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Run Meta Llama on your own computer instead of calling a hosted model. Install a local runtime such as Ollama or llama.cpp, download and license the model weights, and select a local model rather than a cloud endpoint. For sensitive work, download and verify everything on an internet-connected machine, transfer it to an offline computer, disable integrations and outbound networking, then confirm that a harmless prompt still works with the network disconnected.
What “private” Llama actually means
Local inference means the model files, prompt, generated response and conversation history are processed on a device you control. A local user interface or API can still leak data if it invokes web search, remote retrieval, telemetry, browser extensions, plugins or a cloud model. Privacy therefore depends on the entire path, not just on the word “local” in an app’s description.
A private setup has four conditions:
- The selected model is stored on your computer or another device inside your controlled network.
- The runtime is configured to load that local model, not a cloud model name or remote API base URL.
- Optional tools that send text elsewhere are disabled or removed.
- Outbound connections are blocked when your policy requires a technical guarantee rather than a configuration promise.
Once the model files are present, ordinary text generation does not require an internet connection. Internet access is still useful for deliberate updates, downloading a different revision or checking documentation, so separate maintenance from sensitive use.
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Choose a runtime that can stay offline
| Runtime route | Best fit | Offline and privacy controls | Trade-offs |
|---|---|---|---|
| Ollama local mode | Fastest beginner setup on a desktop or laptop | Keep the model name local, avoid cloud model names, and use a localhost-only service | Large models can be slow without a strong GPU; fewer low-level tuning choices |
| llama.cpp | People who need portability, CPU or GPU choice, and detailed tuning | Run the command-line tools or local server without internet access, bind the server to localhost and firewall outbound traffic | More decisions about binaries, model format, quantization and launch options |
| vLLM or TensorRT-LLM | High-throughput, self-hosted service for a team or internal application | Keep model storage and the inference server inside your network; expose only the internal interface you need | Higher operational complexity, stronger GPU requirements and more administration |
| ExecuTorch and other edge runtimes | Phones, laptops and embedded devices | Inference stays on the device when the model and application are packaged locally | Supported models, operators and accelerators vary by device |
When Ollama is the practical choice
Ollama provides a desktop and command-line path that keeps open models on the computer when you choose local mode. It is appropriate if you want a short installation, a local model catalog and a simple local chat or API workflow. Do not assume every model entry is local: cloud model names and remote API settings defeat the privacy boundary. Inspect the model name and the runtime’s connection settings before entering confidential text.
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When llama.cpp is worth the extra setup
llama.cpp is useful when you want portable binaries, a Docker deployment, a source build, a local server or explicit control over quantization. It supports GGUF models and quantized inference, and can use CPU, Apple Silicon, CUDA, HIP or Vulkan acceleration depending on the build and hardware. That breadth makes it a strong option for an offline workstation, a small server or a machine that does not fit a single vendor’s GPU stack.
When a server-oriented runtime is justified
vLLM and TensorRT-LLM make sense when several users or applications need sustained throughput from a managed internal server. They add deployment, authentication, monitoring and GPU-capacity work that a single-user laptop does not need. Keep the service, model files and logs on infrastructure you control, and treat every client integration as a potential data-exfiltration path.
Match model size to memory, storage and speed
Start with the smallest Llama variant that satisfies the task. Meta’s deployment examples list the following FP16 memory figures:
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| Llama 3.2 3B | 6 GB FP16 | An example memory requirement for the model in FP16; leave additional headroom for the runtime, operating system and context. |
| Llama 3.2 11B | 22 GB FP16 | An example memory requirement for the model in FP16; a machine with less available memory needs quantization, hybrid execution or a smaller model. |
| Llama 3.3 70B | 140 GB FP16 | An example memory requirement that generally implies a multi-GPU or high-memory server unless a lower-precision format is used. |
| Llama 3.1 405B | Approximately 750 GB of disk storage | Meta’s model-download documentation figure for storing the files; storage capacity is separate from the RAM or VRAM needed to run inference. |
These are not speed guarantees. Actual generation rate and responsiveness depend on the CPU, GPU, available RAM or VRAM, quantization, context length, operating system, thermal limits and runtime settings. A model that technically fits may still be too slow for interactive use.
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How quantization changes the decision
Quantization stores weights with fewer bits, reducing memory pressure and often making a model practical on a laptop or a single GPU. llama.cpp documents formats from about 1.5-bit through 8-bit and supports CPU/GPU hybrid inference. Lower-bit formats generally trade some output quality or consistency for lower memory use; the size of that trade-off varies by model, quantization method and workload. Compare a few representative prompts before standardising on a format.
