Ollama's new official desktop app for macOS and Windows makes it easy to run open-source models locally. Chat with LLMs, use multimodal models with images, or reason about files, all from a simple, private interface.
When Ollama walks out of your command line and starts interacting with you as a native desktop app, don't be surprised :)
This new app dramatically lowers the barrier to running top open-source models locally. You can now chat with LLMs, or drag and drop files and images to interact with multimodal models, all from a simple desktop interface. And most importantly, it's Ollama, which is one of the most trusted and liked products for users who care about privacy and data security.
Bringing the Ollama experience to people who aren't as comfortable with the command line will undoubtedly accelerate the adoption of on-device AI.
@zaczuo Love it!!!! I've been an Ollama user for a while now, and have told many others about it, but they've never been as comfortable with it, so just finished sending this out to everyone I know! 💪
@marco_visin Agentic use is perfect for local models. As you don't need speed. You can Que up some tasks overnight in different branches. And let it cook.
That's a significant update! Thank you for your product, really like it 🙌
Will the UI be open source as well, so we can adjust/modify the way it works?
Report
awesome, just downloaded.
I've used tools like LLMStudio in the past but this is super slick.
Question: Is there a place to get an overview of best use cases for different models? I see the overview of models on the home page but contexualizing what certain models are best for would be massively helpful to me.
I really like the UI of Ollama, especially the CLI. There's a lot to love there. Unfortunately, on macOS it's not the best option because it doesn't support MLX, which runs models 10% to 20% faster, and with lower memory usage. There is an open ticket with a pull request for adding a MLX backend from 2023, but it's been stalled for awhile. If you use mac, try LM Studio, mlx-lm, or swama instead.
Running top vision models *locally* is huge—no more waiting on cloud stuff or privacy worries, tbh. This update is realy next-level, hats off to the team!
Introduced with the release of Ollama's support for @GPT OSS, is Turbo; Ollama's privacy-first datacenter-grade cloud inference service.
Whilst it's currently in preview, the service costs $20/m, and has both hourly and daily limits. Usage-based pricing will be available soon. So far, the service only has gpt-oss-12b and gpt-oss-120b models, and works with Ollama's App, CLI, and API.
highlight secure, offline use. Users echo the simplicity—easy setup, Docker-like workflows, quick prototyping, solid performance, and cost savings. Some note best results with mid-size models and smooth integrations via APIs.
I switched to Ollama from clunkier solutions and I have no regrets. Multimodal model support is well-implemented - feeding images through the API is just as easy as text. It’s great that the project is evolving so fast; support for new models usually pops up almost the day after they drop on Hugging Face. This is the simplest way to "play around" with modern AI locally without the headache of setting up the environment.
What needs improvement
I’d love to see a simpler way to import my own .gguf files downloaded outside the official Ollama library, without having to manually define all the parameters in a Modelfile.
How much disk space do common models require locally?
Typically, popular medium-sized models (7B-8B parameters) like Llama 3 or Mistral take up about 4.5-5 GB in standard 4-bit quantization
How simple is creating and customizing your own models?
It's simple using the Modelfile system, the process is very similar to writing a Dockerfile
We’re exploring Ollama to test and run LLMs locally—faster iteration, zero latency, total control. It’s like having our own AI lab, minus the GPU bills
What's great
fast performance (1)local AI model deployment (11)no third-party API reliance (3)AI server hosting (2)
Flowtica Scribe
Hi everyone!
When Ollama walks out of your command line and starts interacting with you as a native desktop app, don't be surprised :)
This new app dramatically lowers the barrier to running top open-source models locally. You can now chat with LLMs, or drag and drop files and images to interact with multimodal models, all from a simple desktop interface. And most importantly, it's Ollama, which is one of the most trusted and liked products for users who care about privacy and data security.
Bringing the Ollama experience to people who aren't as comfortable with the command line will undoubtedly accelerate the adoption of on-device AI.
Visla
@zaczuo Love it!!!! I've been an Ollama user for a while now, and have told many others about it, but they've never been as comfortable with it, so just finished sending this out to everyone I know! 💪
DiffSense
Needs MCP and agentic features. Maybe soon? 🙏
Orango
DiffSense
@marco_visin Agentic use is perfect for local models. As you don't need speed. You can Que up some tasks overnight in different branches. And let it cook.
Orango
CNVS
That's a significant update! Thank you for your product, really like it 🙌
Will the UI be open source as well, so we can adjust/modify the way it works?
awesome, just downloaded.
I've used tools like LLMStudio in the past but this is super slick.
Question: Is there a place to get an overview of best use cases for different models? I see the overview of models on the home page but contexualizing what certain models are best for would be massively helpful to me.
TweetDeck in #280Characters
I really like the UI of Ollama, especially the CLI. There's a lot to love there. Unfortunately, on macOS it's not the best option because it doesn't support MLX, which runs models 10% to 20% faster, and with lower memory usage. There is an open ticket with a pull request for adding a MLX backend from 2023, but it's been stalled for awhile. If you use mac, try LM Studio, mlx-lm, or swama instead.
It's great and convenient!
Haimeta
Running top vision models *locally* is huge—no more waiting on cloud stuff or privacy worries, tbh. This update is realy next-level, hats off to the team!