Best Local LLM Solutions 2026
Local LLM solutions enable organisations to run large language models on their own hardware, keeping data private and reducing API costs. These tools range from simple desktop apps to enterprise-grade inference servers.
Methodology
How we evaluated
- Model support
- Performance optimisation
- Ease of setup
- Hardware requirements
- API compatibility
Rankings
Our top picks
Ollama
Simple tool for running open-source LLMs locally on macOS, Linux, and Windows. Provides a command-line interface and local API server with one-command model downloads.
Best for: Developers and individuals wanting the simplest way to run LLMs locally
Features
- One-command model download
- Local API server
- Model customisation
- GPU acceleration
- OpenAI-compatible API
Pros
- Incredibly easy to set up
- Large model library
- OpenAI-compatible API
Cons
- Limited enterprise features
- Basic model management
vLLM
High-throughput LLM inference engine designed for production serving. Uses PagedAttention for efficient memory management and supports continuous batching for maximum GPU utilisation.
Best for: Teams deploying LLMs at scale needing maximum throughput and efficiency
Features
- PagedAttention
- Continuous batching
- Tensor parallelism
- OpenAI-compatible API
- Multi-GPU support
Pros
- Best-in-class throughput
- Excellent memory efficiency
- Production-ready
Cons
- Requires GPU infrastructure
- More complex setup than Ollama
LM Studio
Desktop application for discovering, downloading, and running local LLMs with a chat interface. Provides a user-friendly GUI for non-technical users to explore AI models.
Best for: Non-technical users wanting to explore local LLMs with a desktop app
Features
- GUI model browser
- Chat interface
- Local API server
- Model format support
- Hardware auto-detection
Pros
- Very user-friendly interface
- Good model discovery
- No technical setup required
Cons
- Limited automation capabilities
- Not designed for production deployment
llama.cpp
High-performance C/C++ implementation for LLM inference optimised for consumer hardware. Supports quantisation for running large models on limited hardware with CPU and GPU support.
Best for: Developers needing maximum performance on consumer hardware
Features
- CPU and GPU inference
- Model quantisation
- Low memory footprint
- Cross-platform
- GGUF model format
Pros
- Runs on consumer hardware
- Excellent quantisation support
- Very active development
Cons
- Command-line focused
- Requires compilation for some features
Text Generation Inference (TGI)
Hugging Face's production-grade inference server for deploying LLMs. Optimised for high throughput with features like continuous batching, tensor parallelism, and token streaming.
Best for: Teams deploying Hugging Face models in production environments
Features
- Continuous batching
- Token streaming
- Tensor parallelism
- Quantisation support
- Hugging Face Hub integration
Pros
- Strong Hugging Face ecosystem
- Good production features
- Docker-based deployment
Cons
- Primarily designed for NVIDIA GPUs
- Configuration can be complex
Compare
Quick comparison
| Tool | Best For | Pricing |
|---|---|---|
| Ollama | Developers and individuals wanting the simplest way to run LLMs locally | Free and open source (MIT) |
| vLLM | Teams deploying LLMs at scale needing maximum throughput and efficiency | Free and open source (Apache 2.0) |
| LM Studio | Non-technical users wanting to explore local LLMs with a desktop app | Free for personal use |
| llama.cpp | Developers needing maximum performance on consumer hardware | Free and open source (MIT) |
| Text Generation Inference (TGI) | Teams deploying Hugging Face models in production environments | Free and open source (HFOIL) |
FAQ
Frequently asked questions
Small models (7B parameters) run on modern laptops with 8GB+ RAM. Medium models (13-30B) need 16-32GB RAM or a GPU with 12GB+ VRAM. Large models (70B+) require multiple GPUs or high-RAM systems.
Local models are typically less capable than frontier cloud models but offer complete data privacy, zero per-token costs, and offline availability. Open models like Llama 3 and Mistral are closing the quality gap.
Start with Ollama for simplicity or LM Studio for a visual interface. Move to vLLM or TGI for production deployments needing high throughput. Use llama.cpp for maximum hardware efficiency.
Yes, but fine-tuning is separate from inference. Use tools like Axolotl, Unsloth, or Hugging Face TRL for fine-tuning, then deploy the fine-tuned model with your preferred inference tool.
For high-volume usage, local deployment can be significantly cheaper than API calls. Break-even typically occurs at 1-10M tokens/day depending on hardware costs vs API pricing.
Related Content
Need help choosing the right tool?
Our team can help you evaluate and implement the best AI solution for your needs. Book a free strategy call.