How to Run AI Models Locally on Your PC for Free Today

You can get a local AI chat working with a free desktop app, a compatible model and a few setup steps. You do not need to code to try it. A local runner loads and runs a downloaded model on your computer, rather than sending each prompt to a cloud service.

That can give you more control over your data and let you chat without an internet connection after setup. “Free” means free software and downloadable models, not free electricity, hardware, or every model license. This guide focuses on one first chat, not building a cloud-scale system.

Quantization means reducing the precision used to store model weights so they take less memory, with some possible tradeoff in output quality. A small model that fits your computer is a better first choice than a large model that will not load.

Key Takeaways

• Choose a model that fits your available memory.
• Use a local runner to load and chat with it.
• Download model files before trying an offline chat.
• Check a model’s license before sharing or selling access.
• Try a short prompt before trusting a response for important work.

What changes when AI runs on your computer?

With local inference, your computer runs the downloaded model instead of sending each prompt to a cloud AI service. Your computer handles the work, which can give you more control over where prompts are processed.

Local use can help when you need offline access or want to avoid sending everyday data to a provider. LM Studio can run entirely offline after downloading model files. By contrast, cloud inference sends prompts to a provider’s servers, which may be easier to access across devices.

Local processing alone does not prove that every app setting or feature keeps all data on your computer.

Downloads, model updates, and app settings still matter. Some features may contact online services, even when the model itself runs locally. Review what your runner does before relying on it for private work.

For a broader comparison of local runners and hosted chatbot alternatives, think about how much control you want alongside convenience. After downloading the model, test whether the runner still responds with your internet connection switched off.

Will your computer run a local model?

A compatible operating system and enough memory for the chosen model matter more than owning the newest graphics card. Check your setup before downloading, because local AI hardware varies across computer systems.

Use the runner’s exact system requirements, not a universal model-size chart. RAM, unified memory, and dedicated VRAM serve different roles, so do not add their amounts together as one memory limit.

Mac and Linux compatibility

LM Studio supports Apple Silicon Macs with M1, M2, M3, or M4 chips, using macOS 14.0 or newer. It recommends 16GB or more of RAM. Some 8GB Macs may run smaller models with modest context sizes; Intel-based Macs are not currently supported.

For Linux, LM Studio supports x64 and ARM64 as an AppImage. It requires Ubuntu 20.04 or newer, while Ubuntu versions newer than 22 are not well tested.

Windows and GPU capacity

Windows support includes x64 and ARM, such as Snapdragon X Elite. The x64 version needs AVX2, and LM Studio recommends at least 16GB of RAM and 4GB of dedicated VRAM.

A GPU can improve speed, but it is not a universal requirement for running AI locally. An RTX 4060 with 8GB of VRAM ran Qwen 3 8B Q4_K_M at 42.55 tokens per second in one test. That Windows 11 test used a 4096-token context and a 4.68GB GGUF file, not a promise for other computers.

The RTX 5070 has 12GB of GDDR7, while the RTX 5070 Ti has 16GB. Both use Blackwell architecture and fifth-generation Tensor Cores, but their memory and power needs differ.

A research paper on GPU memory limits and inference speed explores advanced memory offloading, not a consumer-PC setup. Before installing, use this four-point checklist:

• Operating system version
• Available RAM or unified memory
• Dedicated VRAM, if present
• Available storage for model files

Check the exact operating-system and chip requirements for your runner before downloading it.

Which local model should you start with?

For a first try, choose the smallest model that can do your main task while leaving memory for its context. Start modestly, then try a larger model if your computer can handle it.

Quantization reduces the precision used to store model weights, which lowers memory use but can affect quality. Weights are only part of memory use: context, runtime overhead, and the KV cache also need room.

Match model size and context to memory

A 27B model at exactly four bits per parameter has 13.5GB of language weights in decimal units. That is before quantization metadata, runtime buffers, KV cache, and context growth.

A 70B model’s weights take about 140GB at FP16, 70GB at INT8, or 35GB at four bits. At FP16, its KV cache adds about 0.3GB per 1,000 tokens, or around 2.5GB for 8K context and nearly 10GB for 32K.

A model’s advertised context does not mean your computer can serve it at full length. Qwen3.6-27B lists a 262K native context, yet its full-context serving examples use eight GPUs with tensor parallelism. Its instructions advise reducing context after an out-of-memory error.

Pick a model for your first task

Gemma 4 12B accepts text, image, and audio input. GPT-OSS 20B takes text only, and its public capability score is 33.29. Qwen3.6-27B has a public capability score of 48.53, but scores do not guarantee everyday output quality.

MXFP4 GPT-OSS 20B can run within 16GB of memory, though that is not a guarantee for every runner or context. Smaller models make a sensible first fit when memory is limited. Choose multimodal models only when you need image, audio, or video input; NVIDIA Nemotron 3 Nano Omni handles all four, including video.

