DeepSeek V4 + Hermes Agent + ZimaBoard 2: a lightweight AI coding rig

Running powerful AI coding assistants no longer means you need a giant GPU rig. With the right tools and a small single-board computer, you can offload a lot of your coding workflow to an AI agent that lives on your network and quietly does the heavy lifting in the background.

This article walks through a practical setup that combines three key pieces: a ZimaBoard 2 as the always-on machine, Hermes Agent as the AI agent framework, and DeepSeek V4 Flash as the main coding model (served via the cloud). The result is a compact, low-power AI coding environment that still feels fast and capable.

Why Move from Raspberry Pi to ZimaBoard 2?

The original setup ran Hermes Agent on a Raspberry Pi using Gemma 4 as the model. It worked, but there were clear limits in performance and expandability. The ZimaBoard 2 solves several of those pain points while staying small and energy-efficient.

The chosen ZimaBoard 2 configuration includes:

• Intel N150 processor
• 16 GB of DDR5 RAM
• A PCIe slot for storage expansion

The PCIe slot is the real game changer here. By adding a simple PCIe-to-M.2 adapter and an SSD, the operating system and tools run much faster and feel more responsive than on an SD card–based Raspberry Pi setup. Assembly is straightforward: plug in the adapter, mount the SSD, and you’re done. The heatsink and SSD are light enough that they don’t stress the PCIe slot.

Once assembled, the BIOS correctly detects the SSD and shows its full capacity, confirming that the hardware is ready for an OS install.

Setting Up the Server Environment

The ZimaBoard is used as a headless server, accessed remotely from a laptop. That keeps the desk clean while still giving you a dedicated AI machine on your network.

The base setup looks like this:

1. Install Ubuntu Server
Ubuntu Server is installed directly onto the SSD. There’s no need for a graphical interface since all interaction happens over SSH.

2. Minimal cabling
After installation, the board runs with just two cables: power and Ethernet. This makes it easy to tuck away and leave running 24/7.

3. Remote access and terminal workflow
From a MacBook (or any other machine), SSH is used to connect to the ZimaBoard. Inside the remote session, a terminal multiplexer like tmux is launched. This lets you keep long-running sessions alive, detach and reattach later, and avoid losing work if the SSH connection drops.

Installing DeepSeek V4 Flash and Hermes Agent

With the base system ready, the next step is to install the AI stack: DeepSeek V4 Flash as the model and Hermes Agent as the orchestration layer.

DeepSeek V4 Flash via Ollama Cloud

DeepSeek V4 is a large model with strong benchmark results and a 1M-token context window, putting it in the same conversation as Anthropic and Google’s top models. Instead of downloading and running it locally (which would be heavy for a small board), it’s accessed through Ollama Cloud.

The process is:

• Install Ollama on the ZimaBoard
• Create an Ollama Cloud account and generate an API key
• Configure Hermes Agent to use DeepSeek V4 Flash as the model via Ollama Cloud

This offloads the heavy compute to the cloud while the ZimaBoard focuses on coordination, tools, and file operations.

Hermes Agent: One-Command Setup

Hermes Agent is then installed with a single command. During installation, it automatically sets up its core tools and skills, so it’s usable right away without manual wiring.

Key steps in the quick install:

• Choose Ollama Cloud as the LLM provider
• Select DeepSeek V4 Flash as the model
• Confirm paths and environment so Hermes can run from the shell

After reloading the shell configuration, Hermes Agent starts up with a splash screen and a list of available tools and skills. A simple self-query confirms that the agent is connected to DeepSeek and ready to work.

If you want a broader comparison of how DeepSeek V4 stacks up against other frontier models, it’s worth checking out this benchmark-focused breakdown of GPT‑5.5 vs DeepSeek V4.

Turning the ZimaBoard into a Coding Workstation

To make the ZimaBoard usable as a full development environment from the terminal, a few extra tools are installed.

• Git for cloning repositories (initially via HTTPS before SSH keys are set up)
• eza as a modern replacement for ls, with nicer, more readable directory listings
• Neovim as the main editor
• AstroNvim to give Neovim a batteries-included configuration and plugins with a single install

With this in place, the ZimaBoard can be used as a primary coding machine over SSH, while Hermes + DeepSeek handle higher-level coding tasks.

