The best apps for running multiple local AI models on desktop are the ones that stop treating your GPU like a single-slot cartridge console. A recent XDA piece by a writer who stopped chasing bigger local models makes the case bluntly: two 2GB models running side by side outperformed one 8GB model, because each small model was picked for a specific job. A 3B code model handled autocomplete. A 3B chat model handled conversation. Neither had to be everything.
That is the pattern we care about here. Not benchmark chasing. Routing. We tested seven desktop runners and orchestrators over the past few weeks, timed how fast they swap models, checked whether they can hold two loaded at once, and rated how usable the routing is in practice. This list is for anyone with a 12GB to 24GB card who wants a real assistant plus a code model plus maybe a vision model, all on one machine.
What to look for in a local multi-model runner
Six things separate the tools that make multi-model workflows pleasant from the ones that force you into a single-model rut.
- Hot-swap speed. When you switch models mid-session, does the app unload the old one and load the new one in seconds, or does it stall on a cold start every time.
- Concurrent slots. Some tools can keep two or three models resident so responses feel instant. Others load one, evict it, load the next.
- Routing. Can you point chat, autocomplete, embeddings, and vision at different backends without editing config on every request.
- OpenAI-compatible API. The apps that expose an OpenAI-style endpoint plug straight into IDE extensions, note tools, and orchestrators.
- Parallel chat. Being able to send the same prompt to two models and compare outputs in one window is genuinely useful for picking specialists.
- Cross-platform desktop. Windows, macOS, and Linux support with the same feature set matters if your setup mixes machines.
Quick comparison
| App | Best for | Platforms | Free plan | Paid tier | Multi-model story |
|---|---|---|---|---|---|
| Ollama | Foundational runner and API layer | Windows, macOS, Linux | Fully free, open source | None | Hot-swap on demand, keeps recent models warm |
| LM Studio | GUI multi-model tabs | Windows, macOS, Linux | Free for personal use | Team licence for work | Load several models at once, switch tabs |
| Msty | Parallel chat across models | Windows, macOS, Linux | Free tier | Aurum lifetime licence | Side-by-side responses from up to eight models |
| Jan | Open-source multi-model chat | Windows, macOS, Linux | Fully free, open source | None | Swap per conversation, OpenAI-compatible server |
| Open WebUI | Pipeline routing over Ollama | Web UI, self-hosted | Fully free, open source | None | Per-request model pick, pipeline scripting |
| LocalAI | Drop-in API router | Windows, macOS, Linux, Docker | Fully free, open source | None | One endpoint routes to many backends and formats |
| LiteLLM | Cross-backend proxy | Windows, macOS, Linux | Fully free, open source | Managed cloud tier | Route by model name to any local or remote provider |
The apps
1. Ollama, best overall for hot-swapping local models
Ollama is the app most of the rest of this list depends on, and for good reason. Its CLI and API around llama.cpp made model management feel like docker pull, and its behaviour with multiple models is the reason two-small-model workflows work at all. Run ollama run llama3.2:3b for chat, and ollama run qwen2.5-coder:3b for autocomplete, and the daemon keeps the recently used one warm while loading the other. The default keep-alive is five minutes, so a quick switch back does not re-pay the load cost. Set OLLAMA_KEEP_ALIVE higher and the model stays resident until VRAM pressure forces an evict.
The OpenAI-compatible endpoint at http://localhost:11434/v1 means every IDE plugin, chat frontend, and agent framework we tested plugged in without adapters. On a 24GB card we held a 7B chat model and a 3B code model resident together with room to spare.
Where it falls short: No GUI. Model discovery is a text file called Modelfile. Windows GPU support has been steady since the native release, but it still trails macOS for polish.
Pricing:
- Free: everything, open source under MIT.
- Paid: none.
Platforms: Windows, macOS, Linux.
Download: Publisher site - GitHub
Bottom line: Install this first. Even if your daily driver is a GUI app, you probably want Ollama as the backend.
2. LM Studio, best GUI for holding several models at once
LM Studio is the app to hand a non-CLI user who still wants the small-models trick. The 0.3 series added multi-model support in the same window, so you can load a chat model in one tab, a code model in another, and flip between them without waiting on a load each time. The built-in server exposes an OpenAI-compatible endpoint per loaded model, which means an editor extension can hit the code model while your chat window keeps talking to the chat model.
