Loading OpenMindAI
OpenMindAI adapts to available hardware, but local AI performance still depends on memory, compute, model size and backend support.
The default 4B local model does not require a high-end CPU, dedicated GPU or workstation-class memory. Faster hardware mainly improves response speed and allows larger models, longer context and heavier multitasking.
Initial storage planning uses about 6 GB as a starting point for the runtime and default model. Keep more free space for additional models, projects, cache and generated files. SSD storage improves loading speed, but it is not a hard requirement for installation.
The repository uses Node.js 24 in CI, a current stable Rust toolchain and platform-specific Tauri build dependencies. Windows source builds also require the normal Microsoft C++/MSVC tooling.
OpenMindAI is designed to run across a wide range of desktop and mobile hardware, but local AI is different from a normal application. The application itself is relatively lightweight; the main hardware requirements come from the AI models you choose to install and the workloads you want to run.
A small local model can work on a modest laptop or modern smartphone, while larger reasoning, coding, or vision models may require significantly more memory, storage, and processing power. For this reason, OpenMindAI does not use one fixed hardware requirement for every user.
The tables below provide practical minimum and recommended configurations for the OpenMindAI application. Actual performance will depend on the selected model, quantization, context length, operating system, CPU, GPU, available memory, storage speed, battery state, and thermal limits.
Windows is currently the primary packaged desktop platform for OpenMindAI. A 64-bit version of Windows is recommended for local model execution and the full desktop experience.
| Component | Minimum | Recommended |
|---|---|---|
| Operating System | Windows 10 64-bit | Windows 11 64-bit, latest updates |
| Processor | Modern 4-core x64 CPU | 6-core or better modern x64 CPU |
| Memory | 8 GB RAM for lightweight models | 16 GB RAM or more |
| High-performance AI use | Limited on 8 GB systems | 32 GB RAM or more for larger models and heavier workloads |
| Graphics | CPU-only operation supported where compatible | Dedicated GPU with supported acceleration |
| GPU Memory | Not required for CPU mode | 6–8 GB VRAM or more for better local AI performance |
| Storage | 5 GB free for application/runtime plus model space | 20–50 GB+ free depending on installed models |
| Internet | Required for initial downloads and online features | Broadband connection recommended for large model downloads |
| Display | 1366×768 | 1920×1080 or higher |
A system with 8 GB RAM should be considered an entry-level configuration. It is suitable only for smaller models and shorter contexts. For a more comfortable local AI experience, 16 GB RAM is strongly recommended. Developers, researchers, and users planning to run larger reasoning or coding models should consider 32 GB RAM or more.
A dedicated GPU is not mandatory because OpenMindAI can use compatible CPU execution paths. However, supported GPU acceleration can significantly improve generation speed and responsiveness.
OpenMindAI supports macOS, with performance depending heavily on the Mac generation, available unified memory, model size, and the local runtime being used.
| Component | Minimum | Recommended |
|---|---|---|
| Operating System | Recent supported macOS version | Latest stable macOS |
| Processor | 64-bit Intel Mac or Apple Silicon where supported | Apple Silicon recommended |
| Memory | 8 GB unified/system memory for lightweight use | 16 GB unified memory or more |
| Advanced workloads | Limited with 8 GB | 24–32 GB+ for larger models and deeper workflows |
| Graphics | Integrated graphics supported according to runtime | Apple Silicon hardware acceleration where supported |
| Storage | 5 GB free plus AI model storage | 20–50 GB+ free |
| Internet | Required for downloads and online features | Broadband recommended |
Apple Silicon systems are generally better suited to modern local AI because CPU, GPU, and unified memory can work together efficiently. However, available memory remains the main limiting factor. A Mac with 8 GB memory should use smaller models, while 16 GB or more offers significantly more flexibility.
Larger coding, reasoning, and vision models may require 24 GB, 32 GB, or more unified memory depending on model size and context.
OpenMindAI supports Linux desktop workflows, especially for developers, researchers, and technical users. Current desktop support is focused on x64 environments; Linux ARM64 should not be treated as a supported primary target at this time.
| Component | Minimum | Recommended |
|---|---|---|
| Architecture | x86_64 / AMD64 | x86_64 / AMD64 |
| Distribution | Modern 64-bit Linux distribution | Current Ubuntu, Debian, Fedora, or comparable maintained distribution |
| Processor | Modern 4-core x64 CPU | 6-core or better modern CPU |
| Memory | 8 GB RAM for lightweight models | 16 GB RAM or more |
| Advanced workloads | Limited | 32 GB RAM+ for larger models and agents |
| Graphics | CPU operation where supported | Supported AMD, NVIDIA, or other compatible GPU acceleration |
| GPU Memory | Not required | 6–8 GB VRAM or more recommended for accelerated workloads |
| Storage | 5 GB free plus model storage | 20–50 GB+ free |
| Internet | Required for downloads and connected features | Broadband recommended |
Linux installations can vary significantly depending on distribution, drivers, GPU stack, and system libraries. Users planning to use hardware acceleration should keep their graphics drivers and required runtime components up to date.
