How to install a local LLM in 2026
🔎 Why everyone is going local in 2026
Ollama surpasses 52 million monthly downloads in 2026 according to Tamiltech. This isn't a fad, it's a paradigm shift. Cloud API costs are piling up, data leaks make headlines, and open source models have caught up.
The arrival of DeepSeek V4 Pro (open source score of 88) and Kimi K2.6 (score of 85) makes local viable for 90% of professional use cases. No need for a GPU cluster anymore. A Mac M2 or a PC with 16 GB of RAM is enough for high-performance models.
GDPR regulations are also pushing companies to take back control of their data. A model running on your machine never sees your prompts leave your network. This is a decisive argument for the healthcare, finance, and legal sectors.
The essentials
- Ollama remains the most popular tool in 2026 for installing a local LLM, with command-line ease of use and Docker compatibility.
- LM Studio offers the best graphical interface, ideal for those who want to test several models without touching the terminal.
- GPT4All stands out for its lightness: it even runs on older machines thanks to its CPU optimization.
- The best current open source models (DeepSeek V4 Pro, Kimi K2.6, Qwen3.6-27B) offer performance close to proprietary models at zero cost per request.
- 16 GB of RAM is the comfortable minimum, 32 GB allows you to run 70B+ models in 4-bit quantization.
Recommended tools
| Tool | Main use | Price (June 2026) | Ideal for |
|---|---|---|---|
| Ollama | Run and manage LLMs in CLI | Free | Developers, automation, servers |
| LM Studio | Graphical interface for testing models | Free (paid Pro version) | Discovery, quick model comparison |
| GPT4All | Lightweight CPU-optimized LLM | Free | Older PCs, 8 GB RAM, basic use |
| text-generation-webui | Advanced web interface | Free | Power users, fine-tuning, fine parameters |
| llamafile | Portable LLM in a single file | Free | Easy sharing, no installation |
To go further in choosing models, check out our comparison of the best local LLMs updated every month.
Hardware prerequisites — What you really need
A local LLM doesn't require a supercomputer. But you need to be honest about your machine's limits.
Minimum configuration (8 GB RAM)
You will be able to run 4-bit quantized models up to 7-8 billion parameters. This is sufficient for summarization, classification, or simple information extraction. Qwen3.5-27B in its A3B version (3 billion active parameters) is a good candidate here.
Recommended configuration (16-32 GB RAM)
This is the sweet spot in 2026. With 16 GB, you can comfortably run 14B-32B quantized models. With 32 GB, you get access to 70B models in 4-bit like Qwen3.5-122B-A10B, which rival mid-range proprietary models.
The GPU: is it mandatory?
No. GPT4All proves that a modern CPU is enough for small models. But a GPU considerably accelerates generation. On Mac, the integrated GPU (Apple Silicon) shares the unified memory, which simplifies everything. On PC, an NVIDIA RTX 3060 (12 GB VRAM) or 4060 (16 GB VRAM) offers an excellent price-to-performance ratio.
The Claude 5 Hub guide on Ollama, LM Studio and llama.cpp in 2026 details the recommended hardware configurations based on the target model size.
Installing Ollama — The standard method in 2026
Ollama dominates the local AI market with its 52 million monthly downloads. It's the tool I recommend by default.
Installation on macOS and Linux
On macOS, a simple brew install ollama is enough if you have Homebrew. Otherwise, download the DMG from the official website. On Linux, the one-line installation script is available in the official documentation.
The advantage of Ollama is its model management via a simple tag system. You pull a model just like you pull a Docker image.
Installation on Windows
Ollama is now natively available on Windows since early 2026. The installer configures everything automatically: the binary, the background service, and the environment variables. No more need for WSL2 for basic use, although Docker remains useful for advanced deployments.
Downloading and running a first model
To download DeepSeek V4 Pro (the best current open source with a score of 88), the command is straightforward:
ollama run deepseek-v4-pro:70b-q4
Ollama automatically downloads the quantized model, caches it, and opens an interactive session. The first run takes a few minutes depending on your bandwidth. Subsequent ones are instant.
The Tech Insider tutorial in 11 steps to run an LLM with Ollama covers advanced cases: Python integration, Docker, and memory optimization.
Ollama as an API server
Ollama automatically exposes a REST API on port 11434. This means that any OpenAI-compatible application can point to your local instance. You simply replace the base URL. This is perfect for connecting tools like OpenClaw to your local LLM.
By the way, if you want to set up an autonomous AI agent locally, our guide to install OpenClaw on a VPS in 30 minutes shows how to connect Ollama as a backend. And to fully exploit agent capabilities, our article on the best LLMs for AI agents details the optimal configurations.