Model discovery is a search box against Hugging Face with quantisation picker and VRAM estimator baked in. The UI shows GPU offload sliders per model, which matters when you are trying to fit two mid-sized models on one card.
Where it falls short: Not open source. The free licence covers personal use, and there is a separate licence for work use that the download flow prompts you to acknowledge. Some quantisation formats load slower than in raw llama.cpp.
Pricing:
- Free: personal use, no time limit.
- Paid: separate work licence with per-seat terms, contact required.
Platforms: Windows, macOS, Linux.
Download: Publisher site - GitHub
Bottom line: The best pick if you want a real GUI, multiple loaded models, and a working API server without touching a terminal.
3. Msty, best for parallel chat and comparison
Msty is the app that made us stop A/B testing models by copy-pasting. Its split-view lets you send one prompt to two, four, or up to eight models at once and read the responses side by side. That is exactly the workflow you want when you are trying to figure out which small model is worth keeping loaded. Point it at Ollama and it inherits your local models automatically. Point it at OpenAI or Anthropic keys and it compares local against hosted in the same view.
The Knowledge Stack feature lets you attach folders of docs and PDFs and query them across models, which pairs well with a small chat model plus a small embedding model held resident together.
Where it falls short: Not open source. The free tier covers personal use, and the Aurum upgrade unlocks split chat above two models, workspace sync, and a few other extras. UI is heavy on features and takes a session to learn.
Pricing:
- Free: solid personal tier with two-way split chat and local models.
- Paid: Aurum lifetime licence unlocks the wider multi-model view and sync.
Platforms: Windows, macOS, Linux.
Download: Publisher site
Bottom line: Pick this when you actually need to see two models answer the same question at the same time.
4. Jan, best open-source multi-model chat client
Jan is the open-source alternative if you want an LM Studio-style GUI without the licence question. It ships its own inference engine, can run against Ollama or llama.cpp servers, and lets you swap the model on a per-conversation basis. Each thread remembers which model, which system prompt, and which parameters it was using, so a code thread and a writing thread can point at different specialists without bleeding into each other.
The built-in local server exposes an OpenAI-compatible endpoint, and the app itself is extensible via a small extension API. In our testing, model swap between two threads was near-instant when the target was already loaded and around three to eight seconds when it had to be loaded from disk on an NVMe.
Where it falls short: Cannot hold multiple models resident in its own engine yet. If you want two loaded at once, point it at Ollama and let Ollama do the resident management. GPU acceleration on Windows still lags the macOS build for some quantisation formats.
Pricing:
- Free: everything, open source under AGPL.
- Paid: none.
Platforms: Windows, macOS, Linux.
Download: Publisher site - GitHub
Bottom line: The right pick if open source matters and you want per-thread model routing without CLI work.
5. Open WebUI, best routing layer over Ollama
Open WebUI used to be branded as Ollama WebUI, and it remains the most complete web frontend for a self-hosted local stack. What earns it a spot here is its pipeline system: you can define scripts that pick the model per request based on prompt content, message history, or attached files. The classic example is a routing pipeline that sends anything starting with a code block to your coder model and everything else to your chat model, all inside one conversation.
It also supports parallel model responses in a single message, plus RAG with your own document store, plus function calling. Because it exposes the whole thing over your LAN, one Ollama box in the basement can serve routed multi-model chat to every machine in the house.
Where it falls short: Web-first. There is no native desktop client, so you either run it locally in a browser or self-host on a server. Setup asks for Docker or Python, not a double-click installer.
Pricing:
- Free: everything, open source under a BSD-3 style licence.
- Paid: none.
Platforms: Runs anywhere Docker or Python runs. Access via browser on Windows, macOS, Linux.
Download: Publisher site - GitHub
Bottom line: Use this when you want programmable routing and you are comfortable with a five-minute Docker install.
6. LocalAI, best drop-in API router across model families
LocalAI is the answer to a specific problem. You have a chat model in GGUF, a whisper model for transcription, an image model for diffusion, and a small embedding model, and you want one endpoint that speaks OpenAI-compatible for all of them. It runs each backend in its own worker, routes requests by model name, and returns responses in the same shape a hosted API would. Your Continue plugin, your note tool, and your custom script all point at the same URL and pick their model by name.
