How to Run Multiple Ollama Models Together

Running AI models on your own computer used to sound like science fiction, but tools like Ollama have made it surprisingly simple. With just a few commands, you can spin up powerful language models like DeepSeek-R1 for reasoning, CodeLlama for coding help, or Gemma for writing and brainstorming without relying on cloud services.

The catch? One model rarely does everything well. A coding model might be great for generating functions but not so good at explaining concepts. A reasoning model can aid in problem-solving, but it may be too cumbersome for quick tasks. And if you're working with text and images, you'll also need a vision-enabled model.

That's why more and more developers are finding ways to run multiple Ollama models simultaneously. Doing so gives you:

• Flexibility to switch between lightweight models for speed and larger ones for accuracy.
• Task-specific support so you're always using the best model for coding, writing, teaching, or research.
• Multi-agent workflows where models can "team up"—one generating ideas, another refining or fact-checking them.

We'll examine the various approaches to setting up multiple Ollama models, how to integrate them seamlessly, and what real-world projects this enables. If you've been curious about stretching Ollama beyond a single model at a time, you're in the right place.

Running just one model in Ollama is sufficient for basic tasks, but many real-world workflows benefit from combining multiple models. By setting up Ollama concurrent models, you can balance performance, accuracy, and specialization in ways a single model simply can't.

1. Speed vs. accuracy trade-offs
Smaller models are lightweight and fast, making them great for quick answers or simple automation. Larger models are slower but usually deliver more accurate and detailed results. Running both allows you to choose the right tool for the moment, rather than forcing one model to handle everything.

2. Switching between models for different tasks
Not every model is designed for the same job. For example, CodeLlama is built for programming tasks, DeepSeek-R1 excels at reasoning, and LLaVA can handle vision inputs. Having multiple models available means you can easily switch between them based on your current task—coding, analysis, teaching, or content creation.

3. Multi-agent systems
One of the most exciting possibilities is using Ollama integration with multiple LLMs to create agent-like workflows. Imagine a reasoning model breaking down a problem, a coding model generating the solution, and a vision model reviewing image data, all working together. This kind of orchestration makes local AI much more powerful and adaptable.

Running multiple Ollama models isn't just about showcasing your machine's capabilities; it's about building smarter, more flexible workflows that save time and unlock new possibilities.

Before running multiple models simultaneously, it's essential to ensure your system is prepared. Multi-model setups can be demanding, so preparing your hardware and software properly will save you time and frustration in the long run. This is especially true if you require reliable local LLM multi-model support.

Hardware considerations (RAM, GPU, CPU)

• RAM: The more memory you have, the more models you can run concurrently. For lightweight models, 8–16 GB is sufficient, but for larger ones (with 13B+ parameters), 32 GB or more is ideal.

• GPU: A dedicated GPU with enough VRAM (6 GB minimum, 12–24 GB recommended) will speed up inference significantly. If you don't have one, Ollama can still run models on a CPU, but performance will be slower.

• CPU: Multi-core CPUs handle concurrent processes better, especially if you're running more than one model at the same time.

Ollama supports all major platforms.

• On macOS: Installation is straightforward with the official .pkg file.

• On Windows: Ollama now provides a dedicated installer and GUI app, but you can also run it via the command line.

• On Linux: Installation is typically done with a simple script or package manager.

Once installed, confirm that the ollama command works in your terminal by running:
ollama --version

Setting up Docker (optional for orchestration)

For advanced users, Docker can be a powerful way to isolate and orchestrate multiple models. By running each model in its own container, you can prevent conflicts, manage resources more efficiently, and scale workflows more easily. While not required for simple setups, Docker becomes valuable if you plan to build multi-agent systems or run multiple Ollama models as part of a larger project.

With these basics in place, your system will be ready for experimenting with multi-model workflows in Ollama.

1. Run Models in Parallel Sessions

The simplest way to run two Ollama models at once is by opening multiple terminal sessions. Each session can load a different model, allowing you to keep them running simultaneously.

# Terminal 1
ollama run codellama

# Terminal 2
ollama run deepseek-r1

This setup is lightweight and doesn't require any additional tools, but keep in mind that system performance will depend on your available RAM and CPU/GPU capacity.

2. Switching Between Models Easily

If you only need one model active at a time, the command line makes Ollama model switching straightforward. You can specify the model directly:

ollama run gemma

For developers who frequently switch models, simple shell scripts can automate this process. For example, a script could launch CodeLlama when you're coding and Phi-4 when you need a lightweight reasoning model.

3. Using Docker or VM for Isolated Model Instances

For greater stability and resource management, consider using Docker or virtual machines to run models in separate environments. This approach prevents conflicts and facilitates easier scaling.

version: '3'
services:
  codellama:
    image: ollama/ollama
    command: run codellama
  deepseek:
    image: ollama/ollama
    command: run deepseek-r1

This method is beneficial for larger projects where you want a clean Ollama multi-model setup without worrying about process conflicts.

