To run a coding model locally, install a runtime, download model weights it supports, and load a model that fits your computer’s available memory. Choose LM Studio for a graphical workflow, Ollama for a straightforward terminal and local API workflow, or llama.cpp for direct control over model files and compute settings. Local inference can work offline once the model files are on your computer, but model quality, speed, and memory use depend on the model and your hardware.
Choose a runtime for your setup
| Runtime | Setup style | Model files and control | Local API | Best fit |
|---|---|---|---|---|
| LM Studio | Graphical app: discover and download a model, load it from Chat, then prompt it. | Supports model weights in formats such as GGUF and safetensors; model-specific compatibility and license still matter. | Provides local REST and OpenAI-compatible APIs. | People who prefer a desktop interface and want to try models without starting in a terminal. |
| Ollama | Terminal commands for downloading, listing, and running models. | Uses models available through its catalog; names and sizes can change. | Provides a local REST API. | People who want a simple command-line workflow or a local endpoint for supported software. |
| llama.cpp | Command line; install through a package manager, Docker, prebuilt release, or source build. | Requires GGUF files and exposes options for quantization and CPU/GPU use. | Its llama-server can provide an OpenAI-compatible server. |
People who want more direct control over model files, backends, or serving. |
These differences describe workflow, not coding quality or speed: the official documentation cited here does not establish a universal performance ranking. For setup details, see LM Studio’s getting-started guide, the Ollama quickstart, and the llama.cpp README.
Check memory and storage before downloading
There is no single minimum specification for running a coding model. Requirements change with the model’s size, quantization, context length, runtime, and the amount of work handled by the GPU. A model’s download size is not the same as the memory it needs while running.
Runtime-specific guidance
- LM Studio: Its system requirements page recommends 16GB or more of RAM for Apple Silicon Macs; it says Macs with 8GB may still work with smaller models and modest context sizes. For Windows, it recommends 16GB RAM and at least 4GB dedicated GPU VRAM; x64 systems require AVX2. Its listed support includes Windows x64 and ARM, Linux x64 and ARM64, and macOS 14 or newer on Apple Silicon M1, M2, M3, or M4. These are LM Studio’s requirements and recommendations, not universal requirements for other runtimes.
- Ollama: Its quickstart suggests at least 8GB available RAM for 7B models, 16GB for 13B models, and 32GB for 33B models. Treat those as rules of thumb, not guarantees for every quantization, context length, or computer.
Keep download size separate from runtime memory
Ollama’s quickstart lists illustrative download sizes of 1.3GB for Llama 3.2 1B, 2.0GB for Llama 3.2 3B, 4.7GB for Llama 3.1 8B, and 40GB for Llama 3.1 70B. These are model-file sizes, not total RAM or VRAM requirements. Catalog contents and sizes may change.
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Plan disk space for the models you intend to keep. An external SSD can help if internal storage is limited or you want to retain several model files, but no universal capacity or speed requirement is established here. Storage does not replace the system memory or GPU memory used while a model runs.
Install a runtime and load a model
LM Studio: use the graphical app
- Install LM Studio using its official getting-started guide.
- Open the Discover tab, find a model, and download it. The guide gives Qwen, Mistral, Gemma, and gpt-oss as examples; check a specific model’s hardware needs and license rather than treating an example as a recommendation.
- Open the Chat tab and load the downloaded model. Loading allocates memory for the weights and other parameters.
- Send a small coding prompt to check that the model loads and responds as expected.
Ollama: use the terminal
After installing Ollama, its quickstart documents these commands:
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ollama run llama3.2downloads the model if needed and starts an interactive session.ollama pull llama3.2downloads a model without starting a chat.ollama listshows models available locally.ollama psshows models currently loaded.
llama3.2 is a quickstart example, not a permanent catalog recommendation. Model names, availability, and sizes can change; check Ollama’s current catalog and the quickstart before choosing a model.
llama.cpp: run a GGUF model directly
- Install llama.cpp using one of the routes in its README: a package manager, Docker, a prebuilt release, or a build from source.
- Obtain a compatible GGUF model. The README shows how to download a compatible model with the
-hfoption. - Run a local model file with
llama-cli -m my_model.gguf, replacing the example filename with your actual file path. - For a local API server, use
llama-serveras documented in the README and configure the client to use the server’s compatible endpoint.
Make a coding model fit your computer
Quantization changes how model weights are represented and can reduce memory use, but it may also affect output quality. llama.cpp documents quantization levels from 1.5-bit through 8-bit, as well as CPU/GPU hybrid inference. Hybrid inference can place some work on system memory when a model does not fit entirely in GPU VRAM; it does not make hardware limits disappear.
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Start with a model that fits your machine, then assess it on the coding work you actually do. Try a small, bounded task—such as explaining a function, drafting a test, or suggesting a fix for a short snippet—and check the result rather than assuming a larger model or a particular quantization will be better for every setup. The available documentation does not identify one ideal model size or quantization for coding across all hardware.
Connect the model to coding software
LM Studio, Ollama, and llama.cpp document local API options; LM Studio and llama.cpp specifically describe OpenAI-compatible endpoints. A client may be able to send requests to a local model through an API, but compatibility depends on the client’s supported API, model interface, and any tool-calling or code-editing features it needs.
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Before configuring an editor or coding agent, check its documentation for custom or local API endpoints and the features it expects from a model. The runtime documentation does not verify that any particular editor extension or coding agent works without additional configuration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check model rights and offline behavior
Downloading a runtime does not provide model weights by itself: you must obtain weights compatible with that runtime. LM Studio notes that models may be distributed as GGUF or safetensors and that licenses vary. llama.cpp requires GGUF files. Review the license for the specific model you download; describing a model as “open” does not establish uniform rights to use or redistribute it.
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Once model files are available, local inference can work offline depending on the runtime and setup. Downloading models, installing software, or using connected features may still require internet access.
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