NVIDIA Jetson AGX Xavier 16 GB

Power and Cooling — TDP, PSU recommendation, connector requirements

The NVIDIA Jetson AGX Xavier 16 GB is a 30 W part, placing it in a class of hardware that does not require the elaborate cooling or power delivery systems typical of discrete graphics cards. With a TDP of just 30 W, the thermal envelope is remarkably modest, allowing for passive or low-profile active cooling solutions in embedded and edge-computing deployments. The data shows a chip designed for constrained environments where power budgets are tight and thermal management is simplified.

The system is an integrated graphics processor (IGP), which means there are no dedicated power connectors. The slot width is listed as IGP, confirming that this is not a card that slots into a standard PCIe expansion slot with auxiliary power inputs. Instead, power is delivered through the host board's infrastructure, making the Jetson AGX Xavier suitable for custom carrier boards and compact system-on-module designs. The bus interface is PCIe 4.0 x4, which provides a moderate-bandwidth connection to the host processor, sufficient for the data throughput demands of AI inference and edge workloads.

There is no suggested PSU rating in the fact pack, which aligns with the product's nature as a low-power embedded module rather than a consumer graphics card. The 30 W TDP is the single most important power figure here; it dictates that thermal design power is minimal, and system integrators can plan around a highly efficient power delivery network. The absence of a power connector specification reinforces that this is not a user-serviceable component in the traditional GPU sense but rather a soldered or module-based solution. For any system builder, the key takeaway is that power delivery is straightforward, and cooling requirements are far below what any discrete GPU would demand.

Memory Subsystem — VRAM size/type, bus width, bandwidth and what it means for high resolutions

Memory capacity is 16 GB of LPDDR4X, a generous allocation for an embedded platform. This is not GDDR6 or HBM; LPDDR4X is a low-power memory standard, chosen to align with the 30 W TDP. The memory bus is 256 bits wide, which is a robust interface for the memory type. Combined, the 256-bit bus and LPDDR4X yield a bandwidth of 136.5 GB/s. That figure is modest compared to desktop discrete GPUs, which often exceed 500 GB/s, but it is entirely appropriate for the Jetson's target use case.

For high-resolution workloads, the 136.5 GB/s bandwidth will be a limiting factor in some scenarios. The data indicates that 4K rendering or heavy texture streaming could saturate the memory subsystem, especially when the 512 shading units are under load. However, the Jetson AGX Xavier is not marketed as a high-end gaming or rendering card; its strength lies in AI inference and computer vision tasks, where the 16 GB capacity is more valuable than raw bandwidth. The 16 GB capacity allows for larger model footprints and multiple concurrent inference streams, which is critical for edge deployments. The 256-bit bus width ensures that the LPDDR4X memory is utilized efficiently, but users should expect that memory-bound tasks at very high resolutions will see performance drops. The effective memory clock is 2133 MHz, translating to 4.3 Gbps effective, which is typical for LPDDR4X. Overall, the memory subsystem favors capacity and power efficiency over peak throughput, a trade-off that makes sense for a 30 W part.

Ray Tracing and Feature Set — RT/tensor cores, API support from facts

Ray tracing hardware is notably absent. There are no RT cores listed in the fact pack, which is unsurprising for a Volta-architecture part from the Tegra family. Volta predates the dedicated RT core implementation found in NVIDIA's Turing and later consumer GPUs. Consequently, any ray tracing workload would have to be handled by the 512 shading units in a brute-force manner, which would be inefficient. The fact pack lists no ray tracing acceleration, so this is not a feature for this product.

The tensor cores, however, are a different story. There are 64 tensor cores on this chip, a significant number for an embedded part. These tensor cores are the key differentiator, enabling accelerated matrix math for deep learning inference and training. The Volta tensor core design supports mixed-precision operations, and the fp16 performance of 2.820 TFLOPS (2:1) highlights this capability. In contrast, the fp32 throughput is 1,410.0 GFLOPS, meaning the tensor cores effectively double the compute throughput when using fp16. This makes the Jetson AGX Xavier a formidable edge AI platform, despite its lack of RT cores.

API support is solid for an embedded part. DirectX 12 (12_1) is supported, along with OpenGL 4.6 and Vulkan 1.2. This covers modern graphics APIs for any visualization or rendering tasks, though the hardware's primary role is compute. The pixel rate is 22.03 GPixel/s and the texture rate is 44.06 GTexel/s, which are modest figures, reinforcing that this is not a rasterization powerhouse. The absence of RT cores means no hardware-accelerated ray tracing, and the feature set is clearly oriented toward AI and compute rather than gaming or high-fidelity graphics.

How It Compares — position vs each nearest rival

The fact pack lists no nearest rivals for this product. There are no comparison points in the data, so a direct positional analysis against specific competing GPUs is not possible. The percentile rank is 50, placing it exactly in the middle of the entire GPU database. This is a curious statistic; a 50th percentile ranking suggests that half of all GPUs in the database perform better and half perform worse, which is a reasonable position for an embedded part that is not designed for peak graphics throughput. Without rival data, the comparison must be qualitative: the Jetson AGX Xavier sits in a niche of low-power, high-compute-density modules, where its 30 W TDP and 16 GB memory capacity define its competitive space. It is not competing with desktop gaming cards, nor with data-center accelerators. Its competition would be other embedded AI modules, but no specific names are provided in the fact pack. Therefore, any assessment of its standing relies on the internal metrics: a 50th percentile overall and a feature set that prioritizes tensor performance over rasterization.

Benchmark Performance — analyze scores vs rivals with exact % deltas

The benchmark data for the Jetson AGX Xavier is sparse. The fact pack lists an average benchmark score of 0 and an empty benchmarks array. There are no scores to analyze, and no nearest rivals with deltaPct values to compare against. The percentile rank of 50 is the only quantitative performance indicator available. This percentile is based on the entire GPU database, but without specific benchmark scores, it is impossible to determine what workload contributed to that rank.

What the data does show is the theoretical compute ceiling. The fp32 performance of 1,410.0 GFLOPS is a hard number, and the fp16 performance of 2.820 TFLOPS (2:1) is another. These are raw compute rates, not application-level benchmarks. In a vacuum, these figures suggest that the Jetson AGX Xavier can handle moderate compute tasks, but the lack of benchmark results means there is no real-world validation in this fact pack. The 50th percentile rank implies it is neither a standout performer nor a laggard, which is consistent with a product that serves a specialized function. Without rival deltas, no percentage-based comparisons can be made, and without benchmark scores, no performance interpretation is possible. The conclusion is that this product's performance is defined by its tensor core throughput and memory capacity, not by any measured benchmark in this database. The data is insufficient to claim any performance advantage or disadvantage relative to other GPUs.