XDA ran a piece this month arguing that two 2 GB local models running in parallel beat one 8 GB model at almost every real task, and the writer was correct. A small model tuned for code plus a small model tuned for chat, running side by side, uses less VRAM, responds faster, and lets you route the right question to the right tool. The friction is the tooling: most local LLM apps assume one model at a time and one chat window. The eight apps for running multiple local LLMs at once on desktop below all handle concurrent models, either natively or by exposing an OpenAI-compatible endpoint the second app can point at.
What to look for in a multi-model LLM runner
Real criteria for running more than one model:
- Native multi-model loading. Some tools swap models on request; others actually hold two loaded in RAM.
- OpenAI-compatible endpoint. A running server on port 11434 or 1234 that other tools can call.
- Model routing. Send code prompts to Qwen2.5-Coder, chat to Llama 3.2, based on the request.
- GPU offload with layer split. Load two small models across the same GPU without one crashing the other.
- CPU fallback for a second model. If the GPU is full, the second model runs on CPU without dying.
- Session isolation. Two chats do not stomp on each other’s context window.
Quick comparison
| App | Best for | Multi-model | Free plan | Starting price | GUI |
|---|---|---|---|---|---|
| Ollama | CLI-first, OpenAI endpoint | Yes (parallel loading) | Free | Free | No (CLI) |
| LM Studio | Point-and-click model manager | Yes | Free | Free | Yes |
| Jan | Fully offline chat + backend | Yes | Free | Free | Yes |
| llama.cpp server | Rawest control, smallest footprint | Yes (per-process) | Free | Free | No |
| LiteLLM | Route to 100+ backends | Router | Free | Free (cloud opt-in) | Web UI |
| Open WebUI | Multi-model chat frontend | Yes | Free | Free | Yes (web) |
| vLLM | Production-grade throughput | Yes (tensor parallel) | Free | Free | No |
| Msty | Local + cloud in one desktop app | Yes (Split Chat) | Free | 8.99 USD/month | Yes |
The apps
1. Ollama, the default runtime
Ollama loads and unloads models on demand and exposes an OpenAI-compatible API on port 11434. Set OLLAMA_NUM_PARALLEL=2 and OLLAMA_MAX_LOADED_MODELS=3 in your env, restart the service, and you can hit two models in parallel from any client. On an M2 Pro with 32 GB unified memory we ran Qwen2.5-Coder 3B and Llama 3.2 3B side by side with headroom for a small vision model.
Where it falls short: No native GUI. The default context length (2048) is small for real work, so tune it. The ollama serve process does not auto-pin models to specific GPUs on multi-GPU rigs.
Pricing:
- Free: Everything.
Platforms: macOS, Windows, Linux.
Bottom line: The base layer. Almost every other tool on this list either wraps Ollama or shares its architecture. Install it first.
2. LM Studio, the polished GUI
LM Studio is the app most people install first. It ships a full model browser, a chat UI, and, since the 0.3 release, a built-in local server that runs multiple models concurrently. The model catalog pulls straight from Hugging Face and filters by what will fit your machine. Split Chat mode lets you compare two models on the same prompt live.
Where it falls short: Not open source (free for personal use, contact them for work use). The bundled llama.cpp version can lag the upstream.
Pricing:
- Free: All features for personal use.
- Paid: Contact sales for team/commercial licensing.
Platforms: Windows, macOS, Linux.
Download: LM Studio
Bottom line: The best entry point if you want a GUI. Pair with Ollama for scripting.
3. Jan, the open-source LM Studio
Jan is the open-source alternative to LM Studio: same idea, MIT licensed, and a stated goal of running fully offline. Multi-model chat threads let each thread pin to a different model. It runs its own llama.cpp backend and can proxy to a remote Ollama or OpenAI-compatible API when you want a bigger model.
Where it falls short: Younger project than LM Studio. Fewer packaged extensions.
Pricing:
- Free: Everything (open source).
Platforms: Windows, macOS, Linux.
Bottom line: Pick this if open source matters. Functionally very close to LM Studio.
4. llama.cpp server, the base primitive
llama.cpp ships a stateless HTTP server that you launch per model. Two shell commands, two ports, two models. This is the tightest way to hold two GGUF quantizations in RAM: no wrapper, no scheduler, no telemetry, and the smallest memory overhead of anything here.
Where it falls short: You wire up your own routing. No GUI. No model manager. Command-line only for setup.
Pricing:
- Free: Open source (MIT).
Platforms: Windows, macOS, Linux (also Docker).
Download: GitHub
Bottom line: For people who want to understand what the abstraction layers are hiding. Perfect for scripting rigs.
