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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTo find a local AI model that will run well on your computer, check five things together: operating system and processor architecture, available RAM and GPU memory, the model’s weight-file size and format, the context and workload you need, and free disk space. No single model size fits every PC or Mac. Vendor requirements are a useful first filter, not a promise that a particular model will load quickly or feel responsive.
Start with the computer you have
Before choosing a model, record the machine’s operating system and processor architecture, installed and currently available RAM, GPU and dedicated video memory (VRAM), and free space on the drive where models will be stored. On Apple Silicon, note unified memory; it is shared across the system rather than being a separate pool of dedicated VRAM.
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Available capacity matters more than the installed-memory number alone. The operating system and open applications use memory too, and the model’s context length and number of simultaneous requests change its demands. Ollama says memory needs scale with context length and parallel requests: a larger context or more concurrent work requires more headroom.
Check that the runtime supports your system
Model compatibility starts with the software that loads it. LM Studio documents llama.cpp support on Mac, Windows, and Linux, plus MLX support on Apple Silicon. Its documentation names Qwen, Mistral, Gemma, and gpt-oss among model-family examples; that list is not a ranking or a guarantee that every model variant works on every platform. Check the runtime’s current system and model-format support before downloading.
#1 Best Overall
- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
| Platform | LM Studio guidance | What to check |
|---|---|---|
| macOS on Apple Silicon | Supports M1, M2, M3, and M4; requires macOS 14.0 or newer. Recommends 16GB or more RAM. LM Studio says an 8GB Mac may still work with smaller models and modest context. | These are LM Studio-specific requirements and recommendations, not rules for every local-AI runtime. Confirm your macOS version and the chosen model’s demands. |
| macOS on Intel | Intel Macs are currently listed as unsupported by LM Studio. | Check another runtime’s requirements if you have an Intel Mac; do not assume LM Studio support. |
| Windows | Supports x64 and ARM systems, including Snapdragon X Elite. x64 requires AVX2. LM Studio recommends at least 16GB RAM and 4GB dedicated VRAM. | Verify the processor instruction-set requirement for x64 and whether the GPU has dedicated VRAM. The recommendations do not guarantee that a chosen model will run at a useful speed. |
| Linux | Supports x64 and ARM64; LM Studio distributes an AppImage and lists Ubuntu 20.04 or newer. Its requirements page says Ubuntu versions newer than 22 are not well tested. | Check the current requirements for your distribution and architecture before installing. |
LM Studio’s figures are platform guidance for that application, not universal hardware thresholds. Its current system requirements should be checked before installation.
Compare the model file with the full memory budget
A downloadable weight file gives a useful sense of a model’s size, but its file size is not an exact RAM or VRAM requirement. Loading allocates memory for weights and other parameters, as LM Studio explains in its model-loading guide. Runtime, model format, context, concurrent requests, and memory already in use all affect whether a model fits. Do not treat a simple file-size-to-memory conversion as a guarantee.
Rank #2
- Built for Local AI Development: AMD Ryzen AI Halo is designed for local AI development and inference, featuring 128GB unified memory and support for up to 200B parameter models to build and run intensive AI workloads locally.
- 128GB Unified Memory: Features 128GB LPDDR5x unified memory at 8000 MT/s with 256 GB/s memory bandwidth, providing a shared memory pool across the CPU, GPU, and NPU to support larger AI models.
- AMD Ryzen AI Max+ 395 Processor: Features 16 cores, 32 threads, and Zen 5 architecture, paired with AMD Radeon 8060S integrated graphics featuring 40 RDNA 3.5 compute units and an AMD XDNA 2 NPU with up to 50 TOPS.
- Linux AI Developer Platform: Purpose-built for Linux-based AI development with full AMD ROCm software support and preloaded tools, models, and workflows optimized for local AI development.
- Compact, Connected Design: Includes a 2TB M.2 SSD, 10GbE LAN, Wi-Fi 7, Bluetooth 5.4, USB-C connectivity, and HDMI 2.1b.
- Find the specific model variant and weight format you intend to use, not just the family name.
- Compare its downloadable weight-file size with memory available after accounting for the operating system and ordinary applications.
- Leave room for loading parameters, context, and any other work you expect to run at the same time.
- If capacity is tight, try a smaller model or reduce context and concurrency before concluding that local inference is impossible.
For Ollama, larger contexts and more parallel requests increase memory use. Start with one request and a modest context, then raise either only if memory use and responsiveness remain acceptable. Ollama’s FAQ also describes K/V cache quantization when Flash Attention is enabled: q8_0 uses approximately half the memory of f16 with very small precision loss, while q4_0 uses approximately one quarter with small-to-medium precision loss that may be more noticeable at higher context sizes. These figures describe Ollama’s cache quantization, not model-weight quantization generally; the quality effect depends on the model and task.
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Budget disk space separately
Memory fit and disk fit are different checks. A model must be stored on disk as well as loaded into memory. Ollama’s Windows documentation warns that model files may require tens to hundreds of GB, depending on what you download; this is a broad storage warning, not a minimum requirement for every user. Check the free space on the target drive and avoid downloading models you do not plan to try. See Ollama’s Windows documentation for its storage guidance.
Rank #3
- EVOLUTION RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
Choose for the task, then test on your machine
First decide what you want to do—general chat, coding, document questions, or another task—and select a model intended for that use. System requirements alone cannot tell you which model will give the best answers or acceptable response speed. The cited runtime documentation does not provide a controlled, cross-computer benchmark or guarantee of interactive performance.
- Identify the workload. Decide what the model needs to do and whether you expect long prompts, long conversations, or multiple requests at once.
- Check the machine. Note the OS and architecture, available RAM, GPU and dedicated VRAM (or Apple Silicon unified memory), and free disk space.
- Confirm software and format support. Check the runtime’s current platform requirements and that it supports the model’s weight format.
- Choose a modest starting point. Prefer a smaller model and modest context if memory is limited; begin with one request.
- Try the real workload. Use the model for the task you actually care about and judge both response speed and answer quality on your own system.
- Increase demands gradually. Increase model size, context, or concurrency one at a time, watching for memory pressure and unacceptable responsiveness.
This test is necessary because platform support and memory guidance can narrow the options, but they cannot predict your experience with a particular model, runtime, task, and set of settings.
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