Best by task · Local LLMs and model platforms
Best tools for running LLMs locally
Tools for running open models on your own machine or serving them, compared on license, platforms, GitHub activity and price.
- 13tools ranked
6 Tested · 7 Reported
- people running modelswho it is for
- Oct 26, 2026last updated
Last updated . Rankings are editorial; paid placements never affect them.
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Cohere
Enterprise-focused models and the North agent platform.
Verdict: Enterprise AI company: Command models, Rerank and the North platform.
DeepSeek
Model API compatible with the OpenAI and Anthropic formats.
Verdict: Model API that works with the OpenAI and Anthropic SDK formats and can back agent tools such as Claude Code, GitHub Copilot and OpenCode.
Hugging Face
Hub for models, datasets and Spaces with paid compute.
Verdict: The hub where the ML community shares models, datasets and apps, with PRO, Team and Enterprise tiers.
LiteLLM
Python SDK and proxy exposing many providers through an OpenAI-style API.
Verdict: Useful as a single interface in front of local and hosted models; check the license files and pin the version because it releases daily.
llama.cpp
C/C++ inference engine for running LLMs on local hardware.
Verdict: LLM inference in C/C++: the open-source engine many local-model tools build on; MIT-licensed with near-daily build tags.
LM Studio
Desktop app for running local models, now with an agent called Bionic.
Verdict: Desktop app for running open models locally; the homepage now leads with Bionic, an agent for work and code.
Ollama
Run open models locally or in the cloud with a CLI and API.
Verdict: The common way to run open models locally on macOS, Windows and Linux, with a cloud option and an MIT-licensed open-source core.
Open WebUI
Self-hosted web interface for local and hosted models.
Verdict: Self-hosted AI platform: a web interface that connects to Ollama and OpenAI-compatible models, under its own Open WebUI License.
OpenRouter
One API for many hosted models with provider fallback.
Verdict: Unified API to many models with fallbacks; a Free tier with 25+ free models and 4 free providers.
Replicate
Run open-source models through a cloud API.
Verdict: Cloud API for running and fine-tuning open models, with per-use pricing.
Together AI
Inference, fine-tuning and GPU clusters for open models.
Verdict: AI cloud for open models: serverless inference, fine-tuning and GPU clusters, priced per token.
Jan
Open-source ChatGPT-style desktop app for local and cloud models.
Verdict: Open-source alternative to ChatGPT that runs open models locally or connects to cloud models like GPT and Claude.
GPT4All
Private local chatbot by Nomic.
Verdict: Local chatbot from Nomic that runs open-source models on your device; the latest GitHub release we saw is v3.10.0 from Feb 2025.
Side by side: what we recorded for the top 8
Every cell is read from the tool’s own pages on the date shown on its entry. “Not recorded” means we have not verified it, not that it does not exist.
| Tool | Pricing | Licence | Platforms | Evidence | Verified |
|---|---|---|---|---|---|
| Cohere | Official price page read, checked Oct 25, 2026. Pay as you go | Proprietary models (some research weights published separately) | API and private deployment | Reported | |
| DeepSeek | Not verified, checked Oct 25, 2026. Pay as you go (API) | Not read (check each model's license) | API | Reported | |
| Hugging Face | Official price page read, checked Oct 25, 2026. Freemium | Varies by repository | Web, API and libraries | Tested | |
| LiteLLM | Partly verified, checked Oct 25, 2026. Open source | NOASSERTION (mixed; see the repository) | Python | Tested | |
| llama.cpp | Official price page read, checked Oct 25, 2026. Free and open source | MIT (per GitHub) | Cross-platform source and binaries | Tested | |
| LM Studio | Not verified, checked Oct 25, 2026. Not verified | Proprietary application (license terms not read) | Desktop (platform list not read) | Reported | |
| Ollama | Official price page read, checked Oct 25, 2026. Free + paid cloud plans | MIT (per GitHub) | macOS, Windows and Linux | Tested | |
| Open WebUI | Partly verified, checked Oct 25, 2026. Free self-hosted | Open WebUI License (custom; read it before commercial use) | Self-hosted (Docker and others) | Tested |
Who each pick suits
- Cohere: Good for: Enterprises needing private deployment and RAG components. Skip it if: Hobbyists wanting a free chat app.
- DeepSeek: Good for: Developers who want an OpenAI-compatible endpoint; People wiring a cheaper backend into agent tools. Skip it if: You need published prices in this page (not captured); You need a first-party consumer app review (not covered here).
- Hugging Face: Good for: Anyone sourcing open models and datasets. Skip it if: You want a single polished chat app.
- LiteLLM: Good for: Teams that call several model providers; Anyone pointing tools at a local OpenAI-compatible server. Skip it if: You only ever use one provider and its own SDK.
- llama.cpp: Good for: Developers embedding local inference; People who want maximum control over local models. Skip it if: You want a polished app with a GUI.
- LM Studio: Good for: People who want a desktop app for local models. Skip it if: You need an open-source app (see Jan or Ollama).
- Ollama: Good for: Developers who want local models behind a simple API; People who want to try open models privately. Skip it if: You need frontier proprietary models only; You have no GPU and want fast large-model output.
- Open WebUI: Good for: Teams that want a self-hosted chat UI; Ollama users who want a browser interface. Skip it if: You need a standard OSI license for your own distribution.
Compare the options
- Jan vs GPT4All: which should you use?
- llama.cpp vs GPT4All: which should you use?
- Ollama vs llama.cpp: which should you use?
- Ollama vs Open WebUI: which should you use?
- Together AI vs Replicate: which should you use?
Looking for something else? See all best-for categories, or the quick-answer tables.
Setup guides
FAQ
What is the best AI tool for local LLM tools and model platforms?
Our current top pick is Cohere: Enterprise-focused models and the North agent platform. Rankings are editorial and paid placements never affect them.
How are the picks on this page chosen?
Ranks are editorial judgement made by Agentic Weekly when an entry is written or updated. They are not benchmark results, user ratings, popularity counts or paid positions. We rank only the 139 tools we track, not the whole market. "#1" means top of the tools we cover for that task, not best overall. The reason for each rank is the one-line blurb shown beside it. Typical reasons are how well the tool's own docs say it fits the task and how actively it is maintained according to its dated release notes. There is no numeric scoring formula, and we have not run quality head-to-head comparisons between ranked tools; our research studies measure install size, startup time and speed for some tools, and those numbers do not set ranks. Where a rank looks like a verdict on quality, it is our reading of official sources, not a measured result. The Tested label does not raise a rank. Tested means a small smoke test (install plus a short task, usually on a small local model), not a benchmark. Reported and Tested entries are ranked from the same kind of evidence. Payment cannot change a rank. Featured listings are labelled and are never used to order a list. Prices and plan details are shown only with a date and an official source. Where we could only find a third-party figure, or could not read the official page, the entry says so. Ranks can change when a release, price or doc changes. Each page shows when it was last verified.
How often is this list updated?
Every week, when new releases are added to the feed. Each entry shows its release date and when it was last verified.
How to cite this page
Agentic Weekly, "Best tools for running LLMs locally (updated weekly)", last updated 2026-10-26, https://theagenticweekly.com/best/llm-platforms
Keep the Reported or Tested label when you quote a verdict. Plain-text version: https://theagenticweekly.com/best/llm-platforms.md. More for AI assistants: /ai, /tools.json, /llms.txt.
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