Real-World Test #1: Visualizing Sorting Algorithms

The first test for Hermes + DeepSeek V4 Flash is a classic coding challenge: build a web page that visualizes six different sorting algorithms. The task description is intentionally abstract—no step-by-step instructions, just the desired final behavior.

To give the agent context, Hermes is restarted inside the project folder that contains a repository of standard test tasks. This helps it understand which files it should read and write.

The request to Hermes is simple: implement what’s described in the task file. On the first run, Hermes spends a bit of time initializing tools and skills before calling the model. System monitoring shows that the ZimaBoard itself stays under light load, which is ideal for a small always-on device.

Hermes reports that it has implemented the solution and written it to a file. The generated page includes:

• Six sorting algorithms
• Controls for running and visualizing the algorithms
• Speed control for the animations

After copying the file to a laptop and opening it in a browser, the result is impressive: the UI looks good, the animations run smoothly, speed control works, and all six algorithms behave correctly. For a loosely specified task, DeepSeek V4 Flash handles it cleanly.

Real-World Test #2: Building a To-Do Web App with Planning

The second test is more complex: build a full-featured to-do list web app. This time, the task file doesn’t describe the final UI or behavior directly. Instead, it lists a series of smaller tasks that, if implemented correctly, should add up to a complete application.

Using Hermes Planning for Better Results

One of Hermes Agent’s standout features is its built-in planning workflow. Instead of immediately executing a task, you can ask Hermes to create a plan first, review it, and only then run it.

Using the plan command, Hermes reads the original task file and generates a structured implementation plan. The plan includes:

• An overall project goal and context
• A step-by-step implementation strategy
• Concrete breakdowns of each stage (not just a copy of the original tasks)
• A validation checklist to confirm everything works as intended

Comparing the original task file with the generated plan side by side shows how Hermes transforms a loose set of instructions into a clear roadmap. This planning layer is a big part of why the final result is so accurate.

From Plan to Working App

Once the plan looks solid, Hermes is instructed to implement it. While it works, system monitoring again shows that the ZimaBoard’s CPU and memory usage stay modest—another sign that using a cloud model with a lightweight local agent is a good fit for this hardware.

The final output is a single todo.html file. Opening it in the browser reveals a polished to-do app with:

• Task creation
• Marking tasks as completed
• Deleting tasks
• Counters for different task categories that update correctly

Everything works as expected on the first try. In this case, the combination of Hermes’s planning and DeepSeek V4 Flash’s coding ability produces a surprisingly complete and bug-free result.

If you’re interested in how different models behave as orchestrators, executors, or support models inside Hermes, you might also find this guide to the best AI models for Hermes Agent useful.

Takeaways: DeepSeek V4, Hermes, and ZimaBoard 2

After these tests, a few clear conclusions emerge about this setup.

1. DeepSeek V4 Flash is strong for medium-complexity coding

For tasks like interactive visualizations and single-page web apps, DeepSeek V4 Flash performs very well. It understands abstract requirements, structures code sensibly, and produces working results with minimal iteration. How it scales to large codebases and truly complex projects still needs deeper testing, but as an everyday coding assistant, it’s already compelling.

2. Hermes Agent adds reliability through tools and planning

Hermes continues to stand out for its stability and built-in workflow features. Automatic tool and skill setup makes it easy to get started, and the planning command significantly improves the quality of results on multi-step tasks. It feels less like a raw model and more like a structured development assistant.

3. ZimaBoard 2 is an excellent low-power AI hub

The ZimaBoard 2 handles all of this without breaking a sweat. Key advantages include:

• Easy SSD expansion via PCIe
• Low temperatures and no driver headaches
• Enough CPU and RAM headroom to run Hermes, terminal tools, and other lightweight services in parallel

As a compact, always-on machine for AI-assisted coding, it hits a sweet spot between power, simplicity, and efficiency.

Combined, DeepSeek V4 Flash, Hermes Agent, and the ZimaBoard 2 create a flexible AI coding environment that feels far more capable than its size suggests—and it’s a great blueprint if you’re looking to build your own small, dedicated AI dev box.