Jetson Orin Nano 2 vs Orin Nano Super: What Changed?
NVIDIA announced Jetson Orin Nano 2 on August 25, 2026 as its next entry-level robotics and edge AI computer. It is not a product you can buy today: NVIDIA says the module and developer kit are expected in the first half of 2027.
That availability boundary matters. Jetson Orin Nano Super is shipping hardware with a mature developer ecosystem. Nano 2 is an announced successor with promising specifications and vendor-reported performance claims, but no independent benchmarks, confirmed price, or final public deployment record yet.
This comparison separates what NVIDIA has announced from what developers can use now.
Jetson Orin Nano 2 at a glance
NVIDIA’s announcement specifies:
- up to 78 TOPS of AI compute;
- 8 GB of memory;
- an 8-core Arm CPU;
- the same compact form-factor positioning as Orin Nano Super;
- expected module and developer-kit availability in H1 2027.
NVIDIA positions Nano 2 for robots, delivery and inspection drones, vision AI systems, and other power-constrained edge applications. The company also says its software stack supports memory-efficient edge inference for models including NVIDIA Cosmos and Nemotron, Gemma 4, and Qwen 3. That is an announced compatibility and optimization claim, not evidence that every model family, checkpoint, precision, or context length will perform equally well.
Nano 2 vs Orin Nano Super
| Specification | Jetson Orin Nano 2 | Jetson Orin Nano Super |
|---|---|---|
| Product status | Announced; not currently shipping | Shipping |
| Expected availability | H1 2027, according to NVIDIA | Available now |
| Price | Not announced | $249 developer kit |
| Announced AI compute | Up to 78 TOPS | Up to 67 TOPS |
| Memory | 8 GB | 8 GB LPDDR5 |
| CPU | 8-core Arm CPU | 6-core Arm Cortex-A78AE |
| Inference claim | NVIDIA claims roughly 2× Nano Super inference performance | Current-generation baseline in NVIDIA’s comparison |
| Power evidence | NVIDIA reports 40% less power at equivalent performance in a 15W comparison; its Jetson overview also describes a 40W power envelope | Configurable Orin Nano power modes; actual draw depends on workload and mode |
| Form factor | NVIDIA says the same compact form factor | Current Orin Nano developer-kit form factor |
| Best fit today | Evaluation for projects targeting 2027 deployment | Development and deployment that need hardware now |
The TOPS difference alone does not explain NVIDIA’s claimed inference uplift. Nano 2 moves from 67 to 78 TOPS on the headline figures, while NVIDIA attributes the larger application-level gain to improved Tensor Cores and higher memory bandwidth. The announcement does not publish enough workload-by-workload data to translate that claim into camera FPS, VLM token rates, robot-policy frequency, or end-to-end latency.
What NVIDIA’s performance claims mean
NVIDIA says Jetson Orin Nano 2 achieves twice the inference performance of Orin Nano Super. Until reproducible benchmark details and shipping systems are available, treat that as a vendor claim rather than a general rule.
Inference performance depends on:
- the model and precision used;
- whether operators are accelerated by TensorRT or another supported runtime;
- memory traffic and context size;
- camera decoding and preprocessing;
- concurrent ROS 2, perception, planning, and control workloads;
- thermal limits and selected power mode.
A two-times result for an optimized model does not mean every existing CUDA, VLM, or robot-policy workload will run twice as fast. It also does not establish end-to-end robot responsiveness, because sensors, middleware, planning, actuators, and safety loops contribute their own latency.
The power claim needs similar care. NVIDIA says Nano 2 consumes 40% less power to deliver the same performance as its predecessor in a 15W comparison. This is not the same as saying every Nano 2 workload always consumes 40% less energy. NVIDIA’s Jetson overview separately describes a 40W power envelope, so developers should wait for final power-mode documentation and workload-specific measurements before sizing batteries or thermal systems.
What stays similar
Both products occupy NVIDIA’s entry-level Jetson position and target compact embedded systems. Nano 2 retains 8 GB of memory rather than moving to a larger-capacity tier. That means the new device may process supported models faster without eliminating the memory constraints that affect larger VLMs and robot foundation models.
The software ecosystem is also intended to remain a core advantage. Jetson development centers on JetPack, CUDA, TensorRT, DeepStream, and Isaac ROS. NVIDIA has not yet published all Nano 2 production details, so teams should not assume that every carrier board, camera configuration, binary, container, or custom device image will transfer without validation.
For the wider market, see the edge computers for robotics comparison. It covers where Jetson, Raspberry Pi accelerators, and x86 mini PCs differ today.
What matters for robotics workloads
Onboard perception
Robots and drones often run detection, tracking, depth estimation, segmentation, and visual odometry together. More effective inference throughput can create room for a stronger model, another camera stream, or additional perception stages. Real gains will depend on the exact pipeline, not the TOPS figure alone.
