Free tools Windows power users keep installed
One-click scans. No signup required.
Some links on this page are affiliate links: if you buy through them we may earn a commission, at no extra cost to you.
For most people, Ollama is the simplest way to run an AI model locally when you want a command line, scripts, or a localhost service. LM Studio is the better starting point if you want a graphical interface for downloading models, chatting, and starting a local server. Both work across Windows, macOS, and Linux, but your computer matters more than the brand of software: Apple Silicon is the smoothest Mac path, Windows users must verify GPU drivers and available VRAM, and Linux users should plan for more driver configuration. Local AI can keep prompts on your own machine after models are downloaded, but model files are large and performance depends on RAM, unified memory, GPU memory, and storage speed.
Which local AI app should you choose?
Choose Ollama for an automation-first setup and LM Studio for a desktop-first setup. Ollama installs as a native application on Windows, provides the ollama command in a terminal, and exposes a local service at http://localhost:11434. LM Studio gives you visual model discovery, downloads, chat sessions, and local serving controls.
As an Amazon Associate I earn from qualifying purchases.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
| Decision factor | Ollama | LM Studio |
|---|---|---|
| Primary interface | Command line and background service | Graphical desktop application |
| Best fit | Scripts, development tools, Docker, and repeatable services | Trying models, managing downloads, and interactive chat |
| Local serving | Local API at http://localhost:11434 |
Built-in local server with an OpenAI-like interface |
| Windows baseline | Windows 10 version 22H2 or newer | Windows x64 or ARM; x64 requires AVX2 |
| macOS support | Use a build appropriate for your Mac hardware | Apple Silicon M1 through M4 on macOS 14 or newer; Intel Macs are unsupported |
| Linux support | GPU paths documented for NVIDIA, AMD ROCm/HIP, and Vulkan | x64 and ARM64 AppImage; Ubuntu 20.04 or newer is listed |
You can install both and use the one that matches the task. Running two model servers at once, however, divides memory and can make both feel slower, so stop one before loading a large model in the other.
What hardware do you need to run AI locally?
There is no single minimum that works for every model. A small model may run on a modern CPU, while a larger model can exhaust system RAM or GPU memory before it produces a response. LM Studio recommends at least 16GB of RAM on supported Windows and macOS systems, and at least 4GB of dedicated VRAM on Windows. Treat those figures as a practical starting point rather than a guarantee of speed.
#1 Best Overall
- Spacious Design: Measuring 21.1" wide and 14.1" deep, our lap desk comfortably fits most laptops up to 15.6". Extra room for accessories ensures convenience.
- Enhanced Functionality: Packed with handy features, including a 5x9" precision tracking mouse pad and a built-in phone slot for seamless work or video calls. Plus, enjoy ergonomic support with the integrated cushioned wrist rest.
- Cool Comfort: Enjoy a stable surface with our lap desk's dual bolster cushion, designed for comfort and airflow, keeping your lap cool during extended use.
- Durable Surface: Work with confidence on our lap desk's solid surface, featuring a sleek black carbon color, ensuring optimal air circulation to prevent your laptop from overheating.
- On-the-Go Convenience: With an integrated handle and lightweight design (2.8 lbs), our lap desk is portable for travel or moving around the house, offering flexibility in any space.
- System RAM: holds the model when it is run on the CPU and supplies working space even when some layers are offloaded to a GPU. More RAM gives you room for larger models and longer prompts.
- GPU or unified memory: a discrete GPU can accelerate inference when the correct driver and backend are available. Apple Silicon uses shared unified memory, so the memory used by macOS and other applications is not available to the model.
- CPU features: LM Studio’s Windows x64 build requires AVX2. Older processors without AVX2 cannot use that build even if they have sufficient RAM.
- Storage: the application is only part of the footprint. Ollama says its binary needs at least 4GB of disk space, while downloaded model collections can occupy tens or hundreds of gigabytes.
