The Tool Desk
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What the VPS is actually running
Hermes Agent is the software, tools, sessions and automation layer on the server. In the light configuration described above, the language model is not running on the VPS; Hermes sends requests to a provider API and receives the results. That separation is why a small general-purpose VPS can be sufficient and why a GPU is not required for this particular setup.
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The resource picture changes when you add browser automation, multiple simultaneous sessions, dashboards, scheduled jobs, persistent logs, Docker layers or a local large language model. CPU has to handle Hermes processes and concurrent work, RAM must absorb the application plus browsers and caches, and storage must hold the operating system, containers, sessions, skills, logs and backups. Local inference adds model files and a memory requirement that can dominate the rest of the stack.
No single number should therefore be presented as “the Hermes minimum.” The figures below separate documented examples, operator defaults and practical recommendations so you can match the server to the workload.
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Documented figures versus practical starting points
| Source or deployment profile | CPU | RAM | Disk | What the figure means |
|---|---|---|---|---|
| Light, API-backed personal agent | 1 vCPU | 2 GB | 20 GB | Practical entry recommendation for one user; the model runs through a provider API and no GPU is needed. |
| More comfortable general use | 2 vCPU | 4 GB | 40–50 GB is more forgiving | Recommendation for browser automation, multiple profiles, dashboards or overlapping scheduled work. |
| Official Docker configuration example | 1 CPU | 5,120 MB | 51,200 MB | Example container limits with persistence enabled; Hermes documentation does not call these universal minimums. |
| Hermes Agent operator defaults | 500m request; 2 CPU limit | 1 Gi request; 4Gi limit | Default Kubernetes requests and limits in the operator chart, not a requirement for every VPS install. | |
| Community installation guidance | 4 GB | 1 GB plus model storage for local LLMs | Community advice requiring Python 3.11 or newer; it is not first-party policy. |
The Docker example is deliberately larger than the light VPS recommendation because it demonstrates a container configuration with room for persistent operation. Kubernetes requests and limits describe scheduling and enforcement in that operator; they should not be read as a guarantee that every standalone process needs those exact resources.
CPU: how many vCPUs does Hermes need?
One vCPU for a light API-backed agent
One vCPU is a reasonable starting point when one user runs ordinary prompts and occasional tools while model inference happens remotely. The VPS mainly coordinates requests, executes lightweight commands and maintains state. Keep expectations modest: a single core offers little room for several CPU-heavy tasks at the same time.
Two vCPUs for concurrency and browsers
Move to two vCPUs when the agent drives a browser, keeps more than one profile open, serves a dashboard or runs scheduled jobs that can overlap. Browser processes, page rendering, compression and file operations can compete with Hermes for CPU even though the model itself is remote. The 2-CPU figure matches the operator’s documented limit default, but it remains a sizing recommendation for a VPS rather than a universal minimum.
What CPU does not solve
Adding cores will not compensate for insufficient memory, slow storage or a local model that does not fit in RAM. If jobs are waiting on an API or network connection, more vCPUs may have little effect. Increase CPU when you observe concurrent work contending for processor time, not simply because a provider advertises a larger number.
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RAM: plan for the application, browsers and model choice
Hosted-model baseline
Two gigabytes is a practical baseline for a small, single-user, API-backed installation. It leaves memory for the operating system and Hermes without claiming that every plugin or browser workload will fit comfortably. Keep filesystem and swap headroom available so a temporary spike does not immediately terminate processes; the exact swap arrangement depends on the VPS operating system and provider.
When 4 GB is safer
Four gigabytes is the more comfortable target for browser automation, multiple profiles, dashboards or overlapping scheduled jobs. Browser tabs and their helper processes can consume substantially more memory than a text-only agent, and concurrent jobs create peaks rather than a steady average. The operator’s 4Gi limit and the community guide’s 4 GB recommendation both support using this as a practical general-purpose target, not as a mandatory Hermes floor.
Local-model memory
A local language model changes the sizing exercise completely. Model weights, the inference runtime, context windows and concurrent requests all consume memory. The available guidance says local LLMs require additional model storage and that the server must be sized to the chosen model, not to Hermes itself. Because no particular model, quantization or context length is specified here, there is no honest universal RAM number for local inference. Treat the model vendor’s runtime requirements as an additional constraint and leave room for Hermes and the operating system.
Storage: capacity and I/O are separate decisions
Why 20 GB can work
Twenty gigabytes of persistent disk can support a small hosted-model instance when you account for the operating system, Hermes files, a modest session history and normal logs. It is a starting point, not a promise that every update or browser cache will fit indefinitely.
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When to choose 40–50 GB
Forty to 50 GB gives substantially more operational margin for Docker layers, browser binaries and caches, downloaded skills, session data, logs and backups. Hermes’ official Docker example allocates 51,200 MB of container disk with persistence enabled, which illustrates the space a persistent containerized deployment may reserve. Do not confuse that example limit with a required minimum.
Local models and disk growth
Local model files can be many times larger than the agent software. Add the model’s download size, room for an updated copy during upgrades and any runtime cache before choosing a disk. If you keep several models or snapshots, capacity rises accordingly. Disk speed and I/O also matter: extracting a large model, rotating logs or starting browsers can be slow on a heavily contended volume even when the capacity number looks adequate.
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Use persistent storage for sessions, configuration and other state that must survive a container replacement. Reserve additional space for backups rather than filling the volume to its nominal limit. A nearly full filesystem can prevent log rotation, package updates or database writes, turning an otherwise adequate VPS into a failing one.
