Five LLMs to Run Locally in 2026

Deploying an LLM on your own device allows you to maintain control over the configuration, provided you choose the right model and understand its constraints. Between pure text and multimodal capabilities, with parameter counts ranging from 3.8 to 8 billion, the use cases differ significantly. Here are the points to check, the models to consider, and a quick start guide with Ollama.

Privacy and licenses are not automatic

Running an AI model on your device does not guarantee that all data remains private. Connected tools, cloud backup solutions, or application telemetry can transmit information outside the device; therefore, the entire configuration must be checked if privacy is a concern. Regarding licenses, Llama 3.1 8B Instruct falls under Meta's Llama Community License, the terms of which must be reviewed before any redistribution or deployment. Mistral 7B Instruct v0.3 is distributed under the Apache 2.0 license.

Text-only, multimodal, and function calling

Some models are strictly textual, such as Llama 3.1 8B Instruct and the Qwen variants, which do not handle images or screenshots. In contrast, Gemma 4 E2B and E4B accept text, images, and audio to generate text, making them suitable for querying documents, understanding images, transcription, or on-device assistance if the environment allows. Mistral 7B Instruct v0.3 supports function calling, while Phi-4-mini, also textual, targets compact local assistants and tooling experiences.

Effective sizes and resources: from 3.8 to 8 billion

The designations E2B and E4B correspond to effective sizes: Gemma 4 E2B has about 5.1 billion parameters including embeddings, while E4B has 8 billion. It is not advisable to size the hardware solely based on the model name. Mistral 7B Instruct v0.3 features seven billion parameters. Microsoft's Phi-4-mini, with 3.8 billion parameters, targets use cases where memory and latency are major constraints.

Match each model to its use case

Llama 3.1 8B Instruct serves as a versatile base for chat, synthesis, or classification, with the Instruct variant suited for assistance. Qwen2.5-7B-Instruct is indicated for multilingual conversations, document processing, and structured outputs like JSON, while Qwen2.5-Coder-7B-Instruct targets code generation, explanation, and repair. For writing Python functions, it is advisable to start with the Coder version, and for extracting fields in multilingual documents, prefer Instruct. Mistral 7B Instruct v0.3 is suitable for rephrasing, summarizing, documentation writing, and daily exchanges, thanks to an instruction tuning designed to convert notes into ready-to-use content. Phi-4-mini-instruct should be prioritized for small local assistants or for testing tools. Regardless of the model, the generated code should be tested before use.

Getting started quickly with Ollama

Ollama is presented as the simplest option for developers: it installs easily, allows downloading models, and running them via command line, for example, using “ollama run gpt-oss:20b.” The platform offers local packages for models like phi-4, Gemma 4, or Qwen3-Coder, and exposes a local API to connect a model to your own applications or coding agents. With the right hardware, these tools enable models to run entirely on-device, maintaining control over the configuration.

Popularity, hardware, and task guide the choice

The selection combines established versions and newer options, with popularity indicating adoption. The relevant choice primarily depends on the task to be accomplished and the available hardware. It is recommended to compare models yourself, especially for older generalist models like Mistral 7B Instruct v0.3. The needs for modality—text-only or image and audio inputs—are also decisive.