- Cooling and power: sustained local inference can keep a CPU or GPU busy. A laptop may reduce clock speed when hot or running on battery, so a plugged-in, well-ventilated setup is more consistent.
How much RAM and storage should you plan for?
Use 16GB of RAM as the sensible baseline for a general-purpose desktop. An 8GB machine can still be useful for small models and short prompts, but you will have less headroom for the operating system and multitasking. If you intend to experiment with several models, reserve substantially more storage than the size of the first download; model files accumulate quickly and are not automatically interchangeable between every application.
Keep models on a fast internal drive when possible. A USB-C external SSD is a practical expansion option if your internal disk is tight, but leave free space for temporary files, updates, and the operating system. Do not place active model files on a slow flash drive or a network share unless you have tested that arrangement; load times and responsiveness can suffer.
How do you run AI locally on Windows?
On Windows, start by checking the operating-system version, processor architecture, RAM, available disk space, and graphics driver. Ollama is the straightforward choice for a terminal or local-service workflow; LM Studio is easier if you want to see model metadata and manage downloads visually.
Windows setup with Ollama
- Confirm that Windows 10 version 22H2 or newer is installed. Apply pending Windows updates before troubleshooting model performance.
- Install the native Windows application from the official Ollama distribution. After installation, open Command Prompt, PowerShell, or another terminal and enter
ollamato verify that the command is available. - Use the Ollama command-line interface to download and start a model that fits your available memory. Begin with a smaller instruct model rather than the largest option in a catalog.
- Check that the local service is reachable at
http://localhost:11434. Applications on the same computer can use that address instead of sending prompts to a cloud service. - If you need repeatable automation, keep the Ollama service running in the background and point your script or development tool at the local endpoint. Test with a short prompt before building a longer workflow.
Windows setup with LM Studio
- Choose the installer matching your processor: Windows x64 requires AVX2, while a Windows ARM build is available for supported ARM hardware.
- Install LM Studio and open its model browser. Download one model whose memory requirement leaves room for Windows and your other applications.
- Start a chat session and watch memory use while sending a representative prompt. If the application reports that the model cannot be loaded, select a smaller model or close memory-heavy programs.
- Open the local-server controls when an application needs an API. Keep the server bound to the local machine unless you deliberately understand the security implications of exposing it to other devices.
Windows GPU checks
NVIDIA acceleration depends on a compatible vendor driver. AMD systems may use ROCm/HIP or Vulkan, depending on the hardware and installed driver. Install or update the driver before changing model settings; otherwise a model may fall back to the CPU and appear unusually slow. A current driver does not make every model fit: dedicated VRAM still limits how much can be loaded on the GPU.
Can you run an LLM on a Mac without sending data to the cloud?
Yes. After a model is downloaded, Ollama or LM Studio can generate responses on an Apple Silicon Mac without sending the prompt to a hosted model. LM Studio currently supports M1, M2, M3, and M4 Macs running macOS 14 or newer; Intel Macs are not supported by LM Studio.
Rank #2
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
Apple Silicon setup
- Check the Mac model in Apple menu → About This Mac and verify the macOS version in System Settings → General → Software Update.
- Install Ollama or LM Studio for Apple Silicon. LM Studio can run GGUF models through llama.cpp and also supports MLX on Apple Silicon.
- Start with a model sized for the Mac’s unified memory. On an 8GB machine, leave generous space for macOS; a 16GB or larger Mac gives a more comfortable general-purpose margin.
- Run a short chat and observe whether the system begins swapping to disk. If it does, close other applications or move to a smaller model before judging the software.
- For an application integration, enable the local server and use the address and settings shown by the app. Keep model downloads and server access local when privacy is the priority.
Unified memory is shared by the model, macOS, graphics, and every open application. A model that technically fits may still be unpleasant if the system has to reclaim memory constantly, so size for sustained use rather than the smallest possible launch.
Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWindows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallWhat is the easiest local AI setup for Linux?
LM Studio is the easier desktop route on Linux because it is distributed as an AppImage for x64 and ARM64 systems, with Ubuntu 20.04 or newer listed as a requirement. Ollama is the better fit for a service, Docker, or automation workflow, but Linux users should expect to validate GPU drivers and runtimes themselves.
Linux setup with LM Studio
- Confirm that your distribution meets the listed baseline and that your CPU architecture matches the x64 or ARM64 build.
- Download the AppImage, make it executable using your distribution’s file manager or shell, and launch it. Keep the file on a local disk rather than a removable drive.
- Download a small GGUF model first. LM Studio uses llama.cpp for GGUF and can expose a local server after the model loads.
- Test a short prompt, then check memory consumption and GPU utilization with your distribution’s monitoring tools. Increase model size only after the first model responds reliably.
Linux setup with Ollama
- Install the Linux package and verify that the
ollamacommand is available in a new terminal session. - Choose a model that fits system RAM or the combined RAM and GPU capacity. Keep the model directory on a fast disk with ample free space.
- Start the local service and connect your scripts or containers to
http://localhost:11434. - For GPU acceleration, install the appropriate NVIDIA driver or the AMD ROCm/HIP or Vulkan components documented for your hardware. Re-test after each driver change so you know which change affected performance.
How do Ollama and LM Studio handle model formats and acceleration?
LM Studio’s documented desktop path runs GGUF models through llama.cpp and can use MLX on Apple Silicon. Ollama presents a model-oriented command-line workflow and selects an acceleration path appropriate to the operating system and available drivers. Neither application can overcome a model that exceeds available memory.
| Platform | Acceleration considerations | What to verify first |
|---|---|---|
| Windows with NVIDIA | Use a compatible NVIDIA driver; available VRAM limits GPU-resident layers | Driver version, dedicated VRAM, and whether the application detects the GPU |
| Windows with AMD | ROCm/HIP or Vulkan may be available depending on hardware and driver support | Supported runtime and driver, then actual device detection |
| Apple Silicon | Metal-capable builds and MLX support can use unified memory; LM Studio also supports llama.cpp | Chip generation, macOS 14 or newer for LM Studio, and free unified memory |
| Linux with NVIDIA | Vendor driver configuration is a prerequisite for reliable acceleration | Driver installation and a successful GPU detection test |
| Linux with AMD | ROCm/HIP or Vulkan support varies by card and software stack | Card compatibility, runtime installation, and permissions |
| CPU-only systems | Models can run without a discrete GPU but generally need smaller models and patience | RAM capacity, AVX2 support where required, and thermal limits |
There is no universal tokens-per-second figure for these setups. Speed changes with model architecture, quantization, prompt length, context size, thermal state, and the exact driver. Measure your own machine with a repeatable prompt instead of treating a result from another computer as a promise.
How should you choose a model size?
Start with the smallest model that can answer your real task, then move upward if quality is insufficient. This approach exposes configuration problems quickly and avoids filling a disk with models you cannot load.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Rank #3
- Note: Not suitable for MacBooks released after 2023 or devices with a protruding front camera; Not applicable to full-screen or notch-style tempered glass screen protectors; Do not use on the rear camera of the phone.
- 💻 Why Do You Need a Webcam Cover Slide? — Safeguard your privacy by covering your webcam with our reliable webcam cover when not in use. Don't let anyone secretly watch you. Stay protected!
- ✅ Thin & Stylish — Enhance your laptop's functionality and aesthetics with our 0.027" ultra-thin webcam covers. Seamlessly close your laptop while adding a touch of sophistication.
- ✅ Fits Most Devices — Compatible with laptops, phones, tablets, desktops! Keep your privacy intact on Ap/ple, Mac/Book, iPh/one, iP/ad, H/P, L/novo, De/ll, Ac/er, As/us, Sa/msung devices.