Installing LM Studio — The graphical interface that changes everything
LM Studio is the answer for those who want to test local LLMs without opening a terminal. It's a complete desktop app with a polished interface.
Why choose LM Studio over Ollama
Two main reasons. First, model discovery: LM Studio integrates an explorer that lists Hugging Face models with their benchmark scores, size, and hardware compatibility. Second, fine-tuning parameters: temperature, top-p, repeat penalty, everything is accessible via visual sliders.
The DEV Community comparison between Ollama, LM Studio and Jan in 2026 positions LM Studio as the best compromise between power and accessibility.
Installation and first launch
Download the installer corresponding to your OS from the official website. The application is about 200 MB. On first launch, it detects your hardware (available RAM, GPU if present) and filters compatible models.
The integrated search allows you to filter by size, by task (chat, code, instruct), and by format (GGUF). This is a huge time saver compared to manual Hugging Face browsing.
Chat and completion in LM Studio
The Chat tab offers a ChatGPT-like interface with conversation history. You can compare two models side by side, which is very handy for evaluating whether a lighter model is sufficient for your use case. The Completion tab is developer-oriented, with a system prompt editor and live API testing.
Installing GPT4All — When lightness takes priority
GPT4All adopts a different philosophy: run on anything, even without a GPU. SitePoint points this out in its 2026 developer guide as the simplest tool to get started.
The GPT4All use case
You have a PC with 8 GB of RAM, no dedicated graphics card, and you want an assistant that answers in under 2 seconds. GPT4All is made for this. It uses CPU-optimized inference via llama.cpp under the hood, but with complete abstraction.
Installation in a few clicks
The Windows installer is less than 100 MB. On launch, GPT4All offers to download a recommended model by default. You can also browse their internal catalog, which only lists models validated and tested by the team. No bad surprises.
Real-world performance
On an 8th-gen i5 with 8 GB RAM, GPT4All generates about 8-12 tokens per second with a quantized 7B model. This is sufficient for assisted reading, brainstorming, or everyday writing. For more demanding tasks, switch to Ollama or LM Studio.
The best open source models to install in 2026
The tool does nothing without a good model. Here are the best candidates according to your configuration, all from our reference list.
For 8-16 GB RAM (light to medium models)
| Model | Score | Active parameters | Recommended quantization |
|---|---|---|---|
| Qwen3.5-27B-A3B | 67 | 3B active out of 27B | Q4_K_M |
| Qwen3.6-27B | 74 | 27B | Q4_K_M |
| MiniMax M2.7 | 62 | 2.7B | Q5_K_M |
| DeepSeek V4 Flash (High) | 71 | ~7B | Q4_K_M |
Qwen3.5-27B-A3B is an efficiency monster. Only 3 billion parameters are active at each inference, but it benefits from the knowledge base of a 27B model. It is the number one choice for modest machines.
For 16-32 GB RAM (high-performance models)
| Model | Score | Parameters | Recommended quantization |
|---|---|---|---|
| DeepSeek V4 Pro (High) | 84 | ~32B | Q4_K_M |
| Kimi K2.6 | 85 | ~32B | Q4_K_M |
| GLM-5.1 | 83 | ~30B | Q4_K_M |
| DeepSeek V4 Pro (Max) | 88 | ~70B | Q3_K_M (if 32 GB) |
Kimi K2.6 is particularly interesting because it reaches a score of 88.1 in agentic (self-host), making it an excellent candidate for automated local workflows.
For 32+ GB RAM (high performance)
DeepSeek V4 Pro (Max) at 88 points is the absolute king of open source in 2026. In Q4_K_M quantization, it requires about 40 GB of RAM. In Q3, it drops below 32 GB with minimal quality loss. To see how it positions itself against proprietary models, check out our monthly comparison of the best LLMs.
Advanced tools — Beyond the basics
Once Ollama or LM Studio is in place, other tools open up additional possibilities.
text-generation-webui (Oobabooga)
This is the most comprehensive web interface for local AI. It supports dozens of backends (llama.cpp, Transformers, ExLlamaV2), offers integrated LoRA fine-tuning, and allows you to create AI characters with advanced prompt systems. Pinggy ranks it among the top 5 local LLM tools in 2026.
The downside: the learning curve is steep. It's a tool for power users, not for beginners.
llamafile (Mozilla)
llamafile turns an LLM into a single executable file. No installation, no dependencies. You download a .exe file or a Linux binary, you run it, and your model is accessible via a web interface on localhost. It's ideal for sharing an LLM with a colleague who has no technical skills.
AnythingLLM
AnythingLLM adds an RAG (Retrieval-Augmented Generation) layer on top of your local LLM. You give it PDF documents, URLs, text files, and it builds a vector index. You can then "chat" with your documents. The Medium comparison on Ollama vs LM Studio vs AnythingLLM highlights that AnythingLLM excels when the need goes beyond simple chat.