For software development, local agents, larger codebases, and future Code Agent workflows, 16 GB RAM should be considered the practical minimum and 32 GB or more is preferable.
Because model size matters more than the OpenMindAI application itself, the following table is a better way to understand practical desktop requirements.
| Usage Level | Suggested RAM | GPU | Typical Use |
|---|---|---|---|
| Lightweight | 8 GB | CPU or integrated graphics | Small local models, simple chat, short documents |
| Standard | 16 GB | Optional dedicated GPU | General chat, reasoning, documents, moderate models |
| Advanced | 32 GB | 8 GB+ VRAM recommended | Larger models, coding, research, vision, longer contexts |
| Workstation | 64 GB+ | 12–24 GB+ VRAM or strong unified memory | Large local models, multiple tools, heavy research, agent workloads |
These are practical guidance ranges rather than strict model limits. A highly optimized quantized model may work with less memory, while a long context or multimodal workload may require significantly more.
OpenMindAI for Android is designed around local on-device AI. Because phones have tighter memory, storage, battery, and thermal limits, model selection is especially important.
| Component | Minimum | Recommended |
|---|---|---|
| Android Version | Modern 64-bit Android device | Recent Android version with current security updates |
| Architecture | 64-bit ARM device | Modern ARM64 smartphone |
| Memory | 6 GB RAM | 8 GB RAM or more |
| Advanced mobile AI | Limited below 8 GB | 12 GB RAM or more for larger local models |
| Storage | At least 2 GB free for lightweight use | 10–20 GB+ free for multiple models |
| Processor | Modern multi-core mobile SoC | Recent mid-range or flagship SoC |
| GPU Acceleration | Optional depending on runtime/device | Supported Vulkan-capable hardware where available |
| Internet | Required for model downloads, Search, Research, and updates | Wi-Fi recommended for large downloads |
| Camera | Optional | Required for camera/Lens workflows |
| Microphone | Optional | Required for voice input |
OpenMindAI mobile models are downloaded separately instead of being bundled into the application package. This allows users to choose a model that matches their phone rather than downloading a very large model automatically.
A device with 6 GB RAM should use small models. For a smoother experience, 8 GB RAM or more is recommended. Users who want larger reasoning or vision models should prefer devices with 12 GB RAM or more.
OpenMindAI supports iPhone and iPad with local AI features. Performance depends on the device generation, available memory, model size, and supported local acceleration.
| Component | Minimum | Recommended |
|---|---|---|
| Operating System | Recent supported iOS/iPadOS version | Latest stable iOS/iPadOS |
| Device | Modern 64-bit iPhone or iPad | Recent Apple Silicon-based iPhone/iPad |
| Memory | Device-dependent; lightweight models only on lower-memory hardware | Newer devices with higher available memory |
| Storage | At least 2 GB free | 10–20 GB+ free for several models |
| Hardware Acceleration | Device/runtime dependent | Metal-capable modern Apple hardware |
| Internet | Required for downloads, Search, Research, and updates | Wi-Fi recommended |
| Camera | Optional | Required for Lens/camera workflows |
| Microphone | Optional | Required for voice input |
iPhone and iPad memory is managed differently from desktop systems, so local AI models must be selected carefully. OpenMindAI is designed to use mobile-appropriate models and avoid treating a phone like a workstation.
| Device Class | Practical Use |
|---|---|
| 6 GB RAM Android / lower-memory mobile device | Lightweight local model, normal chat, short context |
| 8 GB RAM modern phone | General local chat, Think, documents, moderate workloads |
| 12 GB+ RAM Android flagship | Larger mobile models, improved reasoning, heavier documents and vision |
| Recent high-end iPhone/iPad | Stronger on-device inference subject to iOS memory limits and model compatibility |
Mobile performance may reduce during long sessions because phones actively manage heat and battery consumption. A model that starts quickly may slow down if the device becomes hot.
The OpenMindAI application is only one part of the required storage. Local AI models are usually the largest files.
A single model may require several hundred megabytes or several gigabytes of storage. Larger models, vision models, projectors, speech models, and multiple installed variants can quickly increase total storage use.
