The right VPS size depends first on where the AI model runs. An agent that calls a hosted model can start with 2 vCPUs and 4 GB of RAM for development, then move to 4 vCPUs and 8 GB for a modest production deployment, with at least 20 GB of SSD space in the cited deployment guidance. A VPS that runs the model locally needs substantially more memory and disk: a 7B-class model is a planning case for 16 GB or more of RAM, at least 50 GB of free space is a safer starting point for an Ollama-oriented stack, and GPU requirements depend on the model and quantization.
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Those figures are starting points, not performance guarantees. Agent tools, browser workers, databases, context length, concurrent requests, logs and backups can all change the requirement.
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First decide where inference happens
An AI agent has two separable parts. The agent runtime handles orchestration, tool calls, browser automation, files, databases, queues and authentication. The inference runtime executes the language model and returns tokens. If a hosted provider performs inference, the VPS mainly needs CPU, RAM, reliable networking and persistent storage for the runtime and its data. If the VPS performs inference itself, model weights and the model’s working memory become the dominant resource consumers.
OpenAI’s architecture documentation describes a self-hosted environment as useful when an agent needs your infrastructure, private network or custom software. That choice changes the hardware calculation; it is not simply a larger version of an API-only deployment.
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Starting specifications by workload
| Workload | Starting CPU | Starting RAM | Storage planning | GPU or VRAM |
|---|---|---|---|---|
| One development agent using a hosted API | 2 vCPUs | 4 GB | 20 GB SSD as a practical application-and-logs floor in the cited deployment guidance | Usually unnecessary |
| Small production agent with tools and a database | 4 vCPUs | 8 GB | SSD with headroom for images, logs, indexes and backups | Usually unnecessary |
| Local 7B-class model plus agent runtime | 4–8 vCPUs | 16 GB or more as a planning baseline; verify the selected model | At least 50 GB free is a safer starting point for local-model data | Depends on model and quantization |
| Several concurrent agents, browser workers or larger local models | Scale cores with concurrency; isolate inference if needed | Measure peak working-set memory | Add separate capacity for models, databases and backups | A dedicated GPU or external inference may be appropriate |
The development and production figures come from TechRadar Pro’s 2026 OpenClaw VPS deployment guidance. Docker’s 2026 local-stack example and SUSE’s 2025 AI requirements document provide the local-inference context. The local-model row is a planning recommendation assembled from those requirements, not a vendor guarantee or benchmark.
Requirements for an agent that uses a hosted model
CPU: orchestration rather than token generation
When the model runs elsewhere, vCPUs execute the agent loop and its tools. One lightweight agent can spend most of its time waiting on network responses, so additional cores do not automatically make each answer faster. CPU demand rises when the agent parses large documents, runs code, transforms data, handles several tool calls at once or launches browser automation.
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Start with the development tier in the table for a single builder or test process. Move to the production tier when the service is persistent, has a database, or must handle scheduled jobs and several simultaneous actions. Watch CPU usage during tool-heavy bursts rather than relying on an idle average.
RAM: the working set is what matters
Memory is consumed by the operating system, the agent process, language runtimes, browser processes, database caches, container overhead and temporary tool output. A browser worker can use more memory than the orchestration code itself. Leave headroom for spikes; a VPS that only fits at idle will swap or kill processes when several tasks overlap.
Measure the peak resident memory of the complete stack, including sidecars and monitoring agents. If the service approaches the VPS limit, reduce browser concurrency, cap tool output, move the database to a separate host or choose a larger plan. Adding swap can prevent an abrupt crash, but it does not turn insufficient RAM into predictable performance.
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Storage and networking
Hosted-model agents still need persistent SSD storage for container layers, application files, session data, vector indexes, logs and backups. The 20 GB figure is a practical floor for the cited deployment scenario, not a universal capacity promise. Fast, reliable networking often matters more than local disk speed because every model request and many tool operations cross the network.
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Requirements for local model inference
Model weights and quantization
Running a model on the VPS adds the model files themselves and the memory needed to load them. Quantization can reduce memory use, but each model family and quantization level has different requirements. Do not select a VPS from the model’s marketing parameter count alone; check the runtime’s documented memory requirement for the exact file you intend to load.
A 7B-class model with an agent runtime should be planned at 16 GB of RAM or more until the chosen model, context window and concurrency have been verified. That is a planning baseline, not a guaranteed speed or quality level. Larger models, longer contexts and multiple simultaneous generations can push the working set far beyond the weight file size.
Context length, concurrency and runtime overhead
Every active conversation requires a key-value cache and temporary buffers. Longer prompts and larger context windows increase memory use even when the model weights stay unchanged. Two requests running together can require roughly two active caches, subject to the runtime’s batching behavior. Tool results, retrieved documents and browser snapshots also enter the context.
For a local deployment, test the maximum number of concurrent requests you actually expect. Record peak RAM, CPU utilization, response latency and whether the runtime begins paging. A single interactive user and a queue of background agents are different workloads and should not share an assumed capacity figure.
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Do you need a GPU?
An API-only agent generally does not need a GPU because the provider executes the model in its own environment. A local model can run on CPU, but generation may be slower and CPU capacity is then part of the inference budget. GPU acceleration becomes useful when the selected runtime supports the GPU, the model fits in available VRAM and latency or concurrency justifies the extra cost.
