Top Tools for Running LLMs Locally (2026)

Top Tools for Running LLMs Locally in 2026

Running large language models locally gives you full privacy, zero API costs, no rate limits, and complete control over your AI stack. With the explosion of high-quality open-weight models like Llama 3.3, Qwen 3, Mistral Large, and DeepSeek-R1, the bottleneck is no longer the models -- it is choosing the right tool to run them.

This guide compares the best local LLM tools available in 2026, covering everything from one-click desktop apps to production-grade inference servers.

Quick Comparison

Tool	Best For	GPU Required	API Server	UI	Platform
Ollama	Simplicity, CLI workflows	No (CPU ok)	Yes (OpenAI-compatible)	No (third-party)	macOS, Linux, Windows
LM Studio	Desktop users, beginners	No (CPU ok)	Yes (OpenAI-compatible)	Yes	macOS, Linux, Windows
llama.cpp	Maximum performance, customization	No (CPU ok)	Yes	No	All platforms
vLLM	Production serving, high throughput	Yes	Yes (OpenAI-compatible)	No	Linux
GPT4All	Non-technical users	No (CPU ok)	Yes	Yes	macOS, Linux, Windows
Jan	Privacy-focused desktop use	No (CPU ok)	Yes (OpenAI-compatible)	Yes	macOS, Linux, Windows
LocalAI	Drop-in OpenAI replacement	No (CPU ok)	Yes (OpenAI-compatible)	No	All platforms
KoboldCpp	Creative writing, roleplay	No (CPU ok)	Yes	Yes	All platforms

1. Ollama

Ollama is the most popular tool for running LLMs locally, and for good reason. It wraps llama.cpp in a clean CLI interface with a model registry that makes downloading and running models as easy as Docker.

Installation:

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Or on macOS with Homebrew
brew install ollama

Running a model:

# Download and run Llama 3.3 70B
ollama run llama3.3:70b

# Run Qwen 3 with a specific quantization
ollama run qwen3:32b-q4_K_M

# Run DeepSeek-R1 distilled
ollama run deepseek-r1:14b

Start the API server:

# Ollama serves an OpenAI-compatible API on port 11434 by default
ollama serve

# Test it with curl
curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.3:70b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Why choose Ollama:

• Dead simple CLI interface.
• Huge model library with pre-quantized models.
• OpenAI-compatible API works with most AI frameworks.
• Automatic GPU detection and layer offloading.
• Supports model customization through Modelfiles.

Limitations:

• Less control over inference parameters than llama.cpp directly.
• No built-in UI (use Open WebUI or similar).
• Not designed for multi-GPU production serving.

2. LM Studio

LM Studio is a polished desktop application with a built-in chat UI, model browser, and local API server. It is the best option for users who want a visual interface.

Key features:

• One-click model downloads from Hugging Face.
• Built-in chat interface with conversation history.
• Local API server (OpenAI-compatible) for development.
• GGUF, GGML, and MLX format support.
• Apple Silicon optimization (Metal) and NVIDIA CUDA support.
• Quantization selector in the UI.

Getting started:

1. Download from lmstudio.ai.
2. Open the app and browse the Discover tab.
3. Search for a model (e.g., "Qwen 3 32B") and click Download.
4. Switch to the Chat tab and select your downloaded model.
5. Start chatting.

Running the API server:

1. Go to the Developer tab in LM Studio.
2. Select your loaded model.
3. Click "Start Server."
4. The server runs on http://localhost:1234 by default.

import openai

client = openai.OpenAI(
    base_url="http://localhost:1234/v1",
    api_key="lm-studio"  # Any string works
)

response = client.chat.completions.create(
    model="qwen3-32b",
    messages=[{"role": "user", "content": "Explain quicksort in Python."}]
)

print(response.choices[0].message.content)

3. llama.cpp

llama.cpp is the foundational C/C++ project that powers most local LLM tools. If you want maximum performance and full control, use it directly.

