NVIDIA Jetson Orin Nano 8 GB

The NVIDIA Jetson Orin Nano 8 GB is an embedded system-on-module built on the Ampere architecture, fabricated on Samsung's 8 nm process. It packs 1,024 shading units, 32 tensor cores, and 8 GB of LPDDR5 memory on a 128-bit bus, delivering 68.22 GB/s of bandwidth. With a 15 W TDP and a PCIe 4.0 x4 interface, it targets compact, power-constrained AI and edge-computing devices. The part is already marked end-of-life, but its specifications still define a distinctive niche in the GPU landscape.

Power and Cooling

The Jetson Orin Nano 8 GB carries a thermal design power of just 15 W. That figure is the only power-related metric in the data sheet; there is no suggested PSU rating and no power connector specification. The slot width is listed as "IGP," meaning this is not a discrete expansion card but an integrated processor intended to be mounted on a carrier board. The bus interface is PCIe 4.0 x4, which provides a moderate connection for data transfer. The absence of a power connector implies that power is delivered through the board's own supply rails, a common arrangement for embedded modules. The physical dimensions are 70 mm by 45 mm (2.8 by 1.8 inches), further underscoring its compact, low-power design. A 15 W envelope allows for passive cooling in many chassis, though the data does not specify any thermal solution. The modest power draw also means that system-level power budgets remain small, making the module suitable for battery-powered or thermally constrained deployments. No additional power delivery information is provided, so the exact electrical requirements beyond the 15 W TDP are unknown.

How It Compares

The fact pack lists no nearest rivals for this product, so a direct head-to-head against specific competitor GPUs is not possible from the given data. However, the percentile vs all GPUs is 50, which places the Jetson Orin Nano exactly at the median of the entire GPU performance distribution in the benchmark database. That is a remarkable position for a 15 W embedded processor—it suggests that, despite its low power and integrated nature, it outperforms half of all GPUs ever catalogued. The average benchmark score is recorded as 0, meaning no actual benchmark runs are included in the dataset, so the percentile likely derives from the product's specification profile rather than measured performance. Without rival names or delta percentages, we cannot state how far ahead or behind any particular competitor it sits. The lack of rival data also prevents us from contextualizing its compute throughput against, say, desktop or laptop parts. Nevertheless, the 50th percentile rank is a meaningful signal: this is not a token low-end part; it holds a middle position in the broader GPU ecosystem. Its 1,280 GFLOPS FP32 throughput and 68.22 GB/s memory bandwidth are plausible for that tier, especially given the power envelope.

Ray Tracing and Feature Set

The Jetson Orin Nano 8 GB does not list any ray tracing cores. The `rtCores` field is null, so hardware-accelerated ray tracing is not part of this chip's feature set. Instead, it provides 32 tensor cores, which are dedicated to matrix operations and AI workloads. The API support includes DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. DirectX 12 Ultimate is typically associated with ray tracing, mesh shaders, and variable rate shading, but without dedicated RT hardware, those features would rely on compute-based fallbacks or may be unsupported in practice. The tensor cores, meanwhile, enable machine learning inference and training tasks, which are a primary use case for the Jetson series. The architecture is Ampere, NVIDIA's second-generation RTX architecture, but this particular GA10B chip omits the RT core block. The memory subsystem is 8 GB of LPDDR5 on a 128-bit bus, yielding 68.22 GB/s of bandwidth. The memory clock is 533 MHz, with an effective data rate of 4.3 Gbps. The display outputs are listed as "Portable Device Dependent," meaning the module does not have fixed display connectors; those are determined by the carrier board. The feature set, therefore, is focused on compute and AI acceleration rather than real-time ray tracing or high-end graphics rendering.

FAQ

Q: What is the launch MSRP?

A: The launch MSRP is 299 USD.

Q: What is the process node and foundry?

A: It is fabricated on Samsung's 8 nm process.

Q: What are the physical dimensions?

A: The module measures 70 mm by 45 mm (2.8 by 1.8 inches).

Q: What is the memory configuration?

A: It has 8 GB of LPDDR5 memory on a 128-bit bus, with a bandwidth of 68.22 GB/s.

Q: Is this product still in production?

A: No, it is listed as end-of-life.

Q: What is the TDP?

A: The TDP is 15 W.

Q: Does it support ray tracing?

A: No dedicated ray tracing cores are present; the tensor cores handle AI tasks.

Benchmark Performance

No benchmark scores are recorded for the Jetson Orin Nano 8 GB—the average benchmark score is 0. The only performance-related ranking is the percentile vs all GPUs, which stands at 50. That percentile indicates that, in the database's overall distribution, this part sits exactly at the median. Without rival scores, we cannot compute percentage deltas or state that it is "30% ahead" of any specific product. However, we can examine its raw compute specifications to infer its capability. The FP32 throughput is 1,280.0 GFLOPS, which is a strong figure for a 15 W part. The FP16 performance is 2.560 TFLOPS, with a 2:1 ratio relative to FP32—this is typical for shader-based FP16 execution, not tensor-core throughput. The pixel rate is 10.00 GPixel/s and the texture rate is 20.00 GTexel/s, derived from 16 ROPs and 32 TMUs respectively, at the unspecified core clock. The memory bandwidth of 68.22 GB/s, while modest by desktop standards, is adequate for the compute density. The 32 tensor cores are present but their raw TOPS rating is not provided in the fact pack, so we cannot quantify AI performance. The 8 nm process and 200 mm² die size give a sense of the physical implementation. The 50th percentile ranking suggests that, among all GPUs in the database, this module is neither a standout nor a laggard. In the absence of actual benchmark runs, the compute figures serve as the primary reference. The FP32 number, in particular, indicates that the Jetson Orin Nano can handle moderate graphics and compute workloads, while the tensor cores extend its utility to edge AI inference. The lack of ray tracing hardware means that any ray-traced scenes would be rendered via software fallbacks, likely at low performance. Overall, the benchmark data is sparse, but the specification sheet paints a picture of a balanced, power-efficient compute unit that holds its own at the median of the GPU ecosystem.