NVIDIA Jetson Orin NX 8 GB

Benchmark Performance

The NVIDIA Jetson Orin NX 8 GB is a specialized embedded GPU that occupies a unique position in the database landscape, landing at the 50th percentile overall. This midpoint ranking reflects a design philosophy that prioritizes power efficiency and AI acceleration over raw rasterization throughput. The silicon delivers 1.567 TFLOPS of FP32 compute, which is modest for traditional gaming workloads but paired with 3.133 TFLOPS of FP16 performance (at a 2:1 ratio) that signals a clear emphasis on neural network inference and tensor operations.

The data shows a chip built around its 1024 shading units, 32 texture mapping units, and 16 raster output units. That configuration yields a pixel rate of 12.24 GPixel/s and a texture rate of 24.48 GTexel/s. These figures place the Orin NX in the lower tier of contemporary GPUs for pure graphics work, but the architecture tells a different story. The GA10B chip on the 8 nm Samsung process is fundamentally a system-on-module designed for edge computing, not desktop gaming. Benchmark results indicate that the FP32 output is roughly half of what a mainstream entry-level desktop card might offer, yet the FP16 throughput nearly doubles that, indicating where the engineering effort was concentrated.

The absence of nearest rival data in the database means direct percentage comparisons are unavailable, but the 50th percentile ranking provides context. This is a midpoint performer across all GPUs ever benchmarked, which is remarkable given its 20 W thermal envelope. The average benchmark score of zero in the dataset suggests that traditional graphics benchmarks have not been widely applied to this part, reinforcing its status as an accelerator rather than a renderer. The 8 nm process node and 200 mm² die size indicate a compact, efficient implementation, and the PCIe 4.0 x4 interface provides a modest but adequate connection for its intended embedded roles.

Memory Subsystem

The memory configuration is where the Jetson Orin NX 8 GB reveals its purpose. It pairs 8 GB of LPDDR5 memory on a 128-bit bus, yielding a bandwidth of 102.4 GB/s. The memory clock runs at 800 MHz with an effective 6.4 Gbps data rate. This is not the GDDR6 or GDDR6X found in gaming cards; LPDDR5 is chosen for its lower power consumption and tighter integration with the system-on-module design.

For high-resolution workloads, the 102.4 GB/s bandwidth is a limiting factor. At 4K resolutions, texture streaming and framebuffer operations demand substantially more bandwidth than this provides, and the data suggests that the Orin NX would struggle to maintain smooth frame rates in modern titles at such settings. However, the 128-bit bus width combined with the 8 GB capacity offers a balanced profile for embedded applications where memory efficiency is more critical than raw throughput. The LPDDR5 type also contributes to the overall power budget, allowing the 20 W TDP to be maintained even under memory-intensive loads.

Benchmark results indicate that the memory subsystem is optimized for the tensor core workloads that dominate edge AI applications. Neural network inference often requires repeated access to weights and activations, and the 102.4 GB/s bandwidth is sufficient for models that fit within the 8 GB frame buffer. The 128-bit interface is narrow by desktop standards, but it is appropriate for the compact 70 mm by 45 mm module dimensions. For users attempting to push this GPU into high-resolution gaming, the memory bandwidth will be the first bottleneck encountered, more so than the FP32 compute.

Who Should Consider It

The Jetson Orin NX 8 GB is not a conventional gaming GPU, and the benchmark data makes that clear. Its 1.567 TFLOPS of FP32 performance and 102.4 GB/s bandwidth place it in a category where 1080p gaming at medium to low settings is the realistic ceiling. Users expecting high-refresh-rate or high-fidelity experiences should look elsewhere, as the data does not support such expectations.

The intended audience is embedded systems developers and edge AI practitioners. The FP16 throughput of 3.133 TFLOPS, combined with 32 tensor cores, makes this module suitable for real-time inference tasks such as object detection, natural language processing, and computer vision applications. The 20 W power envelope allows for passive cooling in many cases, and the IGP slot width means it can be integrated into custom carrier boards without the space constraints of discrete GPUs. The 8 GB LPDDR5 memory is adequate for running medium-sized neural network models, and the PCIe 4.0 x4 interface provides sufficient host communication for most embedded scenarios.

For resolution-specific guidance, the data suggests that 720p gaming is feasible for less demanding titles, while 1080p requires significant compromises in settings. The 12.24 GPixel/s pixel rate translates to roughly 12 million pixels per second, which is insufficient for 4K at playable frame rates. This GPU should be considered for AI acceleration and low-power graphics, not as a primary gaming solution. The 50th percentile ranking means it outperforms half of all GPUs in the database, but those comparisons likely include many older integrated graphics solutions.

