlc-llm, per NVIDIA Developer Blog.
Every figure above is a row in the tables below, and each row links out to the review or public benchmark database the number was taken from; the memory size is the on-paper spec. SpecPicks aggregates published measurements; it does not report first-party benchmark runs.
Amazon listing: $629.00
This Amazon listing is above the NVIDIA Jetson Orin Nano Super's $249 launch MSRP. Compare new and used listings on eBay before buying.
*Price sourced from Amazon.com. Last updated 2026-10-22. Price and availability subject to change.
The NVIDIA Jetson Orin Nano Super is an APU from the Jetson Orin family released in 2024 from NVIDIA. Key on-paper specs include 6 CPU cores, 1024 GPU cores, 8 GB of memory shared by the CPU and GPU. It launched with a $249 MSRP, though street prices typically diverge meaningfully from launch pricing — see the linked product cards below for current listings. Data on this page draws on 7 linked listings, 5 synthetic benchmark results, 32 community AI inference reports (top 14 shown), compiled from CNX Software, NVIDIA Developer Blog, smolhub.com, Turing Pi, heyytechy.com, Magazin Mehatronika, IoT Digital Twin PLM, NVIDIA Developer Forums; each table row links to its source. Read this page when shopping the NVIDIA Jetson Orin Nano Super, comparing it against other APUs in your build, or sizing it for a specific workload (synthetic benchmark scores or local LLM inference).
AI Inference Performance
Tokens per second under each model + quantization. Higher = faster generation. Bars compare runs across the same model.
| Model | Quantization | Relative | Tokens/sec | VRAM used | Source |
|---|---|---|---|---|---|
| llama3.2:1b | INT4 mlc-llm | 86.5 tok/s | 1.0 GB | NVIDIA Developer Forums 2025-01-23 | |
| llama3.2:1b | q4_K_M ollama | 79.0 tok/s | — | heyytechy.com 2026-06-17 | |
| smollm2:1.7b | INT4 mlc-llm | 64.5 tok/s | — | NVIDIA Technical Blog 2024-12-17 | |
| llama3.2:1b | FP16 llama.cpp | 55.0 tok/s | — | IoT Digital Twin PLM 2026-04 | |
| qwen3:0.6b | q8_0 llama.cpp | 54.2 tok/s | — | smolhub.com 2026-05-29 | |
| gemma2:2b | q4_K_M ollama | 52.0 tok/s | — | heyytechy.com 2026-06-17 | |
| llama3.2:1b | q4_K_M llama.cpp | 51.9 tok/s | — | smolhub.com 2026-05-29 | |
| qwen3:0.6b | q8_0 llama.cpp | 49.4 tok/s | — | SmolHub / Yuvraj Singh 2026-05-29 | |
| llama3.2:1b | q4_K_M llama.cpp | 47.1 tok/s | — | SmolHub / Yuvraj Singh 2026-05-29 | |
| llama3.2:3b | INT4 mlc-llm | 43.1 tok/s | — | NVIDIA Developer Blog 2024-12-17 | |
| gemma3:1b | q4_K_M llama.cpp | 40.8 tok/s | — | SmolHub / Yuvraj Singh 2026-05-29 | |
| qwen3:1.7b | q4_K_M llama.cpp | 39.4 tok/s | — | Turing Pi 2026-09-26 | |
| llama3.1:8b | INT4 mlc-llm | 19.1 tok/s | — | NVIDIA Developer Blog 2024-12-17 | |
| qwen3:8b | q4_K_M llama.cpp | 12.6 tok/s | — | Turing Pi 2026-09-26 |
Synthetic Benchmarks
Higher is better. Bars are scaled within each benchmark family (multi-thread, single-thread, etc.) so you can compare like-with-like at a glance.
| Benchmark | Relative | Score | Source |
|---|---|---|---|
| 7-Zip | 14,820 MIPS | CNX Software 2025-08-16 | |
| Geekbench 5 multi-core | 2,866 points | Magazin Mehatronika 2025-02 | |
| glmark2 OpenGL 4.6 | 2,563 points | CNX Software 2025-08-16 | |
| glmark2-es2 | 2,488 points | CNX Software 2025-08-16 | |
| Geekbench 5 single-core | 538 points | Magazin Mehatronika 2025-02 |
Products Featuring the NVIDIA Jetson Orin Nano Super
Full Specifications
| Memory | 8 GB |
|---|---|
| AI TOPS | 67 |
| CPU cores | 6 |
| GPU cores | 1024 |
NVIDIA Jetson Orin Nano Super — Frequently Asked Questions
What is the NVIDIA Jetson Orin Nano Super best used for?
When was the NVIDIA Jetson Orin Nano Super released, and what was its launch MSRP?
Where do the NVIDIA Jetson Orin Nano Super benchmark numbers come from?
How many cores does the NVIDIA Jetson Orin Nano Super have, and how does it benchmark?
Where can I buy the NVIDIA Jetson Orin Nano Super?
Buying guides that rank the NVIDIA Jetson Orin Nano Super's class
This page is the raw performance data. The guides below turn it into a ranked pick for a specific build.
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