Typical model sizes can include:
| Model Type | Approximate Storage Range |
|---|---|
| Very small mobile model | 300 MB – 1 GB |
| Lightweight local model | 1 – 2 GB |
| General-purpose model | 2 – 5 GB |
| Larger reasoning/coding model | 5 – 10+ GB |
| Vision model | Several GB, sometimes with an additional projector |
| Multiple-model library | 20 GB – 100+ GB depending on collection |
Actual model sizes vary by architecture and quantization.
OpenMindAI provides model management so users can remove models they no longer need and download them again later.
Storage size and runtime memory are not the same thing.
A 4 GB model file does not necessarily mean the application needs exactly 4 GB of RAM. Runtime memory also includes model buffers, context, cache, application memory, vision components, operating-system use, and other processes.
For this reason, users should always keep additional free memory beyond the model file size.
A practical rule is that larger models and longer context windows require substantially more memory. If the system is close to its memory limit, generation may become very slow or the model may fail to start.
OpenMindAI's hardware-aware routing and compatibility checks are designed to reduce this problem, but users still benefit from choosing models appropriate for their devices.
OpenMindAI can run local models on compatible CPUs when GPU acceleration is unavailable.
CPU-only inference makes local AI accessible on more systems, but response speed depends heavily on the processor and model size.
A modern 6-core or 8-core desktop processor provides a much better experience than an older dual-core or low-power CPU. Smaller quantized models are generally the best choice for CPU-only systems.
Users who primarily want chat, writing, document analysis, and occasional reasoning can still have a useful experience without a dedicated GPU as long as the selected model is appropriate.
A dedicated GPU is optional, not mandatory.
When a compatible GPU runtime is available, OpenMindAI can use hardware acceleration to improve model loading and generation speed.
GPU requirements vary by operating system and hardware vendor. The amount of available VRAM is especially important for larger models.
As practical guidance, 6–8 GB VRAM can be useful for smaller and medium local models, while 12 GB, 16 GB, 24 GB, or more provides much more flexibility for larger models and advanced workloads.
A model does not always need to fit entirely inside GPU memory because some runtimes can split work between system RAM and GPU memory, but performance will vary.
OpenMindAI is local-first, but an internet connection is still required for features that depend on remote resources.
Internet access is normally required for initial application installation, runtime downloads, model downloads, software updates, Search, Deep Research, connected apps, GitHub access, cloud productivity integrations, and other external services.
Once the required local model and runtime are installed, normal local chat, reasoning, local document work, project context, supported vision, supported voice features, and local history can continue without a constant internet connection.
For large model downloads, a stable broadband or Wi-Fi connection is strongly recommended because individual files can be several gigabytes.
OpenMindAI is designed for modern desktop and mobile screens.
For desktop, a minimum resolution around 1366×768 is practical, while 1920×1080 or higher provides a better experience for Projects, documents, file panels, code workflows, and multi-panel layouts.
On mobile, the interface is responsive and designed for normal modern smartphone and tablet displays.
A camera is not required for normal OpenMindAI chat.
Camera access is needed only when the user wants to use camera-based visual workflows such as OpenMindAI Lens or capture a photo for AI analysis.
The application should request camera permission only when relevant to those features.
A microphone is optional.
It is required for voice input and speech-to-text workflows such as OpenMindAI Hear.
Users who do not want voice features can continue using the application normally without microphone access.
Notifications are optional and controlled by the operating system.
On supported mobile and desktop platforms, notifications can be useful for long-running responses, downloads, or completed tasks.
Users can deny notification permission without losing the core AI functionality.
OpenMindAI is designed to avoid unnecessary unrestricted storage access.
On mobile, models and application data are stored in app-managed storage, while system file pickers are used for user-selected documents where applicable.
On desktop, users may choose or approve storage and project locations according to the OpenMindAI workflow.
Project agents and future code-agent capabilities should remain scoped to approved directories rather than automatically receiving unrestricted file-system access.
Basic text and document analysis mainly depends on the selected local model and available memory.
Large PDFs, long DOCX files, large codebases, or many attachments can require additional RAM because more context must be processed.
For frequent document-heavy workloads, 16 GB RAM is recommended on desktop, while 32 GB or more is preferable for larger files, longer context windows, or multiple active tools.
Vision features require a compatible multimodal model.
Some local vision models also require an additional multimodal projector file. OpenMindAI checks for the required components before launching supported vision workflows.
Vision generally requires more memory than text-only chat.
For desktop vision use, 16 GB RAM should be considered a practical starting point, while 32 GB or more provides more flexibility.
On mobile, vision support depends on the model, phone memory, runtime, and device capability.
Voice input and speech processing require supported speech components.
Depending on the platform, speech-to-text may use a local speech model, while text-to-speech may use available platform or local speech capabilities.
Speech models also consume storage and memory, although they are normally much smaller than large language models.