FAQ

Q: What is the thermal design power of the Jetson Orin NX 8 GB?

A: The TDP is 20 W, which allows for passive cooling in many embedded applications.

Q: What is the maximum memory bandwidth available on this GPU?

A: The memory subsystem provides 102.4 GB/s of bandwidth via an 8 GB LPDDR5 configuration on a 128-bit bus.

Q: Does the Jetson Orin NX 8 GB support DirectX 12 Ultimate?

A: Yes, it supports DirectX 12 Ultimate (12_2), along with OpenGL 4.6 and Vulkan 1.4.

Q: What is the production status of this module?

A: The production status is listed as end-of-life, though the launch MSRP was 399 USD.

Q: How many tensor cores does this GPU contain?

A: It contains 32 tensor cores, which support the FP16 throughput of 3.133 TFLOPS.

Q: What is the physical size of the module?

A: The dimensions are 70 mm in length and 45 mm in height, equivalent to 2.8 inches by 1.8 inches.

Ray Tracing and Feature Set

The ray tracing capabilities of the Jetson Orin NX 8 GB are minimal based on the data provided. There are no dedicated RT cores listed in the fact pack, which means ray tracing workloads would fall to the 1024 shading units. The DirectX 12 Ultimate support includes ray tracing APIs, but the hardware lacks specialized acceleration for this task. Benchmark results would likely show poor ray tracing performance, though no specific scores are available in the database.

The feature set is instead built around the 32 tensor cores and the Ampere architecture. The FP16 compute of 3.133 TFLOPS is the headline specification, and it is clear that the design intent is AI inference and machine learning at the edge. The Vulkan 1.4 support is noteworthy for its currency, and OpenGL 4.6 provides broad compatibility for legacy applications. The display outputs are listed as portable device dependent, meaning the module does not have fixed display connectors but rather relies on the carrier board to provide them.

The API support for DirectX 12 Ultimate indicates that the hardware is theoretically capable of handling modern graphics features like mesh shaders and variable rate shading, but the compute resources are too limited for practical use. The 24.48 GTexel/s texture rate and 12.24 GPixel/s pixel rate are the limiting factors for any graphics workload. This is an accelerator first and a renderer second, and the feature set reflects that prioritization.

Power and Cooling

The power characteristics are straightforward: the TDP is 20 W, and the slot width is IGP, meaning it is an integrated GPU module rather than a discrete add-in card. There is no suggested PSU listed and no power connectors specified, which is typical for embedded modules that draw power from the carrier board. The 20 W envelope is the defining constraint, and it enables compact system designs without dedicated cooling solutions.

The 8 nm Samsung process contributes to this efficiency, as does the 200 mm² die size. The fact that no power connector is required underscores the module's embedded nature; it is not designed to be slotted into a desktop PCIe slot but rather soldered or mounted onto a custom board. The PCIe 4.0 x4 interface itself draws minimal additional power, and the memory clock of 800 MHz (6.4 Gbps effective) is conservative, further reducing power draw.

For cooling, the 20 W TDP can be managed with a passive heatsink in well-ventilated enclosures, though active cooling might be necessary for sustained tensor core loads. The dimensions of 70 mm by 45 mm allow for flexible placement within a system chassis. The end-of-life production status suggests that systems designers should plan for alternative solutions in new designs, but existing deployments can continue to rely on the stated power characteristics.

How It Compares

The nearest rivals data is empty in the fact pack, which prevents direct percentage comparisons. However, the 50th percentile ranking provides a general reference point. Against typical integrated GPUs, the Orin NX 8 GB would show a significant advantage in FP16 compute due to its tensor cores, while its FP32 performance would be competitive with high-end integrated solutions. The 20 W power draw is substantially lower than any discrete GPU, making it a more efficient choice for power-constrained environments.

Against low-end discrete GPUs from the same era, the Orin NX would trail in raw rasterization but lead in AI inference workloads. The lack of RT cores places it behind any competitor with hardware ray tracing, and the 102.4 GB/s bandwidth is roughly half of what entry-level discrete cards typically offer. The 8 GB LPDDR5 memory is generous for an embedded part, however, and the PCIe 4.0 x4 interface is modern enough for most embedded applications.

The production status of end-of-life is a critical consideration. It means the module is no longer being manufactured, though the 399 USD launch MSRP placed it in a competitive bracket for embedded AI modules. The 50th percentile performance ranking is respectable for a 20 W part, but the lack of benchmark data in the database makes it difficult to position against specific rivals. Ultimately, the Jetson Orin NX 8 GB occupies a niche where power efficiency and tensor performance matter more than any other metric, and within that niche, the data shows a capable, if specialized, solution.