Build from source:

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp

# Build with CUDA support
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j

# Build with Metal support (macOS)
cmake -B build -DGGML_METAL=ON
cmake --build build --config Release -j

Run inference:

# Interactive chat
./build/bin/llama-cli \
  -m models/qwen3-32b-q4_k_m.gguf \
  --chat-template chatml \
  -c 8192 \
  -ngl 99 \
  --interactive

# Start an OpenAI-compatible server
./build/bin/llama-server \
  -m models/qwen3-32b-q4_k_m.gguf \
  --host 0.0.0.0 \
  --port 8080 \
  -c 8192 \
  -ngl 99

Why choose llama.cpp:

• Fastest CPU inference available.
• Fine-grained control over every parameter.
• Supports GGUF quantization formats (Q2 through Q8, and K-quants).
• Active development with new optimizations weekly.
• Foundation that Ollama, LM Studio, and others build on.

4. vLLM

vLLM is the go-to choice for production LLM serving. It uses PagedAttention for efficient memory management and delivers significantly higher throughput than other tools.

Installation:

pip install vllm

Start a server:

vllm serve Qwen/Qwen3-32B-AWQ \
  --dtype auto \
  --api-key your-secret-key \
  --max-model-len 8192

Key advantages:

• PagedAttention for near-optimal GPU memory usage.
• Continuous batching for high concurrent throughput.
• Tensor parallelism for multi-GPU setups.
• OpenAI-compatible API out of the box.
• Supports AWQ, GPTQ, and FP8 quantization.

Best suited for: Production APIs, high-concurrency applications, multi-GPU servers.

5. GPT4All

GPT4All is designed for non-technical users who want a simple local AI experience. It offers a clean desktop app with curated models.

Features:

• Simple installer for all platforms.
• Curated model library tested for quality.
• Local document Q&A (RAG) built in.
• Low resource requirements for smaller models.
• No technical setup required.

6. Jan

Jan is an open-source desktop app focused on privacy. It stores everything locally, runs models offline, and provides a ChatGPT-like interface.

Features:

• Clean ChatGPT-style UI.
• Extension system for plugins.
• OpenAI-compatible local API.
• Runs fully offline after model download.
• Active open-source community.

7. LocalAI

LocalAI is a drop-in replacement for the OpenAI API that runs entirely local. It supports text generation, image generation, audio transcription, and embeddings.

# Run with Docker
docker run -p 8080:8080 localai/localai:latest

Hardware Recommendations

Model Size	Minimum RAM/VRAM	Recommended Setup
7B (Q4)	6 GB	Any modern laptop, 8 GB RAM
14B (Q4)	10 GB	16 GB RAM laptop or 12 GB GPU
32B (Q4)	20 GB	24 GB GPU (RTX 4090) or 32 GB RAM (CPU)
70B (Q4)	40 GB	2x 24 GB GPUs or 64 GB RAM Mac
70B (Q8)	75 GB	Mac Studio 96/128 GB or 2-4 GPUs

Which Tool Should You Pick?

• Just want to chat with AI locally? Use LM Studio or GPT4All.
• Developer who wants CLI simplicity? Use Ollama.
• Need maximum performance and control? Use llama.cpp directly.
• Building a production API? Use vLLM.
• Want an OpenAI API drop-in replacement? Use LocalAI.
• Privacy is your top priority? Use Jan.

Conclusion

Running LLMs locally has never been easier or more practical. The tools have matured to the point where a single command can download and run a state-of-the-art model on consumer hardware. Whether you choose Ollama for its simplicity, LM Studio for its UI, or vLLM for production throughput, you have excellent options.

If you need AI capabilities beyond text generation -- such as AI avatars, image-to-video, voice cloning, or lip-sync -- Hypereal AI offers affordable API access to cutting-edge generative media models that complement your local LLM setup for building complete AI-powered applications.