How to plan hardware for OpenClaw with a local model

Can my laptop run this model? That is usually the wrong first question.

A local model may answer a short test prompt and still fail when OpenClaw adds the system prompt, conversation history, tool definitions, file contents, and the model's own output. OpenClaw's local-model documentation makes this distinction directly: a short model check does not prove that a full agent turn will work.

The useful decision is whether the whole workflow fits. This includes the Gateway, the model server, context growth, tools, storage, network placement, and the amount of waiting an operator will accept.

This guide separates three things. The documented behavior comes from OpenClaw and Ollama. The test plan is a proposed way to evaluate your machine. The recommendations are editorial judgment, not hardware benchmarks.

Start with the machine layout

First decide where each part will run. Ollama can run on the same machine as the OpenClaw Gateway, or on another host reachable over a local network. OpenClaw's Ollama configuration recipes show both patterns.

That choice changes the hardware question. On one laptop, the Gateway, model server, browser, editor, and other applications compete for memory and compute. With a separate model host, the second machine carries the model workload, while the Gateway depends on a network path and a server that stays available.

Ollama binds to 127.0.0.1:11434 by default. Its FAQ says that exposing the service on another interface requires changing OLLAMA_HOST. A remote setup therefore needs an explicit network and access decision. A GPU box in another room is not automatically a private or reliable endpoint just because it is owned by you.

Write down the location of the Gateway, model server, files, and tools before comparing machines. The location of the model answers where inference happens. It does not, by itself, answer where the agent's files are read or where its tools execute.

1. How much memory remains after the model loads?

Memory is the first hard constraint. OpenClaw documents that local-model requirements depend on model weights, context size, runtime overhead, and other work on the host. Its managed llama.cpp setup checks available RAM, supported GPU memory, and disk space. The smallest documented recipe has an 8 GiB host-memory floor with a 64K context, but the same page says that this floor does not guarantee fit or speed.

That number is a lower boundary for one managed recipe, not a general OpenClaw recommendation. Treating it as a shopping target leaves little room for a Gateway, a browser, tool processes, file indexing, or normal operating-system activity.

For a real decision, record four values:

• the model's reported download or memory size;
• the context setting used by the server;
• free memory before and after loading the model;
• free memory while the full OpenClaw workflow is running.

The last value is the one that matters. A model that fits when the desktop is idle may become unstable when a tool launches a process or when a large file is added to the prompt.

My planning rule is simple: leave visible headroom. If loading the model consumes nearly everything the machine has, the configuration has no useful margin for a longer turn or a second request.

2. What context will a real agent turn consume?

Context is more than the user's last sentence. An OpenClaw turn can include instructions, history, tool descriptions, tool results, selected files, and the requested output. Those inputs compete for the model's context window.

Ollama defines context length as the maximum number of tokens the model can access in memory. Its context-length documentation also warns that increasing context increases memory requirements. It lists agents and coding tools among the workloads that may need a large context.

OpenClaw's Ollama recipes expose two related settings: contextTokens caps the active input budget used by OpenClaw, while params.num_ctx sets Ollama's request context. The documentation advises keeping them aligned when the hardware cannot handle the model's full advertised context.

Do not choose a context limit from the model card alone. Test the input your workflow actually produces:

1. a short request with no tools;
2. a normal task with the usual files and tool calls;
3. a long task near the maximum context you expect;
4. an overlong task that should be summarized, paged, or refused.

Record whether the task completes, how much memory is used, and whether the answer still contains the detail the operator needs. If the long case fails, reducing the model size may not solve the underlying problem. You may need smaller file batches, earlier summaries, a lower context cap, or a different provider for that class of task.

3. Is the accelerator supported and actually being used?

A machine can have a capable-looking GPU and still run the model on the CPU. The software path matters: device family, operating system, driver, runtime backend, and model offloading all affect the result.

Ollama's hardware-support documentation lists supported NVIDIA and AMD hardware along with operating-system and driver requirements. It also documents a Linux suspend-and-resume failure mode where Ollama may stop discovering an NVIDIA GPU and fall back to CPU execution.

Check the running process instead of trusting the product label. Ollama's context documentation uses ollama ps to show the model, allocated context, and processor split. It recommends avoiding CPU offloading for best performance.

The proposed test is to inspect the process after both a cold load and a normal request. Record whether the processor column shows the intended accelerator, whether the context is the value you configured, and whether the behavior changes after sleep, resume, or a driver restart. Do not turn one successful GPU detection into a universal speed claim. It only proves that this model and this software path used the device during that check.

4. How will cold starts and idle periods behave?

Some local servers stay loaded all day. Others load a model only when OpenClaw needs it. OpenClaw's local model services documentation describes an on-demand path: OpenClaw checks a health endpoint, starts the configured process when necessary, waits for readiness, sends the request, and can stop the process after an idle period.

This saves an always-on machine from carrying the model continuously, but it adds a startup path that needs its own timeout and failure handling. A user may experience the first request as a hang even when the server is still loading normally.

Test a cold request and a warm request separately. Measure time to the first useful response, not only total generation time. Then stop the server and repeat the test. If cold starts exceed the time people will wait, keep the model resident, use a smaller model, extend the documented readiness and provider timeouts, or route that workflow elsewhere.

