The best open-source tools to run LLMs locally in 2026
The best open-source tools to run LLMs locally right now are llama.cpp, vLLM and Ollama, out of 9 we have reviewed. Each is ranked by our rating, with its price, our verdict and who it suits.
Updated Oct 23, 2026 · Ranked from our reviews · How we review
- 1
If an open model runs on a laptop, it almost certainly runs through llama.cpp, and nothing else covers as much hardware.
Best for: Developers who want direct control over running open models locally, on edge devices or on modest servers.
Price: Free and open source under the MIT licence. No paid edition.
Read our llama.cpp review4.8/5 - 2
Still the engine to put an open model behind a multi-user API, provided someone on the team owns its upgrades and its network exposure.
Best for: Teams serving open models to many concurrent users on GPU servers.
Price: Free and open source under the Apache 2.0 licence. You pay for the GPUs it runs on.
Read our vLLM review4.7/5 - 3
Local models in one command, with defaults (a 4,096-token context, one request at a time, no login) that suit a laptop and nothing bigger.
Best for: Developers and individuals who want open models running locally with minimal setup.
Price: Open source (MIT) and free to run locally. Optional Ollama Cloud: Free with starter credits; Pro $20/month ($60 credits); Max $100/month ($300 credits); Team $500/month ($1,000 shared credits, unlimited users); Enterprise custom.
Read our Ollama review4.6/5 - 4
MLX turns a high-memory Mac into a credible box for local inference and LoRA tuning; off Apple silicon it is a port, not a reason to switch.
Best for: Mac-based developers and researchers who want native speed and Python control on Apple silicon.
Price: Free and open source under the MIT license.
Read our MLX review4.4/5 - 5
SGLang is built for the agent and RL workloads that resend the same long prompts, and it belongs behind a locked-down network.
Best for: Teams serving open models with heavy prompt reuse, such as agents and multi-turn chat.
Price: Free and open source under the Apache 2.0 license.
Read our SGLang review4.4/5 - 6
A disk-streaming C engine that makes 744B-parameter open models run on a desktop, slowly, with an honest local stand-in for the Jev decision API on the side.
Best for: Enthusiasts and developers who want to run frontier-size open MoE models, or prototype Jev-style decisions, on their own machine.
Price: Free and open source (Apache 2.0). Each model keeps its own licence.
Read our colibri review4.2/5 - 7
TensorRT-LLM is where new NVIDIA silicon gets its first serious optimisations, and the price is a long CVE list and a stable release cadence measured in seasons.
Best for: Teams running large NVIDIA GPU fleets who want maximum throughput per GPU.
Price: Free and open source under the Apache 2.0 license (some bundled components carry their own licenses).
Read our TensorRT-LLM review4.0/5 - 8
The simplest way to hand someone a working local model, but less capable than the llama.cpp it wraps.
Best for: People who need to share or run a local model with zero setup, including offline.
Price: Free and open source under the Apache 2.0 licence, with MIT-licensed changes to llama.cpp.
Read our llamafile review3.9/5 - 9
Thoughtful plumbing around small decision models that only become accurate once you fine-tune them on your own labels.
Best for: Developers who will fine-tune a small decision model on their own data and want a ready router, server and training loop.
Price: Free and open source (Apache 2.0), including the model weights.
Read our Laya review3.6/5