4. Orchestrating Models via API

For programmatic control, the Ollama API enables you to manage multiple models within your applications. You can call different models from code, switch dynamically, and even integrate Ollama with automation tools like n8n, LangChain, or LlamaIndex.

This type of Ollama model orchestration is powerful for building advanced workflows—like routing simple queries to a small model and sending complex tasks to a larger one. It also enables Ollama workflow automation, where models interact as part of a bigger pipeline.

5. Multi-Agent Frameworks

The most advanced method is using Ollama agents with multiple models. In this setup, each model is assigned a specific role:

• DeepSeek-R1 for reasoning
• CodeLlama for coding
• LLaVA for vision tasks

Together, they can collaborate on solving complex problems—much like a team of specialists working on different parts of a project. This is the foundation for true multi-agent systems that developers are now starting to experiment with.

The real power of Ollama lies in combining models within practical workflows. By setting up Ollama integration with multiple LLMs, you can match each model to the task it handles best. Here are a few examples:

A developer might use CodeLlama to generate functions, boilerplate code, or quick fixes, while DeepSeek-R1 double-checks the logic, explains errors, or optimizes the solution. Running both together creates a more reliable coding assistant than either could provide alone.

Writers and content creators can pair Gemma for text generation (such as blog drafts, scripts, and product descriptions) with LLaVA for vision-based tasks, like creating captions or analyzing visual content. This combination makes it easier to handle both written and visual storytelling within a single workflow.

Teachers or students can run Phi-4 as a lightweight model for general Q&A and concept explanations, while SmolLM handles math problems or structured learning tasks. Together, they can provide interactive, subject-specific tutoring without needing internet access.

These examples show how multiple Ollama models can work like a team of specialists—each focused on its strengths, but integrated into a single, seamless workflow.

Running multiple models simultaneously can be powerful, but it also requires some fine-tuning to achieve the best results. Here are a few best practices to keep your setup efficient and stable:

1. Optimize Context Length

Longer context windows can eat up memory quickly. Unless your use case requires very long conversations, set context lengths that strike a balance between performance and accuracy.

2. Use Quantized Models for Lower Hardware Strain

Many Ollama models are available in quantized formats (like Q4 or Q5). These versions reduce memory and CPU/GPU usage, making it easier to run multiple models in parallel without crashing your system.

3. Monitor System Performance

Keep an eye on resource usage—look at tokens per second, RAM, and CPU/GPU load. This will help you spot bottlenecks early and decide when it's time to scale up or switch models.

4. Cache Frequently Used Models

Loading a model from scratch each time slows down the process. Caching or preloading the models you use most often can significantly speed up workflows and improve responsiveness.

By following these practices, you'll maximize efficiency while keeping your multi-model Ollama setup stable and ready for real-world tasks.

While running multiple Ollama models can unlock powerful workflows, it's not without trade-offs. Here are some of the common challenges to keep in mind:

1. RAM & GPU Bottlenecks

Large models require significant memory and compute power. Running several at once can overwhelm your system, especially if you're working on a standard laptop or without a dedicated GPU.

2. Model Switching Overhead

Even with optimizations, switching between models adds overhead. Loading times, caching, and process management can slow down workflows if you frequently switch between models.

3. Trade-off Between Speed and Accuracy

Smaller models run faster but may sacrifice reasoning or precision. Larger models are more accurate but slower and heavier on resources. Balancing these two extremes is an ongoing challenge when using concurrent models.

Understanding these limitations upfront helps you plan more intelligent workflows and decide when it makes sense to run multiple models versus sticking to one.

The way we utilize local AI is evolving rapidly, and running multiple Ollama models together is just the beginning. A few key trends point to where things are headed:

1. Rise of Local AI Agents

More developers are experimenting with agent-style workflows—where specialized models collaborate to solve complex tasks. Running reasoning, coding, and vision models in parallel is likely to become the norm rather than the exception.

2. Roadmap for Multi-Agent Support

While Ollama already supports running models locally, future updates are expected to improve orchestration, context management, and seamless switching between models. This could make multi-model setups easier for everyday developers.

3. Demand for Orchestration Frameworks

Frameworks like LangChain, LlamaIndex, and n8n are gaining traction for coordinating multiple LLMs. As demand grows, we'll likely see tighter integrations with Ollama, allowing local-first AI stacks to rival cloud-based solutions.

Multi-model Ollama setups will continue to expand as users demand more flexible, agent-driven workflows—all while maintaining the efficiency and privacy of local AI.

Running multiple Ollama models together opens up new possibilities—from faster task switching to building robust multi-agent systems. While there are challenges, such as hardware demands and model switching overhead, the benefits of flexibility, specialization, and local-first control make it worthwhile to explore these options.

If you're a developer, creator, or educator, now is the perfect time to experiment with your own workflows. Try pairing reasoning, coding, or vision models and see how much more efficient your projects can become.

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