Both options work. Use a VPS when Hermes Agent must stay online for Telegram or Discord gateways, scheduled jobs, and persistent memory. Run it locally when you want the simplest personal setup or intend to host the language model on your own computer. A hosted-API configuration is lightweight; running a model locally is the resource-intensive option.
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The practical decision is therefore about uptime, where the model runs, administration, and whether you already own suitable hardware—not just where the Hermes command is installed.
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What changes between a VPS and a local installation?
Hermes Agent is the orchestration layer in both arrangements. The major resource difference comes from the model endpoint. If Hermes sends requests to a hosted API, the machine running Hermes mainly handles conversations, tools, scheduling, gateway connections, and stored state. If Hermes connects to a model served on your own hardware, that hardware must also hold model weights and perform inference.
| Question | VPS | Local computer |
|---|---|---|
| Availability | Designed to remain online in a datacenter | Available only while the computer is awake, connected, and Hermes is running |
| Best fit | Messaging gateways, scheduled work, and persistent services | Personal use, experimentation, or using hardware you already own |
| Hosted API model | Small instance is usually sufficient | Works without a powerful GPU |
| Local model | Usually impractical on a small VPS; a GPU-capable server may be needed | Practical if your RAM, GPU, cooling, and storage are adequate |
| Administration | SSH access, updates, firewall rules, backups, and service supervision | No server administration, but you must keep the machine and network available |
| Recurring cost | Monthly hosting charge | Electricity and hardware cost; may be zero if you already own the computer |
There is no single official RAM minimum that resolves every use case. Published third-party guidance conflicts: one estimate gives 1 vCPU, 1 GB RAM, and 5 GB of disk for an API-only installation, while another recommends 2 GB RAM and 10 GB of disk. Those figures describe different margins and workloads, not a guaranteed product specification.
When a VPS is the better choice
Choose a VPS for continuous gateways and schedules
A VPS is the safer default for a bot or assistant that must answer while your laptop is closed. Telegram and Discord gateways, cron-like schedules, webhook listeners, and memory that accumulates over days all benefit from a host that stays online. A local desktop can provide the same uptime only if it remains powered, connected, and free of sleep or reboot interruptions.
Size the server for the actual workload
For a single hosted-API connection, the lower estimates may run, but they leave little room for logs, concurrent conversations, upgrades, or another gateway. A 2 GB instance is a more forgiving starting point when the agent is always on. Several guides commonly suggest about 4 GB of RAM for an always-on agent using multiple channels, and 4–8 GB when browser automation is involved. These are third-party operating estimates, not limits published by Nous Research.
Do not confuse model memory with Hermes memory. A small API-only VPS can outsource inference, whereas a local model requires RAM or GPU memory for the model itself. One guide estimates about 8 GB of system memory for small 3B models and 32 GB or more for models above 27B. Actual needs vary with quantization, context length, runtime, and whether inference uses a GPU.
Account for the monthly and operational cost
Hosting adds a recurring monthly bill, while local operation shifts the expense to hardware and electricity. A guide claims that Hermes can run on a $5 VPS, but that figure was not verified against an official Nous Research page and should not be treated as a promise. A low-priced instance may also have less CPU performance, storage, network transfer, or backup service than your workload requires.
How to install Hermes Agent on an Ubuntu VPS
1. Pick a supported base and create a non-root account
The official project lists Linux, macOS, WSL2, native Windows, and Android through Termux. For a VPS, Ubuntu or another mainstream Linux distribution is the straightforward route. Older community guides often recommend Ubuntu 24.04 or Debian 12 because Ubuntu 22.04 ships an older Python. The current first-party installation documentation targets Python 3.14 and does not promise runtime support for Python 3.11–3.13, so treat Python 3.14 as the working target and verify the requirement in the current official documentation before provisioning.
Create a normal user for Hermes, grant only the administrative privileges needed for installation, and use SSH keys rather than password-only login. Keeping the agent out of a root shell limits the damage from a compromised integration or tool.
2. Update the operating system and protect SSH
Apply the distribution’s security updates before installing application software. Restrict the firewall to SSH and only the inbound ports you deliberately expose for a gateway or webhook. Do not expose an administrative dashboard or model server directly to the public internet unless the current Hermes documentation explicitly requires it and you have authentication and TLS in place.
3. Inspect the installer, then run it
The commonly documented installation route is a shell script hosted in the official repository. You can read it before execution:
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | lessIf the contents look appropriate for your system, run the installer:
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bashThe installer is reported by third-party guides to bring in tools such as uv, Python, Node.js 22, ripgrep, and ffmpeg. Those dependency details are aggregated guidance rather than a guarantee from an official page, so allow the installer to report its own results and review any failures instead of assuming every component was installed.