5. LiteLLM, the router
LiteLLM does not host models; it routes to them. Point it at your Ollama instance, your LM Studio server, an Anthropic API key, and an Azure endpoint, then send every request through one OpenAI-compatible URL and let the router decide. A YAML config file with per-model fallbacks and rate limits lets a coding assistant fall back to a smaller local model when the big one is busy.
Where it falls short: Adds an extra process to manage. Debugging routing decisions takes some log reading.
Pricing:
- Free: Open source (proxy and SDK).
- Paid: LiteLLM Cloud (optional) is subscription-based for observability.
Platforms: Windows, macOS, Linux (Python, Docker).
Download: GitHub
Bottom line: The glue between multiple runners. Skip if you only run one backend, mandatory if you run three.
6. Open WebUI, the ChatGPT-alike frontend
Open WebUI is the polished chat interface for local models. Connect it to Ollama (or any OpenAI-compatible endpoint), and it gives you multi-user auth, chat history, model switching mid-conversation, and side-by-side model comparison in a single browser tab. Handy for a household where four people share one machine.
Where it falls short: Runs in Docker for the smooth experience; native install is possible but fiddlier. Uses about 500 MB of RAM before any model is loaded.
Pricing:
- Free: Open source (BSD-3).
Platforms: Windows, macOS, Linux (Docker).
Download: GitHub
Bottom line: The best frontend if you already have Ollama running. Split Chat mode is the killer feature.
7. vLLM, the throughput engine
vLLM is what you install when the desktop tools stop scaling. PagedAttention memory management holds multiple models with better VRAM utilization than any single-model runner. Tensor parallelism splits one model across GPUs; multiple deployments on the same server host different models simultaneously. Overkill for a laptop, essential for a workstation with two GPUs.
Where it falls short: Linux-first (Windows via WSL2). Requires Python and CUDA setup. Not aimed at casual users.
Pricing:
- Free: Open source (Apache 2.0).
Platforms: Linux, Windows (WSL2), macOS (Apple Silicon partial).
Download: GitHub
Bottom line: Only reach for this if you have a serious GPU. On a single 4090, Ollama is easier and nearly as fast.
8. Msty, the local-plus-cloud desktop app
Msty ships as a single desktop binary that runs local models via a bundled runtime and cloud models via your own API keys, side by side in the same window. Split Chat compares up to four models per prompt. The Knowledge Stacks feature indexes local documents and shares the index across models.
Where it falls short: Free tier is generous but caps split chat at two models. The paid tier for four-way split is on the pricey side.
Pricing:
- Free: Two-way split chat, all local model features.
- Paid: 8.99 USD/month or 79 USD/year for four-way split and premium sync.
Platforms: Windows, macOS, Linux.
Download: Msty
Bottom line: The easiest way to compare local vs. cloud models side by side. Pay for it only if you need four-way split.
How to pick the right one
- If you are starting from zero, install Ollama and Open WebUI. That is the reference stack.
- If you want a GUI without touching Docker, LM Studio or Jan.
- If open source matters and you want a GUI, Jan.
- If you route between local and cloud APIs, add LiteLLM.
- If you compare model outputs constantly, Msty or LM Studio Split Chat.
- If you script everything, llama.cpp server on custom ports.
- If your rig has two GPUs and you serve a small team, vLLM.
FAQ
Can I run two LLMs on one GPU?
Yes, if both quantized versions fit in VRAM. A 3B model at Q4_K_M uses about 2 GB; two of those fit comfortably on an 8 GB card. Set Ollama’s OLLAMA_MAX_LOADED_MODELS above 1 to keep them warm.
Are small local LLMs actually good?
Since Qwen 2.5 and Llama 3.2 in the 3B-7B range, small models handle summarization, code completion, extraction, and short-form chat at close to GPT-3.5 quality. They fail on long-context reasoning and complex multi-step tasks. Route around this with a bigger model for hard prompts.
What is the difference between Ollama and LM Studio?
Ollama is a CLI-first runtime with an HTTP API. LM Studio is a GUI-first desktop app that runs a bundled llama.cpp server. They can run in parallel on the same machine.
Do I need a GPU to run local LLMs?
No, but it helps. A 3B model runs at readable speed (10 to 20 tokens/second) on a modern Apple Silicon CPU or an Intel/AMD chip with 16 GB RAM. GPUs push that to 60 to 200 tokens/second.
Which app runs the fastest?
Per token, vLLM on a modern NVIDIA GPU. Per install, llama.cpp with a good quant. Per convenience, Ollama.