Plan storage separately from working memory. Keep enough free space for the model file, temporary downloads, alternate revisions and runtime caches. A 2 TB external SSD can hold multiple model files and revisions, but it does not add RAM or GPU capacity. If you use removable storage, encrypt it and protect the filesystem permissions as you would an internal disk.
Accept the license before obtaining weights
Meta makes Llama weights available directly from Meta, Hugging Face and Kaggle. Direct downloads require accepting the applicable model license; Meta then sends a pre-signed download URL that expires after 24 hours or five downloads. Save the exact model revision and a checksum with your offline copy so you can identify what was approved and detect corruption or substitution.
Licensing is version-specific. The Llama 4 Community License Agreement is effective April 5, 2025 and grants a limited, non-exclusive, worldwide, royalty-free license subject to its terms. It also incorporates Meta’s acceptable-use policy. If you distribute covered Llama materials or a product containing them, the agreement requires including the agreement and prominently displaying “Built with Llama.” Review the license for the precise model version, your country and your intended distribution or commercial use before release.
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Download and verify a model
- Select a revision and format. Record the model family, parameter size, precision or quantization, source and revision identifier. Choose a format your runtime supports, such as GGUF for llama.cpp.
- Complete the license step. Use the model provider’s acceptance flow before requesting the files. Keep a copy of the applicable terms with your deployment records.
- Download on a connected staging machine. Use the pre-signed URL within its validity window. Do not paste sensitive prompts into the staging machine; it is only handling model files.
- Calculate and record a checksum. Hash each file with your operating system’s checksum utility and store the result beside the model revision. A checksum lets you detect an incomplete transfer or a changed file.
- Transfer through a controlled path. For an air-gapped computer, use an encrypted removable drive or another approved one-way transfer process. Scan and verify the files before disconnecting the staging machine.
- Verify again on the destination. Recalculate the checksum after transfer. Do not load a file whose hash differs from the recorded value.
Beginner setup with a local Ollama model
Use this route when you want the least configuration. The exact model identifier depends on the catalog and revision you downloaded; use a local identifier rather than a cloud variant.
- Install the desktop application or command-line package on the computer that will process prompts. During installation, note where it stores model files and logs.
- Place or import the approved weights using the format and import mechanism required by your model and Ollama version. Keep the checksum and revision in the same controlled inventory.
- Run the local model. In a terminal, use the local model command, for example
ollama run <local-model-name>. Replace the bracketed value with the identifier shown by your local installation; do not substitute a cloud model name. - Confirm the endpoint. The chat window or API should point to the computer itself, normally through a localhost address. Remove remote API base URLs from scripts, environment variables and editor extensions.
- Test with networking disabled. Disconnect Wi-Fi or unplug Ethernet, send a harmless prompt and confirm that the same local interface responds. If it fails, inspect the runtime configuration rather than reconnecting and entering sensitive data.
For a shared workstation, create separate operating-system accounts or service permissions so another user cannot read model caches, prompt logs or shell history. Review the application’s update and telemetry settings before using it for confidential work.
Controlled setup with llama.cpp
llama.cpp gives you more control over the model file, accelerator and network surface. Obtain a binary, container or source build appropriate for the operating system, then keep the GGUF model on local storage.
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- Point the command-line tool at the verified file. A typical local invocation has the form
llama-cli -m /models/model.gguf -p "test prompt". Use your actual path and model file; the command is illustrative of a local file load, not a requirement to use one particular build. - Use a local server only when you need an API. Start the server with the model path and bind it to localhost or an internal interface that your policy permits. Do not bind an offline workstation to all network interfaces.
- Keep remote retrieval out of the application. Ensure client code sends requests to the local server address and does not include a remote API base URL, web-search connector or retrieval plugin.
- Constrain resources deliberately. Select a context length and quantization that fit available memory. If the process swaps heavily or the operating system kills it, use a smaller model, lower-bit quantization or CPU/GPU hybrid execution.
- Repeat the disconnected test. Stop networking, submit a harmless prompt and verify that both the command-line tool and any local API still respond.
Build an air-gapped workflow for sensitive prompts
An air gap is stronger than a preference setting because the computer has no usable network path. It also creates update and transfer responsibilities.
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- Prepare on a connected machine. Accept the license, download the runtime package and model revision, calculate checksums and record the versions.
- Inspect the transfer media. Use encrypted removable media or an approved transfer mechanism. Verify the model checksum before and after copying.
- Install while disconnected. Disable Wi-Fi, unplug network cables and remove unnecessary network adapters before launching the runtime.
- Remove network-dependent components. Disable browser extensions, plugins, web search, remote retrieval, automatic model downloads, crash uploads and telemetry that is not required for your policy.
- Apply an outbound firewall rule. Block the runtime and its helper processes from making outbound connections. A firewall rule provides a technical backstop if an integration is later enabled accidentally.
