TiRex wearing a sweater sits at a desk using a laptop, with a blue background showing an LLM workflow diagram featuring planning, routing, web browsing, and worker agents

TiRex on the Edge

[ Hardware ]
[ Blog ]

Time series are everywhere, shaping our everyday lives—both professionally and privately. That's why time series models need to run quickly and reliably on many end devices, delivering predictions and classifications. But not every foundation model for time series is edge-capable. In our Edge Lab, we analyze the models, deploy them on various devices, and measure their performance and speed. After all, the industrial reality is PLCs or less powerful devices, and our goal is to find out how well foundation models perform on existing hardware.

Management Summary:

  • TiRex is faster than Chronos-2 in inference and requires less energy. The forecast quality is only slightly worse. 

  • TiRex is the best model when considering prediction quality (CRPS) and the ratio between latency and energy consumption. This makes TiRex ideal for industrial applications.

Test devices are:

Table listing six tested edge/industrial computing devices with their processors, RAM, and testing configuration. Devices include: Beckhoff C6015 with Intel Atom x6416RE (4 cores) and 8 GB RAM; KEBA Industrial PC with Intel Core i7-6600U (dual core) and 16 GB RAM; Bosch Rexroth ctrlX COREplus X3 with Zynq Ultrascale+ (4× ARM A53) and 2 GB RAM; Raspberry Pi 5 with Arm Cortex-A76 (4 cores) and 16 GB RAM; NVIDIA Jetson Orin Nano Super with Arm Cortex-A78AE (6 cores) and 8 GB RAM; and AMD Kria KR260 with Zynq UltraScale+ MPSoC EV (XCK26) and 4 GB RAM. All devices were tested on CPU, except the Jetson Orin Nano Super which was tested on CPU and CUDA.

Important: This list is an initial selection and can be expanded as needed, which it will be. Anyone who wants to have their hardware tested is welcome to do so.

The RAM range from 2 GB to 16 GB is striking. Our TiRex model runs smoothly on all devices, but how does it perform compared to its competitors? We compare TiRex on the CPU with Chronos-2, TimesFM-2.5, and PatchTST-FM. The hardware is the industrial PC from KEBA.

The forecasting assumptions:

batch size: 1 (one series at a time)
prediction length: 32 steps
context: 2048 steps

The Results:

Comparison table of time-series forecasting models (NX-AI/TiRex, Amazon Chronos-2, Google TimesFM-2.5, and IBM PatchTST-FM) showing CRPS accuracy, throughput, latency, and energy consumption, with TiRex achieving the highest throughput and lowest energy use.

Bar charts comparing four time-series models (NX-AI/TiRex, Amazon Chronos-2, Google TimesFM-2.5, and IBM PatchTST-FM) across CRPS, throughput, latency, energy consumption, and energy efficiency, showing TiRex with the highest throughput and best energy efficiency.

KEBA Industrial PLC

Comparison table of time-series forecasting models showing relative performance vs. NX-AI/TiRex baseline. TiRex scores highest in throughput, latency, and energy efficiency, while Chronos-2, TimesFM-2.5, and PatchTST-FM show lower throughput and significantly higher latency and energy consumption.

Scatter plot comparing time-series forecasting models by latency and CRPS accuracy. NX-AI/TiRex appears among the lowest-latency models while maintaining competitive CRPS, positioned near the Pareto frontier against models such as Chronos-2, TimesFM-2.5, PatchTST-FM, and Moirai.

Time Series Model Comparison on KEBA PLC

  • TiRex is faster than Chronos-2 in inference and requires less energy. The forecast quality is only slightly worse.

  • TiRex is the best model when considering prediction quality (CRPS) and the ratio between latency and energy consumption. This makes TiRex ideal for industrial applications.

Update TiRex 2

We are heavily working on TiRex 2, will be shipped in the next weeks.