- ✅ 365 Days Protection — Our upgraded 3.0 adhesive ensures a strong hold that won't damage your equipment. Experience reliable, long-term privacy protection day in and day out.
- For an 8GB computer: favor small models, short contexts, and one application at a time. Expect to close browsers and editors when loading a model.
- For 16GB: you have the baseline recommended by LM Studio for Windows and macOS, with reasonable room for small and medium local workloads if the model fits.
- For more than 16GB: extra memory helps with larger models, longer contexts, and multitasking, but it does not guarantee that a model will run quickly.
- For a discrete GPU: compare model requirements with dedicated VRAM, not just total system RAM. If the model spills into system memory, responsiveness may change substantially.
- For Apple Silicon: count all unified memory as shared. A 16GB Mac does not provide 16GB exclusively to the model.
Keep a simple inventory: model name, file size, format, date downloaded, and the machine on which it was tested. Delete duplicate or obsolete files when storage becomes constrained, and retain at least one known-good small model for troubleshooting.
How do you connect local AI to an application?
Both tools can serve models locally. Ollama’s service is addressed at http://localhost:11434; LM Studio provides local-server controls and an OpenAI-like interface. In either case, the application and the model server must run on the same machine unless you intentionally configure network access.
- Load a model and confirm that a manual chat produces a response.
- Enable the local server in the application’s settings or server panel.
- Copy the host address and port shown by the application into your development tool’s provider settings.
- Run a minimal request with a short prompt, then add your normal system instructions and context.
- Stop the server when you are finished if other users or processes share the computer. Leaving a local endpoint running can consume memory even when no chat window is open.
For privacy-sensitive work, inspect the application and integration settings for telemetry, update checks, and extensions. Local generation keeps inference on your computer, but downloading a model, checking for updates, or using a cloud-connected plug-in still involves network traffic.
What are the most common local-AI problems?
The model will not load
The usual cause is insufficient RAM, VRAM, or unified memory. Close other applications, select a smaller model, and confirm that the model file completed downloading. If the error persists, use the known-good small model in your inventory to separate a hardware limit from a damaged or incompatible file.
Responses are extremely slow
Check whether the expected GPU is detected and whether its driver is current. On Windows, verify the NVIDIA, AMD ROCm/HIP, or Vulkan path that matches your hardware; on Linux, revisit the vendor driver and runtime setup. A CPU fallback is usable for testing but may not suit long sessions.
Rank #4
- Anti-Slip Surface - Transform your laptop into a mobile workstation with the AboveTEK portable laptop lap desk. The anti-slip surface provides a strong grip for laptops up to 15.6 inches(Diagonal), while the double rubber strip on the bottom ensures a stable display or typing experience on your lap, couch, or bed.
- Retractable Mouse Pad - Retractable laptop mouse pad extends on both directions for the left/right handed with elevation along the edges for stopping mouse from falling off. The size of laptop tray is 14" X 9.7" and the size of mouse pad is 7.4" X 6.1".
- Effective Heat Shield - The effective heat shield made of sturdy and thick material protects your laptop from overheating. Prioritizes your comfort and safety, an ideal lap pad or board for working anywhere.
- EASY to Carry and Store - With an ergonomic and simplistic design, the lap desk is portable to store in a backpack. Only 15" in size, 2.2 lb of weight and with slim 0.6 inch thickness, it is ready to be easily carried around.
- Widely Applicable - The smooth platform accommodates laptops and tablets up to 15.6 inches(Diagonal), making it a versatile accessory and one of the best gifts for mom, dad, students and professionals. Perfect for use as a laptop bed tray or tablet holder anywhere at home, library, or park.
The application says the operating system is unsupported
Check the exact baseline: Ollama’s Windows documentation requires Windows 10 22H2 or newer; LM Studio lists macOS 14 or newer for Apple Silicon and Ubuntu 20.04 or newer for Linux. LM Studio does not currently support Intel Macs, and its Windows x64 build requires AVX2.
