NVIDIA Jetson AGX Orin 32GB — Benchmarks & Specs
Bottom line: how fast is the NVIDIA Jetson AGX Orin 32GB?
For local LLM inference it generates 52.0 tokens/sec running llama3.1:8b at q4_K_M under llama.cpp, per ProventusNova. It has 32 GB of memory shared by the CPU and GPU, and the llama3.1:8b run above (8B parameters) is the largest model on file at 4-bit quantization on it. Larger models have no sourced run on file.
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.
The NVIDIA Jetson AGX Orin 32GB is an APU from the Jetson Orin family released in 2022 from NVIDIA. Key on-paper specs include 12 CPU cores, 1792 GPU cores, 32 GB of memory shared by the CPU and GPU. It launched with a $999 MSRP, though street prices typically diverge meaningfully from launch pricing. Data on this page draws on 16 community AI inference reports (top 12 shown), compiled from IoT Digital Twin PLM, MultimodalFlow, ProventusNova, arXiv:2506.09554, DFRobot, GitHub, GitHub llama.cpp discussion #17732; each table row links to its source. Read this page when shopping the NVIDIA Jetson AGX Orin 32GB, comparing it against other APUs in your build, or sizing it for a specific workload (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 |
|---|---|---|---|---|---|
| llama3.1:8b | q4_K_M llama.cpp | 52.0 tok/s | — | ProventusNova 2026-06-10 | |
| phi3-mini:3.8b | q4_K_M llama.cpp | 47.0 tok/s | 2.8 GB | MultimodalFlow 2026-05-29 | |
| llama3:8b | q4_0 other | 40.0 tok/s | — | DFRobot 2024-07-12 | |
| qwen3-vl:4b | q4_K_M llama.cpp | 32.7 tok/s | 2.3 GB | GitHub llama.cpp discussion #17732 2025-06-01 | |
| qwen2.5:7b | q4_K_M llama.cpp | 31.0 tok/s | 5.2 GB | MultimodalFlow 2026-05-29 | |
| llama3.1:8b | q4_K_M llama.cpp | 28.0 tok/s | 5.8 GB | MultimodalFlow 2026-05-29 | |
| gemma:7b | — mlc | 23.0 tok/s | — | GitHub (dusty-nv/jetson-containers) 2024-05-20 | |
| gemma:2b | q4_0 other | 23.0 tok/s | — | GitHub dusty-nv/jetson-containers issue #532 2024-05-20 |
Batched serving throughput
| Model | Quantization | Relative | Tokens/sec (aggregate) | VRAM used | Source |
|---|---|---|---|---|---|
| gemma2:2b | FP16 TensorRT-LLM batched | 120.0 tok/s aggregate | — | IoT Digital Twin PLM 2026-04-01 | |
| llama3.2:3b | INT4 TensorRT-LLM batched | 85.0 tok/s aggregate | 3.2 GB | IoT Digital Twin PLM 2026-04-01 | |
| phi3.5-mini:3.8b | INT4 TensorRT-LLM batched | 62.0 tok/s aggregate | — | IoT Digital Twin PLM 2026-04-01 | |
| phi2:2.7b | FP16 vllm batched | 25.4 tok/s aggregate | 5.6 GB | arXiv:2506.09554 (Understanding LLM Inferencing on Edge Accelerators) 2025-06-12 |
Full Specifications
| ram gb | 32 |
|---|---|
| ai tops | 275 |
| cpu cores | 12 |
| gpu cores | 1792 |
NVIDIA Jetson AGX Orin 32GB — Frequently Asked Questions
What is the NVIDIA Jetson AGX Orin 32GB best used for?
When was the NVIDIA Jetson AGX Orin 32GB released, and what was its launch MSRP?
Where do the NVIDIA Jetson AGX Orin 32GB benchmark numbers come from?
How many cores does the NVIDIA Jetson AGX Orin 32GB have, and how does it benchmark?
Where can I buy the NVIDIA Jetson AGX Orin 32GB?
Buying guides that rank the NVIDIA Jetson AGX Orin 32GB'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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