A microphone is required for voice input, while speakers or headphones are required to hear spoken responses.
Project workspaces themselves do not require high-end hardware, but agent-assisted development and large project analysis benefit from more memory and faster storage.
For project-based work, a practical desktop configuration is:
| Component | Recommended |
|---|---|
| RAM | 16 GB minimum for serious project work |
| Preferred RAM | 32 GB or more |
| Storage | SSD strongly recommended |
| CPU | Modern 6-core or better |
| GPU | Optional but helpful for faster local inference |
| Git | Recommended for software projects |
| Internet | Optional for local work; required for remote repositories and connected services |
Large source trees, long contexts, local testing, multiple tools, and larger code models will increase resource usage.
The upcoming OpenMindAI Code Agent is being designed for deeper software engineering workflows. It will require more system resources than normal chat because it may need to inspect project files, maintain larger context, reason across multiple files, execute approved development commands, validate changes, and work with Git state.
For a good Code Agent experience, 16 GB RAM should be considered the minimum practical desktop configuration, while 32 GB RAM or more is recommended.
| Component | Minimum Practical | Recommended |
|---|---|---|
| RAM | 16 GB | 32 GB+ |
| Processor | Modern 4–6 core CPU | Modern 8-core or better |
| Storage | SSD with sufficient project/model space | Fast NVMe SSD |
| GPU | Optional | Supported GPU with 8 GB+ VRAM |
| Git | Recommended | Strongly recommended |
| Development tools | Depends on project | Required compilers, runtimes, package managers, and test tools installed |
| Internet | Not required for purely local projects | Required for package downloads, GitHub, remote APIs, or web research |
The Code Agent will not make every project dependency disappear. If a project requires Node.js, Rust, Python, Flutter, Java, Docker, a compiler, a database, or another development tool, that dependency must still be installed when the agent needs to build, run, or test the project.
The agent's effectiveness will also depend on the quality and context capacity of the installed local coding model.
| Platform | Status / Focus |
|---|---|
| Windows x64 | Primary desktop platform |
| macOS | Supported desktop platform; hardware/runtime capability varies |
| Linux x64 | Supported desktop workflow; hardware coverage continues to expand |
| Linux ARM64 | Not currently a primary supported target |
| Android | Supported mobile platform |
| iOS / iPadOS | Supported mobile platform |
Platform capabilities may evolve as OpenMindAI, local runtimes, drivers, and hardware support continue to improve.
OpenMindAI does not require high-speed internet for local inference because the model runs on the device.
However, model downloads can be large.
A 3 GB model may take only a few minutes on a fast connection but much longer on a slow connection. For this reason, broadband or Wi-Fi is recommended during initial setup and when downloading larger models.
After installation, normal local model inference does not consume internet bandwidth.
An SSD is strongly recommended for desktop use.
Local AI model files are large and can take noticeable time to load from slow storage. NVMe SSDs provide the best experience, but SATA SSDs are also suitable.
Traditional hard drives can work for storage, but model loading and project operations may feel slower.
Users with large model libraries may choose a secondary SSD or external high-speed drive for AI data where supported.
Local AI can place sustained load on CPUs, GPUs, and mobile processors.
On laptops and phones, this can increase battery consumption and device temperature.
For long desktop or laptop sessions, connecting the device to power is recommended.
On mobile, the operating system may reduce performance if the device becomes hot. Smaller models are generally better for long sessions because they place less pressure on memory, battery, and thermals.
Before installing a large local model, users should consider three things: available storage, available memory, and intended workload.
A model that fits on disk may still be too large for practical inference. A device with insufficient RAM may fail to load the model or may become extremely slow.
OpenMindAI's model compatibility and hardware-aware features are designed to help, but choosing a model appropriate for the device will always produce a better experience.
For most desktop users, 16 GB RAM, a modern 6-core processor, an SSD, and at least 20 GB of available storage provide a good starting point for OpenMindAI.
For developers, researchers, vision users, and future Code Agent workflows, 32 GB RAM and a supported dedicated GPU with 8 GB or more VRAM provide a substantially better experience.
For mobile, 8 GB RAM and 10 GB or more free storage provide a good practical target, while 12 GB RAM or more is preferable for larger local models on supported Android devices.
The most important requirement is not having the most expensive hardware. It is choosing local models that match the hardware you already have.
OpenMindAI is designed to scale from lightweight local AI on everyday devices to deeper AI workflows on powerful workstations.
You can start with a small model, use CPU-only inference, and add more capable models or hardware acceleration later.
The application is built around flexibility rather than forcing every user into the same hardware configuration.
Windows · macOS · Linux · Android · iOS
Website: https://www.openmindai.org
Start with the latest published release, then choose where your models and local data should live.