There is no fixed RAM or CPU requirement for every self-hosted n8n installation. The resources you need depend mainly on workflow data, the work nodes perform, and how many executions overlap. Start with a modest setup only as a trial, then measure it with representative workflows before choosing production capacity.
What n8n’s published sizing figures mean
n8n’s undated prerequisites documentation gives an illustrative range of 320 MB–2 GB of memory and “minimum 10 CPU cycles.” The table is based on n8n Cloud, not a guaranteed minimum for self-hosting; the page says requirements vary with users, workflows, and executions. It does not translate “10 CPU cycles” into a general-purpose vCPU count, so do not read it as 10 CPUs.
The same page gives about 100 MB as an example of idle memory for an n8n Cloud instance. That is not a prediction of peak memory for an active self-hosted workload. n8n describes itself as generally not CPU intensive and says small instances from providers such as AWS and GCP are enough for most use cases, but that is broad guidance, not a specific CPU allocation.
What determines how much RAM you need?
Memory use depends on the data and operations in your workflows, not just the number of workflows configured. n8n says it does not restrict how much data each node can fetch and process, so a workflow can require more memory than the instance has available.
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- JSON volume: Large records or datasets can increase the amount of data held during execution.
- Binary data: Files and other binary payloads can make workflows more memory-intensive.
- Workflow structure: More nodes and memory-heavy Code nodes—or older Function nodes—can add to demand.
- Manual executions: These use additional memory because n8n copies data for the frontend.
- Overlap: Concurrent executions can compete for memory while their data is in flight.
These are the factors listed in n8n’s memory-related errors guidance. A workflow that processes small JSON records may behave very differently from one that transforms large datasets or handles files, even on the same instance.
How CPU demand and concurrency affect capacity
n8n says typical use is not CPU intensive, but the work a particular workflow performs and the number of simultaneous executions still matter. Its benchmarking guidance identifies workflow type, available resources, and scaling configuration as performance factors.
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In regular self-hosted mode, production execution concurrency is unlimited by default. n8n warns that excessive concurrency can thrash the event loop, degrade performance, or leave an instance unresponsive. The N8N_CONCURRENCY_PRODUCTION_LIMIT setting can cap production executions; executions above the limit wait until capacity is available. The documented limit applies to production executions started by a webhook or trigger. See n8n’s concurrency-control documentation for the applicable configuration.
For queue-mode deployments, worker concurrency is set with the n8n worker --concurrency flag. n8n’s documentation gives a default of 10 and recommends 5 or higher for worker instances; it also warns that low concurrency combined with many workers can exhaust the database connection pool. These are version-sensitive settings, so check the documentation for the n8n version you run before adopting them.
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Choose a starting point by workload, not a universal minimum
Use these questions to decide what to provision and what to test:
- What will the workflows process? Note whether they handle small JSON records, large datasets, binary files, or transformations in Code nodes.
- What may run at the same time? Estimate overlapping production executions, and whether users will also launch manual executions.
- What else shares the host? Leave capacity for the operating system, database, Redis if used, and any other co-located services. n8n’s illustrative figures do not size the entire stack.
- Can you reproduce expected demand? Run representative workflows with realistic data volumes and expected concurrency, then monitor resource use and errors.
- Does concurrency impair responsiveness? If overlapping production work overwhelms regular mode, consider limiting it; investigate queue mode and workers only if they suit your deployment.
n8n recommends its benchmarking framework for a use-case-specific estimate. The documentation reports up to 220 workflow executions per second on a single instance, but that is a reported benchmark, not a capacity promise: results depend on workflow type, available resources, and scaling configuration. It does not establish how many executions per second your workflows will achieve.
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Recognize and respond to memory pressure
n8n lists “Execution stopped at this node” and JavaScript heap out-of-memory errors as possible memory-related messages. “Problem running workflow,” “Connection Lost,” and HTTP 503 can also indicate that an instance became unavailable, but a 503 alone does not prove RAM is the cause.
If memory is insufficient, n8n’s documented options are to make more memory available or reduce workflow consumption. Review the amount of data workflows retain and process, especially large payloads and memory-intensive nodes. For JavaScript heap errors, the documentation also discusses raising V8’s old-space limit; this is an advanced tuning measure and does not add physical memory.
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