300種類以上のAIモデルをより簡単で迅速に

In most cases, you can use the RBLN SDK with minimal code changes.

• For officially supported Model Zoo models, you can use the provided example code right away.
• Other models can also be compiled by referring to the Model Zoo code.

Check the list of supported operations in advance:

To maximize the performance of transformer-based models, consider the following:

• Set the rbln_tensor_parallel_size value appropriately to utilize NPU parallelism
• Tune the input sequence length and batch size

The RBLN SDK provides a C/C++-bound runtime for applications where Python runtime is unavailable or extremely low latency is required.
Please refer to the C/C++ guide for more information.

The RBLN SDK and Compiler are regularly updated to maintain API compatibility with the latest versions of major frameworks.
For details, please refer to the respective Release Notes.

RBLN SDK offers high compatibility with PyTorch-based models.

• torch.compile() Support: Fully compatible with PyTorch 2.0’s torch.compile() feature, and supports models compiled using TorchDynamo and TorchInductor backends.

• Extensive Operator Support: The RBLN Compiler supports most PyTorch operators. You can check the full list in Supported Ops. It also includes major operators for Vision, NLP, and Audio, making it suitable for a wide range of deep learning models.

• PyTorch Model Zoo Compatibility: Popular models such as ResNet, YOLO, LLaMA, and BERT are supported. See the PyTorch Model Zoo page for more details.

• JIT/Scripted Model Support: Models converted using TorchScript can also be processed by the RBLN Compiler.

The RBLN Driver can be installed using the provided deb or rpm installation files and requires root privileges. During installation, you must ensure that the kernel version is compatible with the driver.

The RBLN Driver can be installed using the provided deb or rpm installation files and requires root privileges. During installation, you must ensure that the kernel version is compatible with the driver.

Python 3.9 or higher is recommended, and there are key package dependencies such as numpy, torch, and onnx.

Please refer to the Support Matrix page for the supported OS and Python versions.
Required packages may vary by model, so refer to the requirements.txt file included in the Model Zoo code for details.

Currently, RBLN SDK only supports Linux. Windows support will be determined based on our technical roadmap.

The RBLN SDK supports distributed inference based on tensor parallelism, called RSD (Rebellions Scalable Design).
Please first check the Model List that support multi-device, and refer to the provided example for compilation instructions.

You can analyze metrics such as latency, throughput, and memory usage using the Profiler included in the SDK.

With rbln-stat, you can also monitor power consumption and utilization.

The optimal batch size may vary depending on the type of NPU used, server configuration, and service requirements.
We recommend using the Profiler tool and conducting various experiments for fine-tuning.

RBLN SDK includes the RBLN Profiler for performance bottleneck analysis, collecting key metrics such as execution time, memory usage, and operation dependencies

• .pb format trace files can be visualized with Perfetto. - You can analyze bottlenecks, inter-operation dependencies, and layer-by-layer latency to suggest optimization directions. For detailed usage, refer to the Profiler Guide.

To process video files, you can use libraries like OpenCV (cv2) to extract each frame from an .mp4 file as an image, and then feed those frames into the model for inference.

For example, when using an object detection model like YOLOX, the typical procedure is as follows:

1.Load the video file using cv2.VideoCapture
2.Extract frames one by one
3.Preprocess each frame to match the model’s input format
4.Perform object detection using the model
5.Visualize the results and either save them or display them in real time

Both are AI inference NPUs developed by Rebellions, but REBEL is a next-generation product designed with a chiplet-based architecture. A detailed comparison chart is available on the product page.

Yes. You can use Rebellions AI processor resources via the Kubernetes Plugin.

• Kubernetes Device Plugin: Supports RBLN NPUs on Kubernetes cluster environment.
• NPU NPU Feature Discovery: Labels Kubernetes nodes with RBLN NPUs for scheduling.
• RBLN Metrics Exporter: Exposes NPU metrics (temperature, power, DRAM, utilization) in Prometheus format for Grafana dashboards.

The V1 Engine improves generation and multimodal models. Enable it by setting:

export VLLM_USE_V1=1

RBLN SDK is compatible with vLLM, Nvidia Triton Inference Server, and TorchServe. Container-based deployment also supports integration with Kubernetes.

Rebellions devices are designed exclusively for inference, and fine-tuning is not currently supported.

To maximize inference performance, we recommend the following optimization strategies:

• Use Mixed Precision and Quantization: Improve memory efficiency and compute speed by using FP16 or INT8 quantized models.
• Adjust Batch Size: Find the optimal batch size based on model characteristics and input data to increase throughput.
• Refactor Model Architecture: Simplify the computation graph through layer fusion and removal of redundant operations to boost performance.
• Double Buffering: Utilize double buffering in AsyncRuntime to improve execution efficiency.
• Apply Continuous Batching for LLM Serving: For large language model (LLM) serving, maximize hardware utilization by applying continuous batching techniques using vllm-rbln.

You can ask questions or discuss technical issues on Rebellions Dev Forum. You can directly reach out to us here.

The SDK is updated approximately every month, and the driver is updated every three months, although the schedule is subject to change.

Currently, for officially supported models listed in the RBLN Model Zoo, you can use the provided compilation and inference example code.

If you’re using a modified model or a model not included in the Model Zoo, technical support may be limited, and compilation may fail.
First, check the error code to identify the cause. If further assistance is required, please reach out via the Rebellions Dev Forum.

Please check the following items:

• Memory Usage: If the system runs out of memory during compilation, the process may fail.
• NPU Configuration: Ensure that the value of rbln_tensor_parallel_size is not greater than the actual number of devices installed in your system. You can verify the number of devices by running the rbln-stat command in your terminal.
• Docker Environment: Refer to the Docker Guide for more details.

You can limit the number of CPU threads used during inference by setting the RBLN_NUM_THREADS environment variable. Specifying an appropriate number of threads can reduce CPU load and help stabilize performance.

Pleae refer to this document for more details.

Issues may arise due to version mismatches between the driver and compiler.

• Refer to the Release Notes of the RBLN SDK to ensure that all components are installed with compatible versions.
• After aligning all libraries to their compatible versions, try recompiling the model.