Evaluating Edge AI Inference on LattePanda Sigma with AX650N NPU

As demand for local AI inference continues to grow at the edge, a key engineering challenge is how to efficiently deploy large language models (LLMs) and multimodal models on general-purpose x86 platforms.

LattePanda Sigma, powered by the Intel Core i5-1340P, combined with the AX650N NPU accelerator, provides a new hardware option for local inference on x86 systems.

The LattePanda Sigma combined with the AX650N NPU was evaluated under practical local AI inference workloads. Testing focused on hardware resources, NPU memory limits, model compatibility, and inference behavior across different model sizes and quantization schemes, with particular attention to LLM deployability, performance, power efficiency, and runtime stability.

Several typical edge AI use cases were also examined to better understand the practical limits and real-world value of this setup, providing reference points for system design and hardware selection.

1.LattePanda Sigma Hardware Performance

High-End Performance in a Compact Form Factor

LattePanda Sigma is positioned as a compact x86 single-board computer with near-desktop-class performance. Its hardware configuration supports not only daily computing tasks but also high-load AI inference workloads.

1.1 Core Specifications and Performance Benchmarks

1.2 Configuration Options and Selection Guidance

LattePanda Sigma is available in four official configurations:

2.AX650N NPU

The Core Engine for Edge AI Acceleration

The AX650N from Axera can be used as a dedicated AI acceleration module for LattePanda Sigma. It integrates high-efficiency NPU, ISP, and video codec units, designed specifically for edge and multimodal AI workloads.

2.1 AX650N Hardware and Software Capabilities

• NPU architecture: Supports W8A16 quantization (8-bit weights, 16-bit activations) with dedicated Transformer acceleration units for attention-based models.

• Auxiliary processing:

        8-core Cortex-A55 CPU for task scheduling

        8K@30fps ISP for image preprocessing

        H.264 / H.265 VPU capable of decoding up to 32 channels of 1080p video

• Developer support:

        Open-source ax-llm project with precompiled model examples

        Axclhost toolkit (v3.6.2, 75.21 MB)

        Compatible with Hugging Face models, reducing deployment effort

3.Model Compatibility and Performance Testing

All tests were conducted on Ubuntu 22.04 with Python 3.10.12, using both 8GB and 16GB AX650N variants.

3.1 LLM Inference Results

3.2 Multimodal Model Inference Results

3.3 Key Findings

• The main advantage of 16GB NPU memory is support for larger models, not a dramatic increase in single-model speed.
• 7B LLMs (INT4) can only run on the 16GB NPU; the 8GB version runs out of memory.
• For models ≤2B parameters, speed differences between 8GB and 16GB are typically 5–15%.
• Tokens per mWh is generally better on the 16GB version, suggesting reduced memory access overhead.
• Multimodal models (e.g. Qwen3-VL) are more memory-sensitive, making 16GB the practical minimum.

3.4 Vision Model Testing (YOLO)

Tests focused on YOLO object detection models using AX650N 16GB:

3.5 Comparison with Other Boards

YOLOv8n inference performance comparison:

4.Summary and Outlook

Based on the test results, LattePanda Sigma paired with the AX650N NPU forms a practical heterogeneous x86 platform for validating local AI inference. By coordinating CPU, memory, and NPU resources, the system can handle workloads from lightweight vision models to mid-sized LLMs, showing clear feasibility for edge AI deployment.

Under appropriate model and quantization settings, the NPU can offload inference from the CPU, improving power efficiency and response time. However, performance remains strongly influenced by model structure, quantization strategy, and the maturity of the current software stack.

From an engineering standpoint, this setup is best suited for inference validation and small-scale deployments rather than as a general CPU or GPU replacement. Model size limits, stability, and efficiency on the NPU still require careful evaluation on a per-model basis.

With continued progress in model optimization and improvements to NPU firmware and inference frameworks, the overall usability of the Sigma + AX650N combination is expected to improve. For now, its most realistic role is a controlled, low-power platform for exploring edge AI inference limits and validating real-world deployment strategies, rather than a universal solution.