Docker’s official 2026 agentic-AI tutorial lists 3.5 GB of VRAM and 2.31 GB of storage as minimum requirements for its example application running as a complete local Docker Compose stack. Those are prerequisites for that example, not a universal requirement for every agent or model. A different model, context size or quantization can need substantially more VRAM.
Check three compatibilities before ordering a GPU VPS: the provider’s driver and runtime support, the model’s VRAM requirement at the intended context length, and whether the inference framework can use the advertised accelerator. If any of those is uncertain, external inference may be more predictable than purchasing a large GPU instance.
Plan storage as a capacity budget
Storage consumption is cumulative. Account for each category instead of allocating space only for the application binary:
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- Model files: every model and quantization variant kept locally.
- Container images and layers: the agent, inference server, browser and database images, plus older layers retained by updates.
- Vector stores and indexes: embeddings, metadata and rebuildable or persistent indexes.
- Logs and traces: tool calls, prompts, errors and audit records, which can grow continuously.
- Databases and user data: conversations, task state, uploaded files and browser profiles.
- Backups: local snapshots or staging copies before they are transferred elsewhere.
SUSE’s 2025 AI requirements document recommends at least 50 GB of free disk space for container images and data processed by Ollama. Treat that as free space available after the operating system and baseline services, not as the total advertised disk size. Keep monitoring free space and set log rotation before the filesystem reaches capacity.
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How concurrency changes the VPS size
Concurrency is usually the reason a configuration that worked in testing fails in production. Each simultaneous agent can have its own context, tool subprocesses, browser tab and database connections. CPU demand rises with parallel tool execution; RAM rises with active contexts and processes; storage rises with retained artifacts.
Scale vertically when a single host is simple and the workload is small. Isolate inference when model memory or GPU scheduling interferes with the agent runtime. Separating the database, browser workers or queue consumers can also make failures easier to contain. For several agents, measure peak rather than average utilization and leave room for deployment tasks, health checks and recovery.
A practical VPS selection procedure
- Classify inference. Write down whether every model call uses a hosted API, a local model, or a mixture. Mixed deployments need capacity for the local components only.
- List always-on services. Include the agent process, database, vector store, browser automation, queue, reverse proxy and monitoring. Count sidecars and containers, not just the main application.
- Estimate concurrency. Define interactive users, scheduled jobs and background workers separately. Note the largest expected prompt, retrieved document set and tool output.
- Set a storage budget. Add model files, image layers, indexes, logs, user data and backups. Reserve free space for updates and temporary files; do not allocate the entire virtual disk to current data.
- Choose CPU and RAM from peak behavior. Use the table’s hosted-agent tier for a starting point, or the local-model planning tier when inference is on the VPS. Treat both as hypotheses to validate.
- Validate the exact stack. Run representative tool calls and the intended model at the expected context length and concurrency. Record peak RAM, CPU, disk growth, latency and error rates before committing to a production size.
- Define a scale and recovery path. Decide which service will be moved, replicated or throttled when a limit is reached. Confirm that backups and snapshots can be restored on a larger instance.
Common sizing mistakes and fixes
Buying a GPU for an API-only agent
If the VPS never loads model weights, a GPU adds cost and operational complexity without moving inference closer to the process. Spend first on reliable CPU, RAM, SSD capacity and network connectivity.
Counting model-file size as total RAM
Loading weights is only part of local inference. Runtime buffers, context caches, the operating system and agent tools also need memory. Verify peak working-set usage with the actual model and prompt length.
Ignoring browser and database processes
Browser automation and databases can dominate a small VPS even when token generation occurs remotely. Include them in the same measurement window as the agent and limit parallel workers if memory spikes.
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Filling the disk with logs or image layers
Set retention and rotation policies, remove unused container layers during maintenance, and keep backups outside the primary data volume when possible. Alert before free space becomes critically low.
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Minimum prerequisites only show that an example stack can start. They do not establish tokens per second, agent success rate or guaranteed concurrency for your workload. Validate those outcomes yourself with representative tasks.
Privacy, latency and operational trade-offs
Hosted inference keeps model execution off the VPS and usually simplifies upgrades, but requests and tool results must traverse the network and follow the provider’s data-handling terms. Local inference gives tighter control over the private network and data path, at the cost of model storage, hardware management, updates and capacity planning.
Choose based on the workload rather than on a GPU-first assumption. A hosted model is often the simpler choice for a small agent with intermittent use. Local inference becomes more compelling when network isolation, custom software, predictable data residency or high sustained usage outweighs the additional RAM, disk and accelerator requirements.
Bottom line
For an agent that calls a hosted model, begin with 2 vCPUs and 4 GB RAM for development and 4 vCPUs with 8 GB RAM for a modest production service, using SSD storage with room beyond the 20 GB practical floor. For local inference, plan around the exact model: a 7B-class deployment commonly warrants 16 GB or more of RAM, local-model data needs a much larger disk reserve, and GPU or VRAM is determined by the model and quantization. Measure peak concurrency, context length, browser activity and storage growth before treating any VPS size as production-ready.
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