Remote API inference versus local inference
| Architecture | What the VPS supplies | Resource implication | GPU |
|---|---|---|---|
| Hosted model API | Hermes, tools, sessions, browsers and network access | Small CPU and RAM footprint is possible; size for concurrency and persistent data. | Not required for the light profile because inference occurs through the provider API. |
| Local model | Everything above plus model files and an inference runtime | RAM, disk and possibly CPU or accelerator capacity must be selected for the specific model and runtime. | May become necessary or highly desirable depending on the model; the simple hosted profile does not establish local-inference requirements. |
Network reliability becomes part of the hosted-API design: the server must reach the provider and tolerate request latency. Local inference removes that API dependency for generation but replaces it with substantial local resource consumption. Decide the architecture first; otherwise a “minimum VPS” number is meaningless.
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Installation prerequisites and supported platforms
The current POSIX source installer documentation lists Git, curl, tar and SHA-256 utilities as prerequisites. Hermes’ current first-party installations run on Python 3.14. Project metadata permits the broader >=3.11,<3.15 range for updater compatibility, so verify the interpreter used by the installation method rather than assuming any Python 3 release will behave identically.
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The official support matrix places Linux/WSL2 and Docker containers in Tier 1. It lists PyPI and Homebrew installation methods as unsupported. On a VPS, a supported Linux host or the documented Docker path is therefore the conservative choice; an unsupported packaging route can make troubleshooting and upgrades harder even if the initial install appears to work.
A practical sizing procedure
- Choose the inference location. Confirm whether Hermes will call a hosted model API or load a model locally. Do not size a local-model server from the hosted-agent baseline.
- Count simultaneous work. List browser profiles, open sessions, scheduled jobs that may overlap, dashboards and any other long-running tools. One quiet user and several concurrent browsers are different workloads.
- Select a CPU tier. Start at 1 vCPU for light single-user API use; choose about 2 vCPUs when concurrency or browser automation is part of normal operation.
- Select a memory tier. Use 2 GB as the light starting point and 4 GB when browsers, multiple profiles or overlapping jobs need headroom. Add the model runtime’s requirements separately for local inference.
- Budget persistent disk. Reserve 20 GB for a small hosted deployment. Prefer 40–50 GB when Docker, browser caches, logs, skills, sessions and backups will accumulate. Add model files and upgrade copies for local inference.
- Leave operating margin. Keep space for updates, logs, browser binaries and persistent data instead of allocating every available gigabyte to current files. Provide swap and filesystem headroom appropriate to the host.
- Use a supported installation path. Prepare Git, curl, tar and SHA-256 utilities for the POSIX installer, or follow the documented Docker configuration. Verify the Python version used by the chosen path.
- Measure after deployment. Watch CPU contention, memory pressure, filesystem use, browser stability and job overlap during the busiest expected period. Increase the constrained resource rather than blindly upgrading every component.
Common sizing mistakes and fixes
Treating a recommendation as a guaranteed minimum
The 1-vCPU, 2-GB, 20-GB profile is useful for planning a small hosted-model agent, but Hermes has not published a universal VPS minimum. Presenting it as a guarantee leads to surprises when browsers, plugins or concurrent sessions are added. State the workload whenever you record the specification.
Copying Docker limits into every deployment
The official example uses 1 CPU, 5,120 MB of memory and 51,200 MB of disk with persistence. Those values are configurable container limits, not proof that a bare-metal process needs exactly 5 GB of RAM or 50 GB of storage. Use them as a conservative container reference and adjust for your own state and workload.
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Ignoring local-model storage
A server sized for remote API calls may fail during a local-model download or run out of memory at inference time. Add model files, runtime caches and an upgrade copy to the disk plan, then validate the model’s RAM and accelerator requirements independently.
Filling the volume with logs and caches
Small installations often grow quietly through browser caches, Docker layers, session data and logs. Monitor filesystem usage, rotate or prune data according to your retention needs, and keep backup space separate from the minimum operating allocation.
Choosing an unsupported package route
PyPI and Homebrew are listed as unsupported in the official matrix. If an installation made through one of those routes breaks during an update, moving to a Tier 1 Linux/WSL2 or Docker path may be more reliable than trying to reproduce an unsupported environment.
Recommended specifications by workload
| Workload | Suggested CPU | Suggested RAM | Suggested persistent disk | Notes |
|---|---|---|---|---|
| One user, hosted model, light tools | 1 vCPU | 2 GB | 20 GB | Keep headroom for updates, logs and sessions; no GPU is needed for this architecture. |
| Browser automation or several profiles | 2 vCPUs | 4 GB | 40–50 GB | Allows more room for browser processes, caches and overlapping jobs. |
| Persistent Docker deployment | Use the workload-based CPU choice | Use the workload-based memory choice | 50 GB is the documented example allocation | Enable persistence and treat the example as a reference configuration, not a minimum. |
| Local language model | Model-dependent | Model-dependent plus Hermes overhead | Model-dependent plus runtime, cache and upgrade space | Select the VPS from the specific model’s requirements; the hosted-agent tiers do not apply. |
Bottom line
For a small Hermes Agent on a supported Linux or Docker setup that uses a hosted model API, begin at 1 vCPU, 2 GB RAM and 20 GB persistent disk, then monitor the busiest workload. Choose roughly 2 vCPUs, 4 GB RAM and 40–50 GB when browser automation, multiple profiles or overlapping jobs are normal. Local models require a separate calculation based on their files and runtime, and Hermes’ documented Docker and operator figures should be treated as deployment examples rather than a universal minimum.
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