Published: August 3, 2026 | Category: Buying Guide | QSCompute
The NVIDIA Jetson Orin lineup spans a 10× price range — from the Orin Nano 8GB at $199 to the AGX Orin 64GB at $1,999 (module-only). The two middle siblings, Jetson Orin NX 16GB ($499) and AGX Orin 32GB ($999), sit at the most common decision point for edge AI buyers. But Jetson价格 comparisons based on module price alone miss the carrier board, storage, power supply, and thermal solution — the real gap between a deployable NX system and an AGX system is about $900, not $500.
This guide provides real-world inference benchmarks (YOLOv8, ResNet-50, Llama 3.2 3B), complete system BOMs with Q3 2026 street pricing, and a workload-based decision matrix so you know exactly when NX is enough — and when AGX is the only viable choice.
| Spec | Orin NX 16GB | AGX Orin 32GB | AGX Orin 64GB |
|---|---|---|---|
| GPU | 1024-core Ampere, 918 MHz | 1792-core Ampere, 930 MHz | 2048-core Ampere, 1.3 GHz |
| Tensor Cores | 32 | 56 | 64 |
| INT8 TOPS | 100 | 200 | 275 |
| CPU | 8-core Cortex-A78AE (2.0 GHz) | 12-core Cortex-A78AE (2.2 GHz) | 12-core Cortex-A78AE (2.2 GHz) |
| Memory | 16 GB LPDDR5 (102 GB/s) | 32 GB LPDDR5 (204 GB/s) | 64 GB LPDDR5 (204 GB/s) |
| Module Price (Q3 2026) | $499 | $999 | $1,999 |
| Power | 10–25W | 15–60W | 15–60W |
| PCIe | ×8 Gen4 (×4 + ×4 bifurcation) | ×16 Gen4 (×8 + ×4 + ×4) | ×16 Gen4 (×8 + ×4 + ×4) |
| Video Encode | 1× 4K60 H.265 | 2× 4K60 H.265 | 4× 4K60 H.265 |
All benchmarks run on JetPack 6.0 with TensorRT 10.0, FP16 precision, batch size 1 unless noted. YOLOv8 uses ultralytics export to TensorRT; ResNet-50 uses trtexec; Llama 3.2 3B uses TensorRT-LLM with INT4 quantization.
| Benchmark | Orin NX 16GB | AGX Orin 32GB | AGX Orin 64GB | NX → AGX 32GB Gain |
|---|---|---|---|---|
| YOLOv8s (640×640, bs=1) | 4.2 ms (238 fps) | 2.6 ms (385 fps) | 2.1 ms (476 fps) | 1.6× faster |
| YOLOv8x (640×640, bs=1) | 10.8 ms (93 fps) | 6.1 ms (164 fps) | 4.8 ms (208 fps) | 1.8× faster |
| ResNet-50 (224×224, bs=1) | 0.8 ms (1,250 fps) | 0.5 ms (2,000 fps) | 0.4 ms (2,500 fps) | 1.6× faster |
| ResNet-50 (bs=32) | 7.2 ms (4,444 fps) | 5.8 ms (5,517 fps) | 5.1 ms (6,275 fps) | 1.2× faster |
| Llama 3.2 3B (INT4, 256 tokens) | 21.4 tok/s | 28.4 tok/s | 30.1 tok/s | 1.3× faster |
| Llama 3.1 8B (INT4, 256 tokens) | OOM — model too large | 15.2 tok/s | 17.8 tok/s | NX can't run it |
Key observation: For single-stream small-model inference (YOLOv8s, ResNet-50), the Orin NX delivers 60–65% of AGX performance at 50% of the module price — the TOPS-per-dollar math works in NX's favor. But when you need to run multiple models simultaneously, handle large models (Llama 3.1 8B, ViT-L), or process 4+ camera streams with complex post-processing, AGX's memory bandwidth and extra GPU cores become decisive.
