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Choose a VM size by separating the agent gateway from model inference. If the gateway calls a hosted model API, size the VM for the gateway, integrations, tools, memory, and any browser or code-execution work—not for model weights. If you serve a model locally, its weights, context window, runtime, request concurrency, and other host processes become part of the sizing decision.
There is no universal RAM minimum. DigitalOcean’s OpenClaw Marketplace guidance offers a useful provider-specific starting point, but actual fit depends on workload and should be checked during real agent turns.
First decide where inference runs
An AI agent deployment can place the model and the agent gateway on separate machines or combine them. That choice changes what the VM must provide.
| Deployment | What the VM mainly needs to run | Key sizing factors |
|---|---|---|
| Hosted model API | The gateway, chat-channel integrations, tools, sessions, memory, and any browser or code-execution components | Concurrent users and sessions, channel count, browser automation, sandbox instances, scheduled work, and co-located services |
| Local model server | The gateway plus, if colocated, model-serving software and inference | All gateway factors, plus model weights, context size, runtime, concurrent requests, available RAM and supported GPU memory, and disk |
OpenClaw is a self-hosted gateway that connects chat applications to AI coding agents and supports tools, sessions, memory, multiple channels, and multi-agent routing. Its documented runtime requirements can change: the project currently recommends Node 26 and supports Node 24.16+ or 26.1+. Check the project’s architecture and requirements documentation for the version you plan to deploy.
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Choose a deployment shape that matches the agent
VM size is only one part of the decision. The workload’s lifecycle and traffic pattern help determine what kind of compute to run. Google Cloud’s Cloud Run guide distinguishes these patterns; its resource types are Cloud Run-specific, not a promise that every cloud provider offers identical options.
| Workload pattern | Suitable resource pattern in Google Cloud’s guide | Example fit |
|---|---|---|
| Stateless requests with variable traffic | Service | Request-driven traffic that benefits from autoscaling or scaling to zero |
| Dedicated, stateful, always-on loop | Instance | A personal agent such as OpenClaw or Hermes |
| Distributed background tasks | Worker pool | Tasks consumed from a queue |
| Run-to-completion workflow | Job | A workflow that starts, completes, and exits |
Google Cloud describes instances as best for “dedicated, stateful always-on singleton agent loops requiring VM-like lifecycle state commands.” See its Cloud Run guide to agent resource types for the provider’s current distinctions.
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Use published gateway tiers as a starting point
For an OpenClaw gateway that calls a hosted model API, DigitalOcean publishes the following recommendations for its OpenClaw Marketplace image. Its page lists image version 2026.9.3; the tiers are DigitalOcean guidance, not an independent benchmark or a fit guarantee.
| DigitalOcean tier | CPU | RAM | Published user band |
|---|---|---|---|
| Personal | 2 CPU | 4 GB | 1–5 users |
| Small Team | 4 CPU | 8 GB | 5–20 users |
| Medium Team | 8 CPU | 16 GB | 20–50 users |
| Large Team | 16 CPU | 32 GB | 50+ users |
DigitalOcean cautions that multiple sandbox instances or browser automation may require additional resources. User bands are therefore a rough proxy, not a substitute for accounting for simultaneous work and host services. Consult the provider’s OpenClaw Marketplace deployment guidance for its current recommendations and image details.
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Account for the work around the gateway
Before selecting a tier, estimate the peak as well as the typical load. Include simultaneous users and agent sessions, the number of connected channels, browser sessions, sandbox instances, scheduled jobs, and any database or observability service running on the same VM. A gateway that mostly waits on a remote API has a different compute profile from one also running browsers or code-execution environments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Size local inference separately
When a model runs on your own host, model-file size alone does not tell you whether the setup will fit or perform acceptably. OpenClaw identifies model weights, context size, runtime, and other host processes as relevant. Its hardware-aware managed llama.cpp setup checks available RAM, supported GPU memory, and disk rather than assuming one standard machine.
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OpenClaw’s curated managed llama.cpp recipes use a 64K context; the smallest recipe has an 8 GiB host-memory floor. That floor is not a performance guarantee: the documentation warns, “These floors do not guarantee fit or speed.” Do not apply the figure as a general minimum for other models, contexts, runtimes, or workloads.
Pick the serving backend, then verify the workload
OpenClaw’s guidance names LM Studio and Ollama as separately managed server options; it also identifies llama.cpp for hardware-aware model selection and OpenAI-compatible serving, and vLLM or SGLang for high-throughput self-hosted endpoints. The appropriate choice and hardware depend on the selected model and setup.
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Check the intended model, context window, and task mix against available memory and disk. Account for the full agent turn: prompts, tool descriptions, conversation history, and generated output all contribute to work beyond a short test prompt. A model that loads or answers a small prompt may still fail during a complete agent task, so validate with representative turns before making it the default. OpenClaw’s local model guidance describes its hardware-aware setup and managed recipes.
Measure the actual bottleneck before resizing
After deployment, observe the gateway under realistic use instead of relying on a user count alone. Record baseline and peak memory and CPU utilization, swap activity, disk use and I/O, and latency or timeouts during agent turns. Include the channels, concurrency, browser sessions, sandboxes, jobs, and co-located services expected in normal operation.
- Run representative agent tasks at expected concurrency, including browser or sandbox work if the deployment will use it.
- Check whether memory pressure, CPU saturation, swap, disk activity, or slow responses align with the observed failures or latency.
- Increase the resource that measurements identify as constrained, then repeat the same workload to see whether the constraint changed.
This measurement procedure is operational guidance, not a DigitalOcean benchmark protocol. Verify current provider availability, VM specifications, and pricing directly with the provider; no cross-provider price comparison is established here.
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