8 Best Ollama Alternatives for Running LLMs Locally (2026)

Ollama is the default way to run an open model on your own machine: install it, run ollama run gemma4, and you have a chat in your terminal plus a local API. It’s MIT-licensed and has 185.8k GitHub stars, and for most people it’s the right first install.

It isn’t always the right last install, though. People usually look for an alternative for one of five reasons:

1. They want a desktop app, not a terminal.
2. They want more control over quantization, context size and GPU offload than Ollama exposes.
3. They need more than text, such as images, speech or video behind one API.
4. They’re serving many users and need higher throughput than a desktop runtime gives.
5. They want to chat with documents without wiring up RAG themselves.

Here’s the alternative that fits each reason. Stars and licenses were checked on 3 November 2026.

Quick comparison

Tool	Best for	Interface	License	GitHub stars
LM Studio	A GUI that just works	Desktop app + local server	Proprietary (free for home and work)	n/a
llama.cpp	Maximum control and efficiency	CLI + server with web UI	MIT	133.3k
Jan	Open-source desktop chat	Desktop app	Apache 2.0	45.4k
LocalAI	Text, images, voice and video behind one API	Server (Docker or binary)	MIT	50.2k
vLLM	Production throughput on GPUs	Server	Apache 2.0	96.2k
SGLang	Fast serving for agent and structured workloads	Server	Apache 2.0	38.4k
AnythingLLM	Chatting with your documents	Desktop app or Docker	MIT	67.9k
textgen	Tinkering with many model formats	Web UI	AGPL-3.0	47.7k

1. LM Studio

Pick it if you’d rather click than type. LM Studio lets you search Hugging Face from inside the app, download a model sized for your hardware, chat with it, and switch on an OpenAI-compatible local server for your own apps. It runs GGUF models through llama.cpp, plus MLX models on Apple Silicon, and works on macOS, Windows and Linux.

Compared with Ollama: a much friendlier way to browse and try models, and MLX support on Macs. It’s free for home and work use, but the app itself isn’t open source.

2. llama.cpp

Pick it if you want every knob. llama.cpp is the C/C++ engine that LM Studio and many other local apps build on. You control quantization (1.5-bit to 8-bit), context length, GPU layer offload and the backend (CUDA, Metal, Vulkan, SYCL and others).

curl -LsSf https://llama.app/install.sh | sh
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

llama serve gives you an OpenAI-compatible API and a built-in web UI.

Compared with Ollama: more work to set up, and in return the most direct control over performance and memory use. A good choice for older GPUs and CPU-only machines.

3. Jan

Pick it if you want a desktop app that is open source. Jan is an offline, ChatGPT-style desktop app from Menlo Research. You download models, chat, and can connect remote APIs when you need a bigger model. It’s available from its website, the Microsoft Store and Flathub.

Compared with Ollama: a full chat UI out of the box. Compared with LM Studio: Apache 2.0 licensed, so you can read and change the code.

4. LocalAI

Pick it if you need more than text. LocalAI puts LLMs, vision, voice, image and video models behind one API. That API is compatible with OpenAI’s, Anthropic’s and ElevenLabs’. Each backend (llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX and others) is pulled only when a model needs it. It runs on NVIDIA, AMD, Intel, Apple Silicon or CPU only, and includes API keys, quotas and roles for multi-user setups.

Compared with Ollama: broader modality coverage and multi-user features, but a bigger system to run and learn.

5. vLLM

Pick it if many people will use the model at once. vLLM uses PagedAttention and continuous batching to serve many concurrent requests efficiently on NVIDIA and AMD GPUs, and exposes an OpenAI-compatible API.

pip install vllm
vllm serve Qwen/Qwen3.8-27B

Compared with Ollama: much higher throughput under load, but it’s built for servers, not laptops.

6. SGLang

Pick it if you’re benchmarking production engines. SGLang is the main open-source alternative to vLLM, with an emphasis on fast serving for agent-style, multi-call and structured-output workloads. Try both on your own traffic before choosing.

Compared with Ollama: like vLLM, it’s for GPU servers, not desktops.

7. AnythingLLM

Pick it if your goal is “chat with my files”. AnythingLLM bundles document ingestion, a vector store, agents and a chat UI in a desktop app or a Docker deployment. It can use Ollama, LM Studio or a cloud API as the model backend.

Compared with Ollama: it’s a layer on top, not a replacement. Many people run AnythingLLM with Ollama underneath. For a team-wide chat UI, Open WebUI is the other common choice. Check its branding clause if you’ll have more than 50 users.

8. textgen (text-generation-webui)

Pick it if you like to experiment. textgen is oobabooga’s long-running web UI, renamed from text-generation-webui. It supports many model formats and loaders, has chat and notebook modes, and lets you change sampling settings directly.

Compared with Ollama: far more settings, less polish, and an AGPL-3.0 license.

Which one should you choose?

• Just starting out? Stay with Ollama, or use LM Studio if you’d prefer a GUI.
• Need an open-source GUI? Jan.
• Want maximum performance on your hardware? llama.cpp.
• Need images, speech or video too? LocalAI.
• Serving a team or a product? vLLM or SGLang on a GPU server.
• Want to chat with documents? AnythingLLM, or Open WebUI for a team, with Ollama underneath.

For a head-to-head of the three most popular local runtimes, read Ollama vs LM Studio vs llama.cpp. For the wider open-source stack, see our top 10 open source AI tools.

FAQ

Is there a faster alternative to Ollama?

On a single machine, llama.cpp often matches or beats Ollama once you tune its flags for your hardware, because it exposes every setting directly. For many concurrent users, vLLM and SGLang are much faster because they batch requests on GPUs.

What’s the best Ollama alternative with a GUI?

LM Studio is the most polished. Jan is the best fully open-source option.

Can I use these with coding agents like Claude Code?

Yes. LM Studio, llama.cpp, LocalAI, vLLM and SGLang all expose OpenAI-compatible APIs, so any tool that accepts a custom base URL can use them. See our Claude Code review for agent options.

Do any of them run models Ollama can’t?

LM Studio runs MLX models on Apple Silicon, LocalAI adds image, voice and video models, and vLLM and SGLang run full-precision Hugging Face checkpoints that haven’t been converted to GGUF.