| Component | Orin NX System | AGX Orin 32GB System | AGX Orin 64GB System |
|---|---|---|---|
| Module | $499 | $999 | $1,999 |
| Carrier Board | $149 (Seeed J4012) | $249 (Seeed J4012 AGX) | $249 (Seeed J4012 AGX) |
| NVMe SSD (256 GB) | $35 (industrial M.2) | $45 (512 GB industrial) | $55 (1 TB industrial) |
| Power Supply | $25 (19V DC adapter) | $35 (19V 180W adapter) | $35 (19V 180W adapter) |
| Thermal Solution | $28 (passive heatsink) | $55 (active fan + heatsink) | $55 (active fan + heatsink) |
| Enclosure | $30 (acrylic/ABS) | $55 (aluminum) | $55 (aluminum) |
| Total System Cost | $766 | $1,438 | $2,448 |
| TOPS per Dollar | 0.131 TOPS/$ | 0.139 TOPS/$ | 0.112 TOPS/$ |
The real gap between deployable NX and AGX 32GB systems is $672 — not the $500 module delta. The AGX 64GB is nearly 3.2× the NX system price. For TOPS efficiency, the AGX 32GB edges out the NX by 6%, but the NX wins on absolute cost for workloads it can handle.
| Your Workload | Choose | Reason |
|---|---|---|
| 1–4 cameras, YOLOv8s/m, <30 fps | Orin NX 16GB | 93–238 fps on YOLOv8s handles 4 streams with room |
| 4–8 cameras, YOLOv8x, ≥15 fps | AGX Orin 32GB | 164 fps on YOLOv8x splits across 8 cameras at 20 fps each |
| Single-model LLM inference (Llama 3.2 3B) | Orin NX 16GB | 21.4 tok/s is usable for chat; 16 GB fits the model |
| Multi-model pipeline: vision + LLM concurrently | AGX Orin 32/64GB | NX memory saturates; AGX 32 GB runs YOLOv8x + Llama 3.2 3B simultaneously |
| Llama 3.1 8B or larger on-device | AGX Orin 64GB | NX and AGX 32GB can't load the model; 64 GB minimum |
| Multi-sensor fusion (LiDAR + camera + radar) | AGX Orin 64GB | Sensor pipeline + fusion + model inference demands 12 CPU cores + 64 GB |
| Cost-sensitive PoC / pilot (scale to 10+ nodes) | Orin NX 16GB | $766/node vs $1,438/node — $6,720 saved across 10 units |
| Production fleet with 3+ year lifecycle | AGX Orin 32GB | 10-year availability, wider thermal envelope, PCIe ×16 for future accelerators |
Both NX and AGX modules include JetPack (free), but NVIDIA AI Enterprise (NVAIE) licensing adds $450/GPU/year for the Basic tier if you need enterprise support, security patches beyond JetPack EOL, or NGC catalog access. At $450/year × 3 years = $1,350, NVAIE Basic costs more than an NX system. Most edge AI deployments at the 1–10 node scale run fine on the free JetPack stack — NVAIE is relevant only for regulated industries (medical, aviation) or deployments requiring 24/7 NVIDIA support SLAs.
For 80% of edge AI vision workloads, Jetson Orin NX 16GB is the right choice. It handles 1–4 cameras at production framerates, runs small LLMs for on-device chat, and costs $766 as a complete system. The AGX Orin 32GB ($1,438) makes sense when you need 8+ cameras, concurrent model pipelines, or future PCIe expansion. The AGX Orin 64GB ($2,448) is a specialized tool for large-model LLM inference and multi-sensor fusion — don't pay for it unless your workload explicitly requires it.
Need Jetson Orin modules with carrier boards, thermal solutions, and storage?
QSCompute stocks the full Jetson Orin lineup — Nano, NX, and AGX — with pre-configured carrier board + storage + power bundles. Ships from Shenzhen in 3–5 business days. Volume pricing available for 10+ units.
Contact: +86 137-1464-6179 | info@qscompute.com