Jetson Orin NX 8GB for Edge AI — Specs, Sizing & Deployment Fit
Last updated: August 2026
The value cut of the NX: 117 TOPS on the same silicon as the 16GB (Ampere GPU, dual NVDLA, same ~102 GB/s bandwidth) with half the memory — enough for 4–8 detection streams per node at ~$649.
Planning Takeaway
The value cut of the NX: 117 TOPS on the same silicon as the 16GB with half the memory, carrying 4–8 detection streams per node at ~$649. Because compute-per-dollar stays high, EdgeAIStack’s multi-node planner actively recommends it as the per-node building block for multi-camera 4K deployments — the constraint to watch is the 8 GB memory ceiling, not TOPS.
Specifications
| Spec | Value |
|---|---|
| Compute | 117 INT8 TOPS (Ampere GPU + 2× NVDLA) |
| Memory | 8 GB LPDDR5, ~102 GB/s |
| Power modes | 10W · 15W · MAXN · 40W Super · MAXN Super (10–40 W envelope) |
| Video decode (modeled planning ceiling) | ~10× 1080p / ~2× 4K H.264 concurrent |
| Est. planning capacity | ~8 concurrent 1080p detection streams |
| Cooling | Active required |
| Form factor | Jetson module (69.6×45 mm) |
| Runtimes / precisions | TensorRT, PyTorch, ONNX, llama.cpp · FP32/FP16/INT8 |
| Software | JetPack 7.2 (Ubuntu 24.04, CUDA 13, June 2026) — current; JetPack 6.x (Ubuntu 22.04) — legacy track |
| Indicative price | ~$649 (module, as of mid-2026; NX 16GB module ~$999) |
Sizing Fit
Fits 4–8 camera 1080p detection nodes where 8 GB of memory covers the pipeline, and multi-node 4K deployments where its compute-per-dollar makes it the planner’s preferred per-node building block.
- 4-camera SMB budget architecture — Reference architecture
- Orin Nano vs Orin NX decision guide — Comparison
- MAXN vs MAXN Super power modes — Guide
- Jetson lineup power comparison — Benchmarks
- YOLOv8 RAM requirements on Jetson — Guide
- YOLO with TensorRT: FP16 vs INT8 throughput — Benchmarks
Decision Framework
Choose the Orin NX 8GB if:
- You need 4–8 concurrent 1080p detection streams per node and your models fit in 8 GB.
- You are scaling out — its compute-per-dollar makes it a strong per-node building block, and the multi-node planner recommends it for multi-camera 4K workloads.
- You want DLA engines for power-efficient offload alongside the GPU without paying for the 16GB.
Choose a sibling instead if:
- Jetson Orin Nano Super — your node serves ≤6 streams and ~$399 buys enough compute.
- Jetson Orin NX 16GB — multi-model pipelines or tracking push past 8 GB of memory, or you need ~12 streams on one node.
- Hailo-10H — detection-only at up to ~8 streams on an existing host at a fraction of the power (~$130).
Frequently Asked Questions
How much does the Jetson Orin NX 8GB cost?
The Orin NX 8GB module lists at ~$649 as of the July 2026 NVIDIA repricing (up from $399); the 16 GB variant lists at ~$999. Carrier board and cooling are additional.
How many cameras can the Orin NX 8GB handle?
EdgeAIStack’s planning model puts it around 8 concurrent 1080p detection streams at 15 FPS, with hardware decode ceilings near ~10× 1080p or ~2× 4K H.264. Real capacity depends on model, resolution, and pipeline — validate with the Hardware Selector.
Orin NX 8GB vs 16GB — what do you actually give up?
Memory and some clocks: 8 GB vs 16 GB, 117 vs 157 INT8 TOPS, at the same ~102 GB/s bandwidth on the same silicon (Ampere GPU, 2× NVDLA, one NVDEC). EdgeAIStack models it at ~8 streams vs ~12 for the 16GB. At ~$649 vs ~$999 the 8GB wins when your models fit its memory; step up when they don’t.
What JetPack version does the Orin NX 8GB run?
JetPack 7.2 (June 2026) is current for this module — Ubuntu 24.04, Linux kernel 6.8, CUDA 13. JetPack 6.x remains a widely deployed legacy track on Ubuntu 22.04. Moving an existing JetPack 6 install to 7 requires the OTA package or a full reflash — a plain apt upgrade is not an upgrade path.