Selection Guide for LattePanda Sigma x86 Windows / Linux Single Board Computer Server

LattePanda Sigma is a compact yet powerful x86 Windows/Linux single board server featuring an Intel Core i5-1340P processor and up to 32GB of dual-channel LPDDR5 memory. This versatile single board computer is ideal for homelabs, media servers, IoT, local LLM deployment, and edge computing.

To help you choose the configuration that best suits your needs, this article will delve into the main features of the LattePanda Sigma, explore the configuration options of each version, and provide selection recommendations for different user groups.

Key features of LattePanda Sigma include:

Ultimate Performance: The Intel Core i5-1340P processor boasts 12 cores and 16 threads, with a max turbo frequency of 4.60 GHz, providing robust power for multitasking and demanding applications. It is particularly well-suited for high-performance computing scenarios, such as virtual machines and AI LLM deployment.

Lightning-Fast Memory: Equipped with up to 32GB of dual-channel LPDDR5-6000 memory, ensuring smooth and speedy operation.

Discrete-Level Graphics: Integrated Intel Iris Xe Graphics performs on par with dedicated GPUs, supporting up to four 4K display outputs to meet high-resolution and high refresh rate needs.

Abundant Interfaces: Offers a variety of ports, including Thunderbolt 4 and M.2, to meet diverse connection requirements.

Compact and Portable: Size comparable to just two traditional mice, facilitating easy integration and transport.

Multi-OS Support: Compatible with Windows 10, Windows 11, Ubuntu, Proxmox, etc.

Image: LattePanda Sigma - x86 Windows / Linux Single Board Server

LattePanda Sigma Selection Guide

LattePanda Sigma offers 4 different configurations to meet various needs. All models feature an Intel Core i5-1340P processor.

The following table only lists the main differences between models. For complete specifications, please refer to the detailed spec sheet at the end of this article.

Choosing the most suitable model depends not only on current needs but also on potential future applications. If budget allows, opting for a higher-spec Model usually provides greater flexibility for future use.

Below are selection recommendations based on different scenarios and requirements for your reference.

Memory Options

- The memory chip of the LattePanda Sigma is directly soldered on the board, making it impossible to upgrade or replace on your own afterward. Therefore, we recommend carefully considering the memory size you require at the time of purchase.

- 16GB Memory Version:

- Suitable Users: Beginners using single-board computers, lightweight developers, single-service deployers
- Use Cases: General purposes such as basic programming, web browsing, Jellyfin media server, lightweight IoT projects, etc.
- Advantages: The 16GB version offers good value for money and can meet daily application needs. For general users, 16GB of memory is sufficient for basic development and testing work, making it suitable for constructing simple servers or streaming solutions.

Purchase Link：

LattePanda Sigma - The Small Hackable x86 Windows/Linux Single Board Computer Server (16GB RAM)

LattePanda Sigma - x86 Windows / Linux Single Board Computer Server (16GB RAM, 500GB SSD, WiFi 6E)

- 32GB Memory Version:

- Suitable Users: Corporate tech teams, Homelab enthusiasts, virtual machine or container users, AI and machine learning aficionados.
- Use Cases: Ideal for memory-intensive applications such as Homelabs, Proxmox virtual machines, VMware, and Docker containerized applications; especially recommended for AI and machine learning tasks.
- Advantages: Provides larger operational space, suitable for high-performance computing needs, and excels particularly in deploying large language models (LLM).

Purchase Link：

LattePanda Sigma - x86 Windows / Linux Single Board Computer Server (32GB RAM)

LattePanda Sigma - x86 Windows / Linux Single Board Computer Server (32GB RAM, 500GB SSD, WiFi 6E)

Guide For Deployment and Running AI Models on LattePanda Sigma

For users looking to run smaller-scale SLMs like phi3, gemma2, mathstral, and llama3.1 on LattePanda Sigma, our article Running SLM on SBC (LattePanda Sigma) can provide you with configuration guidance. 

For those planning to deploy more complex LLMs, such as LLaMA, Alpaca, LLaMA2, and ChatGLM, we strongly recommend opting for the 32GB version of LattePanda Sigma. Based on our testing, this configuration can significantly enhance performance. For detailed information, please refer to our article Deploy and Run LLM on LattePanda Sigma.

Additionally, if you are seeking detailed guidance on accelerating LLMs on LattePanda Sigma, refer to our tutorial article How to Use Optimum-Intel to Accelerate LLaMA 3.1 on LattePanda Sigma.

Image: Speed test of Llama2 running on LattePanda Sigma 32GB

Storage Options

· LattePanda Sigma supports two M.2 2280 NVMe SSDs

· Model without SSD:

- Suitable for: Users with compatible SSDs; those needing high-capacity or specific performance SSDs

- Highly recommended for: Deploying and running LLMs, which require large storage for model data, especially when storing multiple models. Consider buying 1TB+ SSDs separately

- Advantage: Flexible control over storage capacity and performance, can install OS yourself

· Model with SSD:

- Suitable for: Users wanting plug-and-play; those without compatible SSDs; single-board computer beginners

- Advantage: SSD pre-installed with Windows 11 Pro OS, saves installation hassle

Wireless Options

· LattePanda Sigma supports M.2 2230 WiFi modules

· Model without WiFi module:

- Suitable for users: 

- Users with specific network performance requirements, those who already have a high-performance WiFi module, and those who wish to choose a WiFi module based on project needs (such as enterprise tech teams or Homelab users) 

- Users who do not require wireless network connections (such as for local computing projects, embedded system development, etc.) 

- Advantages: 

Customers can flexibly choose the model of WiFi module they need, which is suitable for customized network performance configurations. List of compatible known WiFi models

· Model with WiFi module:

Suitable for users: 

Beginners using single-board computers, home media player users, etc. 

Advantages: 

- Pre-installed with WiFi 6 module and includes an antenna, eliminating installation hassle, allowing easy wireless connection and reducing installation steps 

- Enables quick wireless network connection to support streaming media or IoT project needs

FAQ

Q. Which SSDs are supported?

A. Two types are supported:

· NVMe SSDs in M.2 2280 form factor, with protocol support up to PCIe 4.0 x4.

· SATA SSDs in M.2 2280 form factor.

Q. Is there a capacity limit for SSDs?

A. Any commercially available SSD meeting the above specifications can be used.

Q. Which WiFi modules are supported?

A. WiFi modules in M.2 2230 form factor with PCIe x1 or CNVio protocols are supported, such as common models like Intel AX200, AX201, AX210, AX211. For a detailed compatibility list, please check the official documentation.

Q. What antenna does the WiFi module use?

A. Typically, a dual-band (2.4G and 5G) antenna with an IPEX4 port.

Q. Why is the 32GB memory frequency slower than the 16GB?

A. The 32GB memory chip has double the internal ranks, requiring a slight speed reduction for stability. This reduction has minimal impact on performance and can be disregarded.

Q. Does the memory support ECC?

A. In-band ECC is supported and can be enabled in the BIOS.

Q. Which operating systems are compatible?

A. Common systems like Windows 10, Windows 11, and Ubuntu are well-supported. For a complete compatibility list, please refer to the official documentation.

Complete Specifications