Vision-language inference
An 8 GB edge device must fit model weights, runtime state, visual inputs, caches, and the rest of the robot software in the same memory budget. NVIDIA’s stated support for memory-efficient Cosmos, Nemotron, Gemma 4, and Qwen 3 deployments suggests a focus on compressed or optimized models. It does not mean unrestricted versions of those model families fit in 8 GB.
Robot control and perception stacks
Learned policies normally sit above deterministic motor and safety loops. Faster model inference may improve high-level perception or action-chunk generation, but Nano 2 should not be treated as a replacement for real-time control hardware. Teams still need to measure worst-case latency and define safe behavior when an AI process stalls or fails.
Drones and battery-powered systems
Performance per watt can matter more than peak TOPS in a flying or mobile platform. NVIDIA’s 15W efficiency comparison is relevant here, but design decisions should wait for shipping hardware, sustained-load measurements, thermal data, and a complete power-mode specification.
The physical AI technology stack explains how onboard compute connects to sensors, middleware, policies, and deployment operations.
Model support: what is actually confirmed
NVIDIA names Cosmos, Nemotron, Gemma 4, and Qwen 3 in its Nano 2 announcement. The confirmed claim is that the Jetson software ecosystem is intended to run optimized, memory-efficient edge inference for models from those families.
The announcement does not provide a complete matrix of:
- supported checkpoints and parameter counts;
- quantization formats;
- prompt or context limits;
- vision resolution;
- tokens or frames per second;
- memory use alongside a full robot stack.
Developers should therefore wait for Nano 2-specific model documentation and reproducible examples. For comparison, NVIDIA’s documented 4B Cosmos3-Edge-Policy-DROID workflow currently targets the much larger Jetson AGX Thor T5000, not Orin Nano. The Cosmos 3 Edge deployment analysis explains that hardware and memory boundary.
Should you wait for Jetson Orin Nano 2?
Use Orin Nano Super now if
- your robot or prototype needs hardware before H1 2027;
- 67 TOPS and 8 GB are sufficient for the validated workload;
- current carrier-board, camera, container, and JetPack compatibility matter;
- project risk matters more than an announced performance uplift.
Nano Super remains the practical reference because it is shipping and developers can test it now. Its limitations are measurable rather than hypothetical.
Consider waiting for Nano 2 if
- your deployment schedule starts in or after H1 2027;
- higher edge inference throughput could remove a pipeline bottleneck;
- performance per watt is important for a mobile robot or drone;
- an 8 GB memory ceiling is acceptable;
- your team can wait for price, final documentation, availability, and independent testing.
Do not delay a viable 2026 prototype solely because Nano 2 has been announced. A software stack validated on Nano Super can provide useful evidence now, while hardware qualification for Nano 2 can become a separate step when systems ship.
Practical verdict
Jetson Orin Nano 2 is a credible roadmap improvement for entry-level robotics compute: NVIDIA has announced 78 TOPS, an 8-core Arm CPU, 8 GB of memory, and a substantial inference and efficiency uplift over Nano Super. The important unknowns are price, final platform details, workload-specific performance, and real availability.
For teams buying today, Orin Nano Super remains the deployable option. For teams targeting 2027, Nano 2 is worth tracking—but it should remain marked announced, expected H1 2027, and not currently shipping until NVIDIA confirms availability.
Frequently asked questions
Is Jetson Orin Nano 2 available now?
No. NVIDIA announced it on August 25, 2026 and says the module and developer kit are expected in the first half of 2027.
How much will Jetson Orin Nano 2 cost?
NVIDIA had not announced a price as of August 26, 2026. Do not use the Nano Super price as a Nano 2 estimate.
Is Nano 2 twice as fast as Nano Super?
NVIDIA claims roughly twice the inference performance through improved Tensor Cores and higher memory bandwidth. Independent, workload-specific benchmarks are not yet available, so this should not be generalized to every robotics pipeline.
Does Nano 2 have more memory?
No increase has been announced: both the Nano 2 announcement and the common Nano Super configuration specify 8 GB. Effective model capacity also depends on precision, runtime memory, caches, and other software running on the device.
Can Nano 2 run Cosmos models?
NVIDIA lists Cosmos among the model families supported through optimized, memory-efficient edge inference. The announcement does not specify that every Cosmos checkpoint or robotics policy runs on Nano 2. The published Cosmos3-Edge-Policy-DROID reference targets Jetson AGX Thor.
Sources
- NVIDIA announces Jetson Orin Nano 2 — official specifications, attributed performance claims, model positioning, and expected H1 2027 availability
- NVIDIA Jetson platform overview — current Jetson lineup, Nano 2 positioning, and power-envelope language
- NVIDIA Jetson Orin Nano Super Developer Kit — current Nano Super product specifications and availability
- NVIDIA Orin Nano Super performance update — official background on the Super configuration