As artificial intelligence continues to expand across industries, many users are shifting their focus from cloud-based models to local large language models (LLMs). These offline models offer privacy, flexibility, and freedom from recurring subscription costs. Thanks to open-source ecosystems like Hugging Face, GPT4All, H2O.ai, and Text Generation WebUI, it’s easier than ever to run these powerful models directly on personal devices.

Local LLMs allow users to explore AI capabilities without depending on internet connectivity or centralized platforms. Whether it's for coding, content creation, research, or experimentation, here are 9 of the best local/offline LLMs available today.

1. Hermes 2 Pro GPTQ

Hermes 2 Pro GPTQ is a high-performing language model developed by Nous Research. Based on the Mistral 7B architecture, it has been fine-tuned with over a million instruction-based samples, many reaching GPT-4 quality. It includes functionality like JSON output and function calling, making it suitable for developers and content creators.

With a model size of 7.26 GB and 4-bit quantization, Hermes 2 Pro offers strong performance in code generation, reasoning, and conversation—all while running efficiently on modern personal systems. It supports a wide range of tasks, making it a strong all-rounder in the offline LLM landscape.

2. Zephyr 7B Beta

Zephyr 7B Beta is built on the Mistral 7B foundation but differs in training approach. Using Direct Preference Optimization, this model focuses on creating a helpful assistant without restrictive alignment. It makes Zephyr highly conversational and responsive.

The model handles open-ended tasks with ease and feels more natural in interactions. Though its safety alignment is lighter, it delivers an engaging offline assistant experience and is ideal for users seeking dynamic conversation without relying on cloud tools.

3. Falcon Instruct GPTQ

Falcon Instruct GPTQ is designed for instruction-following applications. Built on the Falcon-7B decoder-only architecture and trained with 1.5 trillion tokens, this model is tuned for inference and not meant for additional fine-tuning.

Its performance shines in structured language tasks like translation, summarization, and form-based responses. With a size of 7.58 GB, it runs well on capable devices and is favored by small businesses and power users looking for a local model to streamline workflow automation.

4. GPT4ALL-J Groovy

It is based on the GPT-J design and was fine-tuned by Nomic AI. It focuses on text generation, making it a favorite among writers and creatives. Whether crafting stories, poetry, or dialogue, this model delivers imaginative content on demand.

It’s trained on English-only data, limiting multilingual use, but it’s lightweight and resource-efficient. With a size of just 3.53 GB, GPT4ALL-J Groovy can operate on mid-range hardware, making it accessible to creators working without internet access.

5. DeepSeek Coder V2 Instruct

DeepSeek Coder V2 Instruct is a model built for developers. It supports over 330 programming languages and has an extended context length of up to 128,000 tokens. It allows it to handle complex code generation, debugging, and logical reasoning tasks.

This 13 GB model, with 33 billion parameters and 4-bit quantization, has outperformed many premium AI tools in coding benchmarks. It’s especially valuable for engineers and programmers seeking a private, offline development assistant that doesn’t sacrifice power or depth.

6. Mixtral-8x7B

Mixtral-8x7B introduces an innovative architecture known as a sparse Mixture of Experts models. Developed by Mistral AI, this model uses eight expert networks, two are actually used for each token during reasoning. This structure allows it to deliver the performance of a 45-billion-parameter model while maintaining the speed and efficiency of a much smaller one.

This model supports multiple languages, including English, German, French, Spanish, and Italian, and features a context window of 32,000 tokens. It is particularly well-suited for users who need powerful multilingual capabilities. Due to its efficiency and broad applicability, Mixtral-8x7B is favored by advanced users working on diverse tasks across language and content types.

7. Wizard Vicuna Uncensored GPTQ

Wizard Vicuna Uncensored GPTQ is based on the LlaMA architecture and offers users a model with minimal alignment constraints. At 30 billion parameters and 16.94 GB, it delivers unfiltered outputs, allowing for greater control over prompts and responses.

This model is ideal for researchers, developers, or advanced users experimenting with AI alignment, bias, or prompt engineering. Due to its uncensored nature, it must be used responsibly but provides unmatched flexibility for advanced offline exploration.

8. Orca Mini GPTQ

Orca Mini GPTQ is a compact model derived from Microsoft’s Orca research. It follows a teacher-student learning approach where the model learns through detailed explanations instead of simple Q&A patterns.

With just 3 billion parameters and a size of 8.11 GB, Orca Mini is lightweight enough to run on modest hardware. While not suited for professional applications, it serves as an excellent educational tool for understanding model behavior and reasoning patterns.

9. Llama 2 13B Chat GPTQ

Llama 2 13B Chat GPTQ is one of the most widely recognized and reliable models available for offline conversational AI. Created by Meta, this model is the successor to the original Llama series and is specifically optimized for dialogue. It strikes a balance between size and performance, making it suitable for a variety of use cases, including customer service, personal productivity tools, and research.

Its licensing permits commercial and academic usage under certain conditions, making it a favorite for startups and independent developers. With excellent fluency, coherence, and contextual understanding, Llama 2 13B Chat is often the first choice for building dependable offline chatbot applications.

Conclusion

The availability of local LLMs marks a major turning point in how artificial intelligence is accessed and used. These nine models represent the best options currently available for offline deployment, offering users the ability to operate powerful AI systems without needing internet connectivity or cloud-based tools.

Whether you're a developer, writer, educator, or enthusiast, running LLMs locally means greater control, improved privacy, and significant cost savings. As tools and hardware continue to improve, the future of AI won’t just be online—it will be sitting right on your device.