NVIDIA Jetson Xavier NX 8 GB

The NVIDIA Jetson Xavier NX 8 GB is an embedded system-on-module built on the Volta architecture, packing 384 shading units, 48 tensor cores, and 8 GB of LPDDR4X memory into a 70 mm by 45 mm board. With a 15 W TDP and a 50th percentile rank among all GPUs in the database, it targets edge AI and compact computing tasks rather than desktop gaming. The absence of recorded benchmark scores means its performance must be inferred from theoretical throughput numbers and architectural features.

Memory Subsystem

The Jetson Xavier NX 8 GB pairs 8 GB of LPDDR4X memory with a 128-bit bus, yielding a bandwidth of 59.71 GB/s. The memory clock is 1866 MHz, with an effective data rate of 3.7 Gbps. This configuration is modest by desktop standards, but for an embedded module designed for AI inference and lightweight graphics, it offers a balanced capacity-to-bandwidth ratio. The 8 GB capacity allows loading moderate-sized neural network models or multiple concurrent inference workloads without spilling to system memory, which is critical in edge scenarios where system RAM may be limited. The 128-bit interface, combined with the relatively low memory clock, results in bandwidth that is sufficient for the compute throughput available—844.8 GFLOPS of FP32 and 1.690 TFLOPS of FP16 (2:1 ratio). At high resolutions, such as 4K or above, the bandwidth could become a bottleneck for heavy graphics workloads, but the module is not designed for high-end gaming; its primary role is compute-intensive AI tasks where memory access patterns are often more predictable. The LPDDR4X type is power-efficient, aligning with the 15 W TDP envelope. Overall, the memory subsystem is appropriate for the intended use case, though it would not satisfy demanding high-resolution texture streaming or large frame buffer requirements.

Ray Tracing and Feature Set

The Jetson Xavier NX 8 GB does not include dedicated ray tracing cores; the `rtCores` field is null. Instead, it relies on 48 tensor cores, which are specialized for matrix operations used in AI and deep learning workloads. The architecture is Volta, which predates NVIDIA's RTX line and thus lacks hardware-accelerated ray tracing. For graphics, it supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.2, meaning it can run modern APIs but without ray tracing extensions. The tensor cores provide significant FP16 throughput—1.690 TFLOPS at a 2:1 ratio relative to FP32—making it well-suited for inference tasks that leverage mixed-precision arithmetic. The feature set is clearly oriented toward embedded AI, robotics, and computer vision rather than ray-traced gaming. The lack of RT cores is not a drawback in its target domain, but it limits the module's appeal for any application requiring hardware ray tracing. The API support is broad enough for general-purpose compute and traditional rasterization, but the absence of RT-specific features means developers must rely on software-based techniques if needed.

Benchmark Performance

No benchmark scores are recorded for the Jetson Xavier NX 8 GB; the `avgBenchmarkScore` is 0, and the `benchmarks` array is empty. However, the database places it at the 50th percentile among all GPUs, indicating it sits at the median of the performance distribution—though this percentile is likely based on a broader set of metrics rather than a specific test. The theoretical compute figures provide a basis for analysis: FP32 throughput is 844.8 GFLOPS, FP16 is 1.690 TFLOPS (2:1), pixel rate is 17.60 GPixel/s, and texture rate is 26.40 GTexel/s. These numbers suggest a GPU that is roughly comparable to entry-level desktop GPUs from several generations ago, but with a strong emphasis on tensor operations. The FP16 performance being exactly double the FP32 is typical of Volta's mixed-precision design, enabling efficient AI inference. The pixel and texture rates, while not exceptional, are sufficient for driving simple displays or processing video streams. In the absence of direct benchmarks, one can infer that the module's performance is adequate for its intended embedded workloads—such as real-time object detection, image classification, and sensor fusion—but it would struggle with modern AAA gaming at 1080p, let alone higher resolutions. The 50th percentile rank suggests that among all GPUs in the database, it is neither a performance outlier nor a laggard, but rather a middle-of-the-road part in terms of raw compute capability.

Who Should Consider It

The Jetson Xavier NX 8 GB is designed for developers and engineers building compact, power-constrained systems that require GPU acceleration for AI inference. With 8 GB of memory and 48 tensor cores, it can handle models like ResNet, YOLO, or transformer-based architectures at modest batch sizes. The 15 W TDP makes it suitable for battery-powered robots, drones, and portable medical devices. Its small dimensions—70 mm by 45 mm—allow integration into custom carrier boards. The module supports PCIe 4.0 x4 for connectivity, and its display outputs are "portable device dependent," meaning they vary by carrier board design. For users who need to run traditional graphics workloads, the DirectX 12, OpenGL 4.6, and Vulkan 1.2 support can drive lightweight interfaces or simple 3D scenes, but the lack of ray tracing and the limited bandwidth will not satisfy high-end gaming or professional rendering. The production status is end-of-life, so it is not recommended for new high-volume designs, but it remains viable for prototyping or legacy systems. The launch MSRP was 399 USD, though that is a historical reference. Given its compute characteristics, it is best suited for edge AI applications where low power and compact size outweigh raw performance.

FAQ

Q: What is the memory type and capacity?

A: The Jetson Xavier NX 8 GB has 8 GB of LPDDR4X memory on a 128-bit bus, with a bandwidth of 59.71 GB/s.

Q: Does it support hardware ray tracing?

A: No. The module has no RT cores; it relies on 48 tensor cores for AI and compute tasks.

Q: What is the power consumption?

A: The TDP is 15 W, making it suitable for power-sensitive embedded designs.

Q: Which graphics APIs are supported?

A: It supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.2.

Q: When was it released?

A: The release date is May 13, 2020.

Q: What is the form factor?

A: It is an IGP (integrated graphics processor) module measuring 70 mm by 45 mm, with no dedicated power connectors listed.

Power and Cooling

The Jetson Xavier NX 8 GB has a TDP of 15 W, which is remarkably low for a GPU with 384 shading units and 48 tensor cores. This power envelope allows for passive cooling in many chassis designs, though the fact pack does not specify a suggested PSU or power connector requirements. The absence of a dedicated power connector implies that power is supplied through the carrier board, likely via the PCIe slot or a system rail. The low TDP also means that thermal management is relatively straightforward—a small heatsink or even a well-ventilated enclosure may suffice. The module's dimensions (70 mm x 45 mm) further contribute to its ease of integration. Given the end-of-life status, prospective users should consider that replacement parts may become scarce, but the power efficiency remains a strong selling point for battery-operated devices. The 12 nm process node and 9,000 million transistors on a 350 mm² die yield a transistor density of 25.7M per mm², indicating a mature manufacturing process that balances performance and power.

How It Compares

The fact pack does not include any nearest rival data, so a direct comparative analysis against specific GPUs is not possible from the provided information. The module's 50th percentile rank among all GPUs suggests it sits in the middle of the performance spectrum, but without rival scores or delta percentages, we cannot quantify its lead or deficit relative to any particular competitor. In the broader context of embedded GPUs, the Jetson Xavier NX 8 GB's combination of 8 GB LPDDR4X, 48 tensor cores, and 15 W TDP positions it as a mid-range option for AI edge computing. It is neither the most powerful nor the most power-efficient in that category, but its balanced specifications make it a versatile choice. The lack of benchmark scores means that any claims about superiority or inferiority to other products would be speculative, so the data supports only a qualitative assessment based on the listed compute and memory parameters.