GGUF is a model-file format used by local runners. Labels such as Q4 identify quantized weights, and a lower-memory file can have different output quality.

If a model runs out of memory, reduce its context or choose a smaller quantized file before assuming the computer cannot run local AI.

Which local runner fits your comfort level?

The easiest runner is usually the one whose interface you will actually use, not the one with the most settings. Choose for your workflow, since local runners are separate tools with different features and model support.

LM Studio offers a graphical setup flow for finding and loading downloaded models. Ollama supports customized models through a Modelfile and command-line commands. Neither option makes every model or feature interchangeable.

Jan and llama.cpp are other local-runner options. To compare the broad use cases for LM Studio and Ollama, consider how you prefer to work, rather than assuming one runner fits every computer.

• Choose a graphical interface for point-and-click setup.
• Choose a command line for repeatable commands and configuration.
• Choose a hands-on engine when you want lower-level control and can handle extra setup.

A graphical app can make discovery easier, while a command line can make a repeated setup easier to record. Your model and runner must still work together.

Start with one runner and one model, then add alternatives only when you have a specific need.

How to Run AI Models Locally with LM Studio

You can get a first LM Studio conversation working by downloading one compatible model and loading it from the Chat tab. The basic setup takes a few choices in the app, not coding.

Downloading needs internet access, but a downloaded model can run offline. Loading allocates memory for its weights and other parameters, so keep the model size suited to your computer.

Install, download and load a model

Follow these steps to deploy your own model for personal use:

1. Install LM Studio on your computer.
2. Open Discover and download a compatible model.
3. Open Chat and select the model in the model loader.
4. Load it into memory and send a test prompt.

Once loaded, the local runner processes your prompt. If the model does not load, its memory needs may exceed what the computer can make available.

Chat offline or connect a coding tool

For ordinary chat, you do not need an API connection. Download a compatible model, load it in the runner, and send a prompt.

An API is an optional advanced step when another app needs access to the model. LM Studio 0.4.1 provides an Anthropic-compatible /v1/messages endpoint with streaming and tool use.

Start its local server on port 1234 with lms server start --port 1234. Claude Code integration recommends a context size of at least 25K tokens.

For a coding tool, check that its expected API matches the server endpoint. A model that works in chat may need a different setup for another app.

Before using the model for important work, try a short prompt and confirm the response is coming from the local runner.

How do you customize a model with Ollama?

Ollama’s Modelfile lets you set a base model and add configuration instructions for a reusable custom model. Use one when you want a repeatable setup instead of only a ready-made model.

Every Modelfile needs a FROM instruction to name its base model. It can also include PARAMETER, TEMPLATE, SYSTEM, LICENSE, MESSAGE, CAPABILITY, and REQUIRES instructions.

For example, a Modelfile can set a system message or a context parameter. The reference defaults include a 2048-token context, temperature 0.8, repeat_last_n 64, and repeat_penalty 1.0. Those are starting values, not ideal settings for every model or workload.

Use these steps to create and run a custom model:

1. Set FROM to the base model.
2. Add only the configuration instructions you need.
3. For a local GGUF, set its absolute path or a path relative to the Modelfile.
4. Create and run the named model with Ollama commands.

Use ollama create choose-a-model-name -f <location of the file> to create it. Then run ollama run choose-a-model-name.

Keep a copy of the Modelfile beside the model details so you can reproduce the same setup later.

What should you check before sharing a model?

A model that costs nothing to download can still have conditions on redistribution and commercial use. Personal use is not the same as sharing model files or offering access through a product.

The Llama 3 Community License sets specific rules for some uses. For a product or service with more than 700 million monthly active users in the preceding calendar month on its April 18, 2024 release date, the license requires requesting a license from Meta.

Check the exact license attached to the model you downloaded before making it available to others.

The Llama 3 license grants a non-exclusive, worldwide, non-transferable, royalty-free limited license for its materials. Distributors must provide a copy of the agreement and display “Built with Meta Llama 3” prominently on a related site, interface, blog, about page, or product documentation.

A model created, trained, fine-tuned, or improved using Llama Materials and then distributed or made available must begin its name with “Llama 3.” Read the exact model license before sharing model files or making a model available through a product.

Conclusion

You do not need a high-end workstation to begin experimenting with local AI. One modest model and a familiar runner can show you how local chat works.

Response speed and capability depend on your computer, model size, and context. A smaller model may answer more slowly or handle fewer tasks than a cloud service, but it can still help you test local use.

Pick a simple first task, such as summarizing a short note or drafting a grocery list. Avoid sensitive information until you understand how your app handles its settings and data.

Choose one model that fits your available memory, download a local runner and test it with a prompt that does not contain sensitive information.