Origin PC S-CLASS Edge AI

The core compute is the NVIDIA Jetson Orin NX 16GB Super — an 8-core Arm Cortex-A78AE CPU paired with a 1024-core Ampere GPU featuring 32 Tensor Cores. The key specs for AI workloads:

• VRAM: 16 GB LPDDR5 (128-bit, 102.4 GB/s bandwidth)
• INT8 Performance: 157 TOPS
• TDP: 52 W (configurable, with efficient thermals in sub-5L chassis)
• Storage: 256 GB M.2 NVMe SSD (expandable via M.2 slot)
• Connectivity: Dual Ethernet, MIPI CSI-2, CAN bus, USB 3.2, HDMI 2.0, M.2 expansion for 5G/Wi-Fi

Why these numbers matter for inference:

• 16 GB unified VRAM is the practical ceiling for running large language models (LLMs) on edge hardware. It can load a 7B-parameter model at 4-bit quantization (Q4) with room left for context and preprocessing. Memory bandwidth of 102.4 GB/s is sufficient for real-time token generation on edge-optimized models, though it won’t match high-end desktop GPUs (e.g., RTX 4090’s 1 TB/s). For edge deployment, token throughput is acceptable — expect 30–50 tokens/second for a 7B Q4 model using TensorRT, depending on batch size and prompt length.

• 157 TOPS is the headline compute figure for INT8 operations. This translates to strong performance on vision models (ResNet, YOLO, ViT) and small-to-medium transformer-based pipelines. For LLMs, the bottleneck is typically memory bandwidth, not raw TOPS, but the Tensor Cores accelerate key operations like matrix multiplications when using TensorRT optimizations.

• 52 W TDP makes this kit deployable in constrained environments — lab benches, robot chassis, or edge cabinets — without active cooling noise or thermal throttling. The sub-5L enclosure is thermally optimized for sustained loads.

Comparison with alternatives:

The bare NVIDIA Jetson Orin NX 16GB Super module costs ~$600–$700. The Origin PC kit adds $500+ for the enclosure, storage, preinstalled software, and support. If you value time-to-deployment and a validated environment, the premium is justified. Against a consumer GPU like an RTX 4060 (8 GB VRAM, ~$300), the S-CLASS offers double the VRAM and a fraction of the power draw, but lower raw throughput for dense models. The choice depends on whether you need edge form factor, power efficiency, and deterministic software stacks.

The S-CLASS Edge AI is designed for models that fit within 16 GB of unified memory. Here’s what that means in practice:

• LLMs (Large Language Models):
• 7B parameters at Q4 (e.g., Llama 3.1 8B, Mistral 7B, Qwen 2.5 7B, DeepSeek-R1 7B) — these fit comfortably with ~6–8 GB used, leaving room for context windows of 4k–8k tokens.
• 13B parameters at Q4 (e.g., Llama 2 13B, CodeLlama 13B) — tight fit, requires aggressive quantization (Q3) or smaller context. Not recommended for production.
• 3B–4B models at Q8 (e.g., Phi-3-mini, Gemma 2B) — run easily with high precision.
• Expected tokens/second: With TensorRT and batch size 1, 7B Q4 models typically achieve 30–50 tokens/s. For edge deployment, this is usable for chatbots, code completion, and lightweight agentic workflows.

• Vision models:
• YOLOv8, ResNet-50, MobileNet, ViT-B — all run at high throughput (hundreds of FPS) thanks to the 157 TOPS INT8 engine.
• Multimodal models like LLaVA 7B (vision-language) fit at Q4 but with reduced context. Expect 10–20 tokens/s.

• Edge-optimized models via TensorRT:

The kit ships with TensorRT preinstalled, which can further compress and accelerate models. For example, Llama 3.1 8B with TensorRT-LLM can achieve 40–60 tokens/s with INT4 quantization and FlashAttention. The sweet spot is 7B Q4 — best quality-to-speed tradeoff for most edge use cases.

• What doesn’t fit:

Models above 13B parameters (even at Q4) exceed 16 GB. Mixtral 8x7B (46 GB at FP16) is out of reach. Long-context tasks (32k+ tokens) will also be constrained by memory. The S-CLASS is not a replacement for multi-GPU servers.

The S-CLASS Edge AI is built for practitioners who need a reproducible, deployment-ready edge platform. Who should buy it?

• Robotics developers: The CAN bus, MIPI CSI-2 camera interface, and ROS2-ready environment make it a natural fit for prototyping perception stacks, SLAM, and autonomous navigation. The sub-5L chassis can be mounted on a robot base.

• Embedded AI engineers: Teams validating models for edge deployment (smart cameras, industrial sensors, drones) benefit from the turnkey JetPack environment. No need to wrestle with flashing modules or configuring CUDA — just load your model and test.

• Researchers and educators: Consistent hardware and software allow reproducible experiments. The structured benchmarks and sample pipelines reduce variability between lab setups.

• Hobbyists running local AI agents: For those who want a dedicated edge device for local LLM inference or home automation, the S-CLASS offers a plug-and-play experience. It’s quieter and more compact than a gaming PC, and draws 52 W at peak — ideal for always-on operation.

• System integrators: The M.2 expansion (5G/Wi-Fi) and dual Ethernet enable remote deployment and data streaming. The lifetime labor warranty and 2-year parts replacement reduce risk for field deployments.

Training vs. inference: This kit is strictly for inference and fine-tuning (with limited LoRA). Do not expect to train 7B models from scratch — the 16 GB VRAM and Arm CPU are not suitable for training workloads. For training, look at desktop GPUs with 24+ GB.

vs. NVIDIA Jetson Orin NX Developer Kit (reference design)

The NVIDIA kit is a bare module with a carrier board, no enclosure, no storage, and no preinstalled software beyond JetPack. It costs ~$700–$800 total with a power supply and SD card. The S-CLASS Edge AI adds a rugged sub-5L chassis, 256 GB NVMe, preconfigured JetPack with sample pipelines, and a warranty. If you value time-to-market and don’t want to build your own setup, the S-CLASS is the better pick. If you need maximum flexibility and have your own enclosure and storage, the bare module is cheaper.

vs. ASUS Jetson Orin NX Developer Kit

ASUS offers a similar turnkey solution with a larger enclosure and more I/O (e.g., dual 2.5GbE). It costs ~$1,400. The S-CLASS is $150 cheaper and smaller, but ASUS has slightly better networking for high-bandwidth sensor fusion. Choose the S-CLASS if you prioritize compact size and lower cost; choose ASUS if you need dual 2.5GbE.

vs. consumer GPU (e.g., RTX 4060 + mini-ITX PC)

A self-built mini PC with an RTX 4060 (8 GB VRAM) costs ~$800–$1,000 but draws 150 W+ and is larger. The RTX 4060 has higher FP16 throughput but half the VRAM — cannot run 7B Q4 models with reasonable context. For edge deployment, the S-CLASS wins on power efficiency, VRAM, and software stack. For desktop inference where power and size are less critical, a used RTX 3090 (24 GB) is a better value at ~$1,000.

When to pick the S-CLASS Edge AI:

• You need a validated, reproducible edge development environment.
• Your workloads fit within 16 GB unified memory (7B Q4 models, vision models).
• Power, size, and quiet operation are priorities.
• You want lifetime labor support and a 2-year parts warranty.