The disk fills unexpectedly
Model libraries can reach tens or hundreds of gigabytes, separate from the application’s own installation. Review downloaded models, remove unused variants, and move the library to a spacious internal drive or fast external SSD before downloading another large file.
A local API works in one program but not another
Confirm that the server is running, copy the host and port exactly as displayed, and check whether the client expects Ollama’s API style or an OpenAI-like interface. Start with a short prompt and no optional plug-ins; add features one at a time so a client-side error is not confused with a model failure.
Which setup is best for your workflow?
| Your priority | Recommended starting point | Reason |
|---|---|---|
| Chat visually and browse models | LM Studio | The GUI makes downloads, model selection, and local serving visible. |
| Use scripts or development tools | Ollama | The command-line workflow and localhost service fit automation. |
| Apple Silicon Mac | LM Studio or Ollama | Both can run locally; LM Studio adds GGUF and MLX options, while memory remains the deciding constraint. |
| Windows with a supported GPU | Either, after driver verification | Acceleration depends on the driver and backend more than on the interface. |
| Linux server or Docker host | Ollama | A service-oriented workflow is easier to integrate without a desktop session. |
| Linux desktop experimentation | LM Studio | The AppImage and graphical controls reduce setup friction. |
If you are undecided, install one small model in LM Studio to validate that your hardware can run local inference, then install Ollama when you need an API or automation. The reverse also works: Ollama gives a minimal service quickly, while LM Studio can provide a visual way to compare models later.
What should you check before downloading a large model?
- Confirm the operating-system and architecture requirements for the application.
- Record free RAM, dedicated VRAM or unified memory, and free disk space.
- Verify the GPU driver or runtime before assuming the model is too slow.
- Choose a smaller model first and keep one known-good test prompt.
- Plan where the model library will live; a nearly full system disk can cause failures unrelated to model quality.
- Decide whether you need a GUI, a command line, a local API, a background service, or Docker integration.
Frequently Asked Questions
Can local AI work completely offline?
Inference can run without an internet connection once the application and model files are already on the computer. You will still need connectivity for initial downloads, updates, or any plug-in that deliberately calls an online service.
Best Value
- Spacious Design: Measuring 21.1" wide and 12" deep, our lap desk comfortably fits most laptops up to 15.6". Extra room for accessories ensures convenience.
- Enhanced Functionality: Packed with handy features, including a 5x9" precision tracking mouse pad and a built-in phone slot for seamless work or video calls. Plus, enjoy laptop support with the integrated device ledge.
- Cool Comfort: Enjoy a stable surface with our lap desk's dual bolster cushion, designed for comfort and airflow, keeping your lap cool during extended use.
- Durable Surface: Work with confidence on our lap desk's solid surface, featuring a blush pink color, ensuring optimal air circulation to prevent your laptop from overheating.
- On-the-Go Convenience: With an integrated handle and lightweight design (2.14 lbs), our lap desk is portable for travel or moving around the house, offering flexibility in any space.
Is 8GB of RAM enough for local AI?
It is enough for experimenting with smaller models and short prompts, but the operating system and other applications consume part of that memory. Expect to close programs or move to a machine with 16GB or more for a smoother general-purpose setup.
Which operating system is fastest for local models?
There is no cross-platform speed winner supported by the available evidence. The result depends on the exact CPU, GPU, driver, model, and thermal conditions, so compare runs on your own hardware.
Do I need a dedicated GPU to run a local model?
No. CPU-only inference is possible, but smaller models and shorter workloads are more practical. A supported GPU can improve responsiveness when its driver and runtime are correctly installed and the model fits its memory.
Can one local server be used by several applications?
Yes, applications on the same computer can connect to a running local endpoint, provided they support that server’s interface. Test each client with a short request and avoid running multiple large model servers simultaneously because they compete for memory.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