For the two-small-models pattern, it means a workflow like Continue asking qwen-coder-3b while your note app asks llama-3.2-3b, both served from the same proxy, both hot-loadable, both isolated so a heavy embed job does not stall chat.
Where it falls short: Setup is heavier than Ollama. Model config is YAML per backend. GPU acceleration works but wants some knowledge of what you have installed. Documentation is thorough but assumes you already know what an OpenAI-compatible payload looks like.
Pricing:
- Free: everything, open source under MIT.
- Paid: none.
Platforms: Windows, macOS, Linux, Docker.
Download: Publisher site - GitHub
Bottom line: The right choice when you want one local API that speaks to text, vision, audio, and embeddings without gluing separate servers together.
7. LiteLLM, best cross-backend proxy for task routing
LiteLLM started as a Python SDK for calling a hundred model providers with one interface, and its proxy server is what earns it a place on a local-first list. Run it in front of Ollama, LM Studio, LocalAI, or any mix, and clients talk to it with an OpenAI-compatible payload. It rewrites the request for whichever backend is behind the requested model name. That means one endpoint routes chat to your local Llama and code to your local Qwen Coder, with the same tool getting both without knowing where either one lives.
It also handles fallbacks, retries, rate limits, and per-key budgets, which matter the moment you start letting more than one app share your GPU.
Where it falls short: It is a proxy, not a runner. You still need Ollama or LocalAI behind it to actually serve the models. Configuration is a YAML file. The polished dashboard sits in the managed cloud tier, so self-hosters lean on the free UI plus config files.
Pricing:
- Free: open-source proxy, unlimited local use.
- Paid: hosted control plane with team features, billed per usage.
Platforms: Windows, macOS, Linux, Docker.
Download: Publisher site - GitHub
Bottom line: Add this the day you have more than one app hitting more than one local model and you want a single source of truth for names, routing, and limits.
How to pick the right one
If you want the least setup for the small-models trick, run Ollama and be done. Pull a 3B chat model and a 3B code model, let the daemon keep one warm while you use the other, and point your editor at the local endpoint.
If you want a GUI and no terminal, install LM Studio. Load two models in two tabs and use its built-in server for anything that needs an API.
If you actually want to compare models before committing VRAM to them, use Msty and split the same prompt across candidates until one wins.
If you want a GUI and open source, run Jan on top of Ollama.
If you want to route by prompt content rather than by app, self-host Open WebUI in front of Ollama and write a pipeline.
If your workload includes vision, transcription, or embeddings alongside chat, put LocalAI in the middle and let one API serve all of it.
If you already have three tools hitting two models and it is getting messy, drop LiteLLM in front and rename everything by role.
FAQ
Can I really run two local AI models at the same time?
Yes, on any GPU with enough VRAM to hold both. Two 3B or 4B quantised models fit comfortably on a 12GB card. Ollama and LM Studio can hold both resident, and requests to whichever one is idle return immediately without a cold load. The XDA piece that inspired this article is a working example, not a theoretical one.
Is one big local AI model always better than two small ones?
Not for real workflows. A single 8B model has to be a generalist, which means it is a middling coder and a middling writer. Two 3B models picked for their specialty (a coder plus a chat model, say) often beat the bigger single model on each specialist task, especially with the right routing. The one place a bigger single model still wins is deep reasoning on a single hard question that needs long context.
What is the best free app for running multiple local models on desktop?
Ollama, for the daemon and the API. Add Jan or Open WebUI on top when you want a GUI or programmable routing. All three are open source and free with no seat cap.
Does LM Studio let you run two models at once?
Yes, since the 0.3 series it supports loading multiple models in the same session with tabs, and the built-in OpenAI-compatible server exposes each loaded model as a separate endpoint. The main limit is VRAM.
How much VRAM do I need to run two local AI models?
A comfortable minimum for two 3B or 4B quantised models is 12GB. On 8GB cards you can just about run two very small models at Q4, but you will lose some concurrency headroom. For a 7B plus a 3B held resident together, plan on 16GB and up.
What is the difference between Ollama and LocalAI?
Ollama is a focused runner around llama.cpp with a clean CLI, a text-model catalogue, and an OpenAI-compatible endpoint. LocalAI is a broader API layer that can front many backends at once, including text, image, audio, and embeddings, with YAML config per backend. Ollama is easier to start with. LocalAI is what you reach for once your workflow is more than chat.