You can run a cloud-backed AI agent at home on a modest computer because the provider runs the language model. Running the model locally is different: memory—especially GPU VRAM or Apple unified memory—must accommodate the model, its context, the inference software, and other workloads. For a practical local starting point, NVIDIA’s 2026 guide suggests 6–8GB of RTX VRAM for Qwen 3.5 4B, 12–16GB for Qwen 3.5 9B or Gemma 4 12B, and 24GB or more for Qwen 3.6 27B. These are vendor recommendations, not guarantees of speed or agent quality.
First decide where the model will run
An AI agent is the software that plans tasks and uses tools; a language model supplies its responses and decisions. The agent can run on your home computer while sending model requests to a cloud provider, or both the agent and model can run locally. NVIDIA describes OpenClaw as supporting either local or cloud language models (NVIDIA’s OpenClaw setup guide).
- Cloud model, home-hosted agent: Local model VRAM tiers do not apply, because the provider performs inference. Your computer still needs to run the agent and any local services or tools you connect. Relevant prompts or data may be sent to the provider.
- Local model and agent: Your machine must hold the model and support its inference runtime. Memory capacity, context length, and other simultaneous workloads all affect whether a model fits and remains usable.
OpenClaw’s managed local-model recipes have an 8 GiB host-memory floor, but that threshold is not a guarantee that a model will fit comfortably or run quickly. Its documentation says fit depends on model weights, context, runtime, and other host workloads (OpenClaw local-model documentation).
How much memory should a local model have?
For NVIDIA RTX GPUs, the following are starting recommendations in NVIDIA’s guide, checked October 4, 2026. They are not universal minimums, independent benchmarks, or promises of a particular response speed.
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| Local model tier | NVIDIA’s suggested starting point | What it tells you |
|---|---|---|
| Qwen 3.5 4B | 6–8GB of RTX VRAM | An entry point for local inference experiments; the recommendation does not establish how well the model will handle your agent tasks. |
| Qwen 3.5 9B or Gemma 4 12B | 12–16GB of RTX VRAM | A larger starting model tier, with context and other GPU use still competing for memory. |
| Qwen 3.6 27B | 24GB or more of RTX VRAM | More room for larger model weights, but actual fit and performance also depend on quantization, context, and the rest of the system. |
| Qwen 3.6 35B | NVIDIA recommends DGX Spark, a platform it says has 128GB of memory | A vendor platform recommendation, not a general household value recommendation. |
These figures come from NVIDIA’s RTX large-language-model guide and its OpenClaw model setup playbook. They do not establish equivalent requirements for every model, GPU vendor, or operating system. Apple systems use unified memory rather than a separate GPU VRAM pool, so check the model and runtime requirements for the specific device and software you plan to use.
Why agent workloads need more than enough memory to load a model
An agent turn can include instructions, tool descriptions, conversation history, and the model’s generated output. OpenClaw advises leaving room for those demands and testing real tasks before making a local model the default. A model that loads for a short chat may still run out of memory, slow down, or be unsuitable when asked to process a longer history and call tools.
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Context length changes the fit
NVIDIA recommends at least a 32K context for its OpenClaw local setup and suggests 64K or higher when memory headroom permits. A longer context can let the model consider more input, but it also uses more memory; a GPU that fits a model at a shorter context may not have enough spare capacity at a longer one. See NVIDIA’s OpenClaw setup playbook for its context guidance.
Quantization trades memory use against quality
NVIDIA notes that quantization can reduce the memory required to run a model, while aggressive quantization can hurt response quality. Choosing a smaller or more compressed model may help with hardware fit, but neither choice alone establishes whether it will perform well on your agent’s specific work. NVIDIA recommends choosing the largest model that fits comfortably (RTX LLM guide).
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Choose a serving setup your computer supports
The inference software connects the model to the hardware. OpenClaw can manage a local llama.cpp server with hardware-aware recommendations or connect to a separately managed model server; its documentation also lists LM Studio, Ollama, and OpenAI-compatible servers. NVIDIA presents LM Studio and Ollama as straightforward serving options for discrete GPUs, and vLLM as a more configurable Linux path (OpenClaw local models; NVIDIA RTX guide).
Compatibility depends on the exact GPU, operating system, driver, and backend. Ollama documents NVIDIA support subject to compute-capability and driver conditions, AMD support for specified ROCm configurations, and Metal acceleration on Apple devices. Check its current GPU support documentation against the exact hardware and software versions before buying or configuring a machine.
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Check fit before you buy or make a model the default
- Choose cloud or local inference. If the model will run at a provider endpoint, do not use local-model VRAM tiers as a purchase requirement. If inference will be local, identify the exact model and serving software.
- Check the model’s memory needs and context setting. Account for weights, the intended context length, runtime overhead, and other applications using system or GPU memory. OpenClaw’s setup checks available RAM, supported GPU memory, and disk space; its 8 GiB managed-recipe floor does not promise usable performance (OpenClaw documentation).
- Verify hardware and software compatibility. Confirm that the GPU or Apple device, operating system, driver, and inference backend are supported together. For Ollama, consult its current GPU compatibility details.
- Test representative agent work. Try the kinds of turns you expect to use, including tool calls, realistic conversation history, and the intended context length. A short prompt that produces a reply is not a sufficient test of an agent workflow; OpenClaw advises testing before setting a local model as the default (OpenClaw local-model documentation).
NVIDIA summarizes its model-first approach this way: “The easiest way to get started is to choose a model that fits your GPU, then choose the app that matches what you want to do” (NVIDIA RTX LLM guide).
Plan for safe, always-on operation
Hardware is only part of a home deployment if the agent can access files, accounts, or tools. NVIDIA warns that those connections can expose personal information and the host to malicious code or attacks. Its March 15, 2026 OpenClaw guide recommends using a separate clean PC or virtual machine, dedicated agent accounts, sharing only selected information, vetting third-party skills, protecting interfaces with authentication, and limiting internet access when the task allows.
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OpenClaw also notes that local models do not include the safety filters provided by hosted model services. Restrict what tools and files the agent can access, and treat untrusted inputs and third-party extensions cautiously even when inference stays on your own machine (OpenClaw local-model documentation).
What these recommendations do not establish
The cited vendor and project documentation does not provide an independently measured household performance ranking across platforms, typical electricity use, or current retail prices. The VRAM tiers therefore cannot tell you which exact computer is best value or promise a particular generation speed. Actual results depend on the selected model, quantization, context length, runtime, and machine configuration.
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