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After installation, initialize the configuration and run the built-in diagnostics:
hermes setup
hermes doctorFollow the prompts for the model provider, credentials, storage, and any messaging channels you intend to enable. Keep API keys in the environment or the configuration mechanism documented by the current release; never paste secrets into shell history, public issue reports, or a world-readable file.
5. Keep the process alive after logout
An SSH session is not a service manager. For a production-like VPS, create the systemd service described by the current Hermes release or use another supervisor that restarts the process after a crash and starts it after reboot. Because service names and daemon subcommands can change between releases, first inspect the installed command with hermes --help and use the service instructions shipped with that version rather than copying an old unit file. Verify the result by rebooting during a maintenance window, checking logs, and confirming that the gateway reconnects.
6. Plan persistence and recovery
Persistent memory, conversation state, and task data are valuable and may contain sensitive information. Identify where Hermes stores them, include that directory in encrypted backups, and test restoring to a clean account. Keep enough disk for logs and model-related caches; the 5 GB and 10 GB figures in community sizing advice are starting estimates, not a promise that long-running browser sessions or extensive history will fit.
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When running locally is the better choice
Use local installation for simplicity and privacy control
The same installer is used on macOS, Linux, and WSL2, so local deployment avoids VPS provisioning, firewall rules, SSH hardening, and service supervision. It is convenient for a personal assistant that you start when needed, and it lets you keep files and credentials on equipment you control. The trade-off is availability: sleep, travel, Wi-Fi changes, reboots, and a closed laptop interrupt gateways and scheduled work.
Know the desktop platform limits
The official project lists native Windows installation through PowerShell, as well as WSL2. The desktop application README lists builds for macOS, Windows, and Linux. On an Intel Mac, the bootstrap installer reports that the platform is unsupported; Apple Silicon is the supported Mac route for that installer. If you compile from source, expect to need a native compiler and platform development libraries.
Separate Hermes from a local model server
Hermes can point at a local model server such as Ollama or LM Studio. You can run that server on the same computer, on another machine on your local network, or—according to community guidance—connect to a separate machine over SSH. This separation is useful when a lightweight laptop runs the agent while a desktop or dedicated GPU box performs inference. Secure the connection and bind the model server only to interfaces you trust.
Local-model sizing is dominated by the model, not by the Hermes process. A third-party estimate places small 3B models around 8 GB of system memory and models above 27B at 32 GB or more. Quantization can reduce memory use, while longer context windows, multiple simultaneous requests, and GPU offload can raise it. Treat those figures as planning guidance and check the model server’s own requirements.
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- Hosted API and occasional personal use: install locally unless you need uninterrupted background tasks.
- Hosted API with Telegram or Discord: use a VPS or keep a local machine permanently awake with a reliable supervisor.
- Scheduled jobs that must run while you are away: prefer a VPS, with backups and restart handling.
- Local model for privacy or predictable offline use: choose local hardware with sufficient memory, or place the model on a separate GPU machine and connect Hermes to it.
- Browser automation: budget more memory than an API-only setup; community guidance commonly places this class around 4–8 GB of RAM on a VPS, before accounting for the model.
- Development and source changes: local installation is usually more convenient, provided your operating system has the required compiler and development libraries.
Common installation problems and fixes
Python version mismatch
If setup selects an unsupported interpreter or fails while building a dependency, check the current first-party requirement and confirm the active version with python3 --version. Do not assume a guide written for Python 3.11+ applies to the current release; the first-party documentation now identifies Python 3.14 as the target.
Installer fails while building a package
Source builds may require a native compiler and operating-system development libraries. Read the first missing-header or compiler error, install the matching package from your distribution, and rerun the setup. Avoid solving a build failure by running the entire installer as root.
Out-of-memory kills or slow responses
Check RAM and swap usage while Hermes and any browser or model server are active. Reduce concurrent jobs, move inference to a separate machine, select a smaller or more aggressively quantized model, or resize the VPS. A hosted API removes model inference from the VPS but does not remove the memory cost of browser automation or multiple gateways.
The agent stops when SSH closes
Run Hermes under the release’s documented systemd integration or another supervisor. A foreground process tied to an SSH terminal is suitable for testing, not for a 24/7 gateway.
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This is a platform limitation reported by the bootstrap installer, not necessarily a problem with your shell. Use Apple Silicon, Linux, Windows, or WSL2, or investigate a source-build path only if the project documentation lists your hardware as supported.
A local model cannot be reached
Confirm that the model server is running, listening on the expected interface, and reachable from the Hermes host. For a second machine, verify routing and firewall rules, then use authenticated encryption rather than exposing an unauthenticated inference endpoint.
Version and support cautions
Community material may label instructions for a particular Hermes major or minor version; one 2026 guide is titled for v0.20. Version numbers in those guides were not checked against the official release list, so use the current repository and installation documentation for command names, service integration, Python compatibility, and supported platforms. The same caution applies to claims about a 64,000-token context minimum: that requirement was not confirmed on an official page.
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