Jetson T5000 vs T4000: What Is the Difference?

The Jetson T4000 and Jetson T5000 are the two production modules in the NVIDIA Jetson Thor family, the generation after Jetson Orin. Both are built on the NVIDIA Blackwell GPU architecture with Arm Neoverse-V3AE CPU cores and LPDDR5X memory, and both are aimed at generative and physical AI at the edge: humanoid and mobile robots, autonomous machines, and vision-language models that read a scene as well as detect objects in it.

The T5000 is the full Thor configuration and is the module on the Jetson AGX Thor developer kit. The T4000 is a cut-down version with fewer GPU and CPU cores, half the memory and a lower power ceiling. They share the Thor module form factor and connector and run the same JetPack 7 software, so a design can usually move between them as the workload becomes clear.

The table sets out the headline differences. AI performance is quoted at FP4 precision with sparsity, as NVIDIA quotes it for Blackwell.

Taken together, the T5000 has roughly 1.7 times the AI compute and twice the memory of the T4000, at up to nearly twice the power. Check NVIDIA’s module datasheets for interface counts, which also differ between the two.

Memory is often the deciding factor. Large language and vision-language models need the weights to sit in memory, and 128 GB allows larger models, longer context or several models side by side. Where the model fits comfortably in 64 GB, the T4000 gives Blackwell performance at a power level that is much easier to cool. Power matters too: a 130 W module needs a substantial heatsink or active cooling, and a vehicle or robot battery has to supply it for the whole shift. Benchmark the real model on both modules before fixing the choice.

Thor figures are not directly comparable with Orin. NVIDIA quotes AGX Orin at up to 275 INT8 TOPS, while Thor is quoted in FP4 TFLOPS, a lower precision that Blackwell Tensor Cores support natively. The real gains come from the newer GPU, a much faster CPU and far more memory, which together let Thor run transformer models that AGX Orin cannot hold or run in real time. Our white paper on how Jetson Thor advances edge AI beyond AGX Orin covers the architecture, and our FAQ on what TFLOPS means explains why precision changes the figure.

For classic detection and tracking on a few cameras, Jetson AGX Orin computers remain a lower-power and well-proven choice. Thor makes most sense where the application is moving to generative models or needs far more compute per system.

Thor is designed for systems that have to understand a scene and act on it, often with several models running together on one computer.

A Thor system is a platform change rather than a drop-in upgrade. The carrier board, power supply and cooling all have to suit the chosen module and power mode, and the software moves to JetPack 7. Our Jetson Thor computers and Jetson Thor carrier boards are offered for either module, as edge AI box PCs, AI NVRs and bare carriers with high-speed networking for many cameras.

We build Jetson Thor systems with the module, carrier, storage, cooling and JetPack image fitted as standard, and validate each in its power mode before it ships. Tell us your model sizes, sensors and power budget, and we will recommend the T4000 or T5000 to match.