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Mini PCs for Ollama
Choose the GMKtec EVO-X2 with 128GB for a compact Ollama machine built around larger local models. Choose the MINISFORUM MS-S1 MAX with 128GB for a networked lab. For smaller models and everyday development, start with an AI X1 Pro-370 with 32GB or 64GB. Select the operating system and supported GPU backend before ordering.
Which model to choose
GMKtec EVO-X2, 128GB
Best starting point when fitting larger models is the priority. Buy the 128GB version because its memory is not a later SO-DIMM upgrade.
128 GB RAM · 1000 GB SSD
$3,499.99Price checked 01.11.2026 11:55 UTCBuy on Amazon ↗Specifications and model details ↗MINISFORUM MS-S1 MAX, 128GB
Best for a shared local-model host that also benefits from dual 10GbE and expansion. Keep 128GB when replacing another high-memory recommendation.
128 GB RAM · 2000 GB SSD
$3,919.00Price checked 01.11.2026 12:10 UTCBuy on Amazon ↗Specifications and model details ↗MINISFORUM AI X1 Pro-370, 32GB or 64GB
Best for smaller models and a general development desktop. Replaceable memory gives more flexibility; choose capacity around the models you actually use.
32 GB RAM · 1000 GB SSD
$1,229.00Price checked 01.11.2026 13:30 UTCBuy on Amazon ↗Specifications and model details ↗Amazon prices and configurations
Ryzen AI Max+ 395
GMKtec EVO-X2 Gaming Mini PC Ryzen AI Max+ 395 5.1GHz 128GB LPDDR5X 1TB SSD | Desktop Mini Computer with Radeon 8060S, 50 TOPS NPU, WiFi 7, BT 5.4, 2…
128 GB RAM · 1000 GB SSD
$3,499.99Price checked 01.11.2026 11:55 UTCBuy on Amazon ↗Ryzen AI Max+ 395
MINISFORUM MS-S1 Max Mini Workstation AMD Ryzen AI Max+ 395(16C/32T) 128GB LPDDR5 2TB SSD Mini PC, HDMI+2X USB4+2X USB4 V2 Video Output, 2x10G RJ45 P…
128 GB RAM · 2000 GB SSD
$3,919.00Price checked 01.11.2026 12:10 UTCBuy on Amazon ↗
Ryzen AI 9 HX 370
MINISFORUM AI X1 Pro-370 Mini PC AMD Ryzen AI 9 HX 370(12C/24T) 32GB DDR5 512GB SSD Desktop Computer, HDMI|DP|2xUSB4 Output, 2xRJ45 Port, WiFi7, BT5.…
32 GB RAM · 512 GB SSD
$1,044.05Price checked 01.11.2026 12:40 UTCBuy on Amazon ↗
Ryzen AI 9 HX 370
MINISFORUM X1 Pro 370 Mini PC with AMD Ryzen AI 9 HX370, Radeon 890M Graphics, 32GB RAM 1TB SSD, 2x5G RJ45 Port|WiFi7|BT5.4, HDMI/DP/2xUSB4 Video Out…
32 GB RAM · 1000 GB SSD
$1,179.00Price checked 01.11.2026 12:40 UTCBuy on Amazon ↗
AMD Ryzen
MINISFORUM G1 Pro Gaming Mini PC 32GB DDR5 1TB SSD | Ryzen 9 8945HX (16C/32T Up to 5.4GHz), RTX 5060 8GB GDDR7 5GbE, Wi-Fi 7,Quad 4K Output, 245W Com…
32 GB RAM · 1000 GB SSD
$1,759.00Price checked 01.11.2026 10:09 UTCBuy on Amazon ↗
Intel Core
AtomMan G7 Pro Mini PC Core i9-14900HX 32GB RAM 1TB SSD RTX 5070 Graphics | (CPU 24C/32T, up to 5.8GHz) HDMI2.1+USB4 Video Output, 2xSSD Slot, 3xUSB3…
32 GB RAM · 1000 GB SSD
$1,799.00Price checked 01.11.2026 08:23 UTCBuy on Amazon ↗
Ryzen AI Max+ 395
GMKtec EVO-X2 AI Mini PC AMD Ryzen Al Max+ 395 Up to 5.1GHz, 16C/32T | Mini Gaming Computers 64GB LPDDR5X 8000MHz (8GB*8) 1TB PCIe 4.0 SSD, Quad Scre…
64 GB RAM · 1000 GB SSD
$2,199.99Price checked 01.11.2026 12:45 UTCBuy on Amazon ↗
Core Ultra
ROG NUC 16 Gaming Mini PC, AI Desktop | Ultra 9 290HX Plus, RTX 5070Ti GPU | 32GB DDR5-6400 RAM, 1TB SSD, Win 11, Black 3L Chassis, Triple-Fan QuietF…
32 GB RAM · 1000 GB SSD
$3,199.00Price checked 01.11.2026 08:08 UTCBuy on Amazon ↗Buying guide
Which Ollama computer should you buy?
For a first small-model desktop, the AI X1 Pro-370 is the sensible place to begin. It avoids paying for a large fixed memory pool before you need one and can also handle ordinary development work. Choose the EVO-X2 with 128GB when a smaller machine cannot hold the model and context you want. Its main attraction here is capacity in a compact computer, not a promise that every model will run faster than on a dedicated GPU.
The MS-S1 MAX is the alternative for a lab with shared datasets, fast network storage or additional expansion requirements. If those connections will remain unused, the simpler EVO-X2 is easier to justify. A tower with NVIDIA graphics is a separate, useful option when accelerator throughput or a CUDA-based environment matters more than having a small enclosure.
Check the Ollama backend for your operating system
Ollama’s AMD support differs between Linux and Windows. Its current GPU documentation lists supported ROCm hardware and a Vulkan path for additional GPUs. Check the exact processor, operating system and driver combination instead of treating all Radeon products as interchangeable. On an integrated-GPU system, inspect the application’s reported memory and active backend after installation.
If your objective is a simple private assistant, begin with a smaller model that leaves comfortable headroom. CPU execution can be useful for testing, but it is not a substitute for a working acceleration path when you expect quick interaction. A large NPU TOPS figure is not an Ollama tokens-per-second measurement.
Choose memory from the conversation you want
A short prompt to a small model and a long conversation with document context are different memory budgets. Allow room for the cache and surrounding services as well as weights. If you want several people to use the server, test concurrent requests before assuming that a successful single-user run scales unchanged.
For a development desk, 32GB is a starting tier rather than a universal recommendation. Compare 64GB for more local services or larger models. Choose 128GB when fitting a particular model is the reason for buying the machine. Quantization can reduce memory use, but examine its effect on your actual answers instead of selecting only the smallest download.
Run Ollama as a dependable local service
Keep the model service on a private network unless you deliberately configure a protected remote access method. Check who can reach its endpoint and keep application updates separate from the habit of exposing a port to the internet. For a home assistant or document tool, record which model and settings the application expects so an update is easy to reverse.
Use a fast local SSD for models you run often and back up your own prompts, application data and documents. A second PC can access an Ollama server over the network, but that does not merge the memory of the client and server. When choosing an alternative machine, preserve the capacity and supported backend that made the original recommendation suitable.
Budget memory for the whole