- Run a harmless acceptance test. Ask a non-sensitive question and check that the local UI or localhost API returns a response while the network remains disabled.
- Inspect evidence of isolation. Review runtime logs, operating-system connection lists and firewall events. Investigate unexpected DNS lookups, sockets or failed remote calls.
- Control updates as a separate change. Re-enable connectivity only for a planned maintenance window, download and verify new files, then restore the firewall and repeat the offline test.
Harden the privacy boundary even on a connected workstation
Disable tools that can transmit text
- Turn off web search and browsing features unless the task explicitly requires them.
- Remove retrieval connectors that fetch documents from a remote service.
- Disable browser extensions, plugins and editor assistants that can read the current page or file.
- Turn off automatic telemetry, prompt analytics and crash uploads that are not necessary for operation.
- Check shell history, application logs and chat exports for prompts that should not be retained.
Keep interfaces local
Prefer a localhost-only listener for a single computer. If another device must connect, place the service on a private network, require authentication and expose only the required port. Do not publish a local inference server directly to the public internet. Remove remote API base URLs from configuration files, environment variables and scripts, and inspect the effective settings after every update.
Separate model privacy from data-at-rest security
Local inference prevents a provider from receiving prompts over the service you did not call, but anyone who can read the computer can potentially read model caches, logs, temporary files or saved conversations. Use full-disk encryption, operating-system permissions, locked sessions and a retention policy appropriate to the data. Delete temporary copies from staging media after a verified transfer when policy requires it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test and troubleshoot before trusting the setup
| Symptom | Likely cause | Action |
|---|---|---|
| The model works only when the internet is connected | A cloud model name, remote API URL, online retrieval tool or automatic download is being used | Replace it with the imported local model, remove remote URLs, disable integrations and retest with networking disabled. |
| The process exits or the computer becomes unresponsive | Insufficient RAM or VRAM, an oversized context or a quantization that does not fit | Choose a smaller model, reduce context length, use a lower-bit quantization or enable CPU/GPU hybrid inference. |
| Responses are extremely slow | The model is running mostly on CPU, the model is too large, or thermal throttling is occurring | Use a smaller or quantized model, select the correct accelerator build and check memory and temperature limits. Do not treat a theoretical fit as an interactive-speed guarantee. |
| The imported file is rejected | Unsupported format, incomplete transfer or a checksum mismatch | Confirm the runtime’s required format, recalculate the hash and recopy the file from the verified staging copy. |
| A local API is reachable from other machines unexpectedly | The server is bound to all interfaces instead of localhost | Change the bind address, restrict the firewall and verify the listening sockets after restart. |
| Sensitive text appears in logs | Conversation logging, debug mode or shell history is enabled | Disable unnecessary logging, secure or purge existing logs according to policy and test with a harmless prompt before resuming work. |
Choose the right privacy level for your situation
Personal experiments
A local Ollama installation with a small quantized model is usually sufficient. Keep the model local, avoid cloud model names, disable web tools and verify once that the interface still works with Wi-Fi disabled.
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Use a dedicated operating-system account or computer, local-only binding, encrypted storage, controlled logs and an outbound firewall rule. Import only verified model files and do not paste confidential documents into a connected staging machine.
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Regulated or highly sensitive workloads
Use an air-gapped or tightly segmented host, document the model revision and license, enforce removable-media controls, retain connection and firewall evidence, and define who may update or replace the model. A local runtime is a technical component of the control set, not a substitute for organizational access, retention and incident-response policies.
Internal applications and teams
A self-hosted server runtime can serve multiple clients, but every client, log sink and monitoring agent expands the privacy boundary. Keep storage and inference inside the approved network, authenticate callers, limit retained prompts and review network flows after deployment.
License and distribution checklist
- Identify the exact Llama family, revision and license before deployment.
- Record acceptance of the applicable terms and preserve the text with the model inventory.
- Check acceptable-use requirements for the intended application and geography.
- If distributing model materials or a product containing them, include the agreement and prominently display “Built with Llama” when the applicable agreement requires it.
- Have counsel or your compliance owner review commercial release, redistribution and geographic restrictions before shipping.
A repeatable pre-prompt checklist
- Is the selected model stored locally and identified by a recorded revision?
- Does the runtime configuration contain only a local model path or local model name?
- Are web search, retrieval, plugins, extensions and unnecessary telemetry disabled?
- Is the service bound to localhost or an approved private interface?
- Is outbound traffic blocked when the data classification requires it?
- Did a harmless prompt succeed with networking disabled?
- Are logs, caches, temporary files and removable media protected?
If any answer is no, treat the setup as not yet private. Correct the configuration and repeat the disconnected test before entering sensitive material.
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