Local AI vision — Analyzing images without the cloud
An often overlooked aspect of local AI: vision. Several open source models support image analysis directly locally, without sending your photos to a remote server.
Some models from the Qwen and GLM families include multimodal capabilities. With Ollama, loading a vision model is done in the same way as a classic text model. You then pass the image in base64 or as a file path in your prompt.
The use cases are concrete: OCR on sensitive documents, analysis of screenshots for technical support, classification of medical images. Everything stays on your machine. For a complete deep dive into the subject, our guide on AI vision with LLMs details the models and configurations.
Local LLMs and AI agents — The powerful combo
A local LLM becomes really interesting when you plug it into an agent framework. An AI agent can browse the web, execute code, interact with APIs, all while keeping the reasoning local.
Ollama is perfectly suited to this use case thanks to its OpenAI-compatible REST API. Agent frameworks like OpenClaw can use it as a backend without modification. The OpenClaw tools then make it possible to chain complex actions: web search, document analysis, report generation.
Kimi K2.6 in self-host reaches 88.1 in agentic score, making it particularly suited to these workflows. This is a score higher than that of GPT-5 (78.1 in high mode) in self-hosted configuration. The local agent + open source model combination is now a credible alternative to proprietary cloud solutions.
❌ Common mistakes
Mistake 1: Installing a model too big for your RAM
This is the number one mistake. A 70B model in Q4_K_M requires about 40 GB of RAM. If you have 16 GB, your system will swap and generation will be slow at best, impossible at worst. Solution: check the size of the GGUF file before downloading it. Ollama displays the size during the pull. LM Studio automatically filters by available RAM.
Mistake 2: Ignoring quantization
Downloading a model in FP16 (full precision) when a Q4_K_M equivalent exists is a waste. 4-bit quantization reduces the model size by 4 with a quality loss of less than 2-3% on benchmarks. It is always the right choice for local use, unless you are doing fine-tuning.
Mistake 3: Using Ollama without locking the version
Ollama frequently updates its models. An ollama pull deepseek-v4-pro without specifying a tag might fetch a different version from one day to the next. For production, always specify the full tag: deepseek-v4-pro:70b-q4_K_M-2026-05-15.
Mistake 4: Neglecting the system prompt
A local LLM does not have the filtering or alignment of a cloud model. The system prompt is your only safeguard. Without clear instructions, an open source model can produce incoherent or off-topic responses. Take 2 minutes to write a system prompt adapted to your use case.
Mistake 5: Comparing a local 7B model to GPT-5.5
It's like comparing a Renault Clio to a Porsche 911 and concluding that the Renault is bad. A local 7B model does what it was designed for: simple tasks, quickly, for free, and privately. For an honest comparison, test DeepSeek V4 Pro (Max) against a proprietary model with an equivalent score.
❓ Frequently asked questions
Can a local LLM replace ChatGPT?
For 80% of daily uses (summarization, writing, brainstorming, simple code), yes. DeepSeek V4 Pro (Max) at 88 points approaches the scores of GPT-5.4 (89). For extreme reasoning tasks or advanced multimodal capabilities, proprietary models keep the advantage. Our page of the best free LLMs compares free cloud options to local ones.
How much storage should you plan for?
Count 4-8 GB per model in Q4. If you test 5-6 different models, plan for 40-50 GB of free space. Ollama stores everything in ~/.ollama/models on macOS/Linux and in %USERPROFILE%\.ollama\models on Windows.
Is local AI really free?
Yes, the cost per request is strictly zero according to the Tamiltech study. You pay for the electricity (negligible for normal use) and the potential hardware. It's a fixed cost, not a variable one. At 1000 requests per day in the cloud, the monthly bill runs into hundreds of euros. Locally, it's zero.
Can you use a local LLM in French?
Yes, but with nuances. Qwen and GLM models handle French well. For truly optimized French, some models are specifically trained on Francophone corpora. Our article on the best LLMs in French details the options.
Ollama vs LM Studio: which to choose in 2026?
Ollama if you are a developer or if you want to automate (API, Docker, CI/CD). LM Studio if you prefer a visual interface to compare models quickly. Both can coexist on the same machine without conflict.
How to update a model on Ollama?
ollama pull model-name:tag downloads the latest available version. The old one remains in cache. To delete old versions and free up space: ollama rm model-name:old-tag.
✅ Conclusion
Installing a local LLM in 2026 has become trivial: Ollama installs in one command, LM Studio in three clicks, and open source models like DeepSeek V4 Pro offer performances that would have seemed impossible a year ago. For a detailed step-by-step guide with all the commands, check out our local LLM installation guide with Ollama and LM Studio.