OpenClaw's service documentation also says that the managed server is a child process of the OpenClaw process that started it. That makes process ownership part of the operational design. A laptop that sleeps or closes the Gateway is not the same as a dedicated host that remains available.

5. Is there enough storage for the model lifecycle?

Model storage is easy to underestimate because the first download is the only number visible during setup. OpenClaw's local-model setup can show a model's download size before downloading it. Ollama's FAQ documents the default model directories for macOS, Linux, and Windows, and says that OLLAMA_MODELS can move them elsewhere.

Check the exact storage volume, not only the advertised capacity of the computer. Keep room for the selected model, a second model for comparison or fallback, partially completed downloads, logs, and ordinary work on the same disk. If the model directory is moved to an external or network volume, test that path under the same workflow; a file that exists is not the same thing as a server that can load it reliably.

Storage is also a data-boundary question. Record who can read the model directory, where logs are written, and whether backups include model files or only configuration. The proposed storage test is mundane: fill the workflow with its normal files, switch models once, restart the service, and confirm that the selected model remains discoverable without manual repair.

6. Can the machine handle tools, not only text?

Tool use changes the hardware and compatibility profile. Tool definitions consume context. Tool results may be much larger than the original request. The model also needs to emit a structured call that the server and OpenClaw can interpret.

OpenClaw's local-model documentation recommends a staged check. A local model probe exercises the model without agent context. A Gateway probe checks routing, authentication, and provider selection while still sending only the prompt. A normal agent turn adds transcript assembly, bootstrap instructions, tools, and other context.

Use the documentation-derived commands below as a sequence, not as proof of success:

openclaw infer model run --local --model <provider/model> --prompt 'Reply with exactly: pong' --json
openclaw infer model run --gateway --model <provider/model> --prompt 'Reply with exactly: pong' --json
openclaw models list --provider ollama
ollama ps

If the first command passes and the full agent turn fails, the next suspect may be context size, memory pressure, the server's chat template, or tool-call parsing. OpenClaw documents cases where a local model emits raw JSON, XML, or ReAct-style text instead of a structured tool call. That is not a completed tool execution.

Run at least one harmless read-only tool call before allowing writes, browser actions, or outbound messages. A local model also does not inherit hosted provider safety filters. Keep permissions narrow while evaluating the machine.

A worked decision: one host or two?

Imagine a developer wants a private repository summarizer. The Gateway runs on a laptop, while the model could run on that laptop or on a separate GPU machine.

The choice depends on the workload. A short, occasional, read-only task may justify a smaller model on the existing machine. A long-context workflow that runs repeatedly may justify a separate host, but only if the network, power, access control, and maintenance burden are acceptable. No table can decide that without the measured task.

Proposed acceptance test before buying hardware

Pin one exact model, one backend, and one OpenClaw configuration. Then run the same workflow through these checks:

1. Load the model and record its reported size and available memory.
2. Inspect ollama ps or the equivalent backend status to confirm the processor and context allocation.
3. Run a short text-only probe.
4. Run a Gateway-routed probe.
5. Run the complete read-only agent task with real-sized files.
6. Repeat after a cold start, after context growth, and with one additional request waiting.

Set the pass criteria before testing. For example, the task must return the required fields, stay below a defined memory-pressure threshold, and finish within the delay an operator accepts. Those thresholds belong to your workflow. The tests do not establish a benchmark for every model or computer.

Failure paths worth planning

A few symptoms point to different decisions:

• The model will not load: check available host or GPU memory and disk space. Reduce the model or context, or move inference to another host.
• The short probe passes but the agent turn fails: inspect context allocation, tool schemas, model compatibility, and server logs. Do not treat the short probe as proof that the workflow works.
• The server returns an interrupted stream or connection reset: match the timestamp with memory and server logs. OpenClaw specifically lists memory pressure as a possible cause for local-server termination.
• The Gateway cannot reach Ollama: check whether Ollama is listening only on loopback, whether the configured host is correct, and whether the Gateway runs in another container or machine.
• The first request times out: separate model startup time from generation time, then adjust the provider timeout or keep the server loaded.
• The accelerator disappears after resume: verify the driver and processor status again before comparing performance.

The safest fallback is bounded. Keep the model on read-only tasks until it passes the tool test. If a task needs more context or capability than the local host can provide, route that task to a different provider with the change visible to the operator. A local endpoint is a deployment choice, not a guarantee that every task belongs on that machine.

The hardware questions that actually matter

Before buying hardware for OpenClaw, ask:

1. How much memory remains after the model, context, Gateway, and tools are running?
2. What context does the largest normal agent turn require?
3. Does the selected backend support the accelerator and use it consistently?
4. Will the Gateway and model server share a host or cross a network boundary?
5. Can storage hold the model set and its normal lifecycle?
6. What happens after a cold start, sleep, crash, or second request?
7. Can the model make a real, harmless tool call rather than only produce text?

If those answers are measured against the real workflow, the hardware decision becomes smaller and clearer. Choose the least demanding setup that leaves room for the task, then keep a recovery path for the cases it cannot handle.

Sources

• Local models - OpenClaw
• Ollama config recipes - OpenClaw
• Local model services - OpenClaw
• Context length - Ollama
• Hardware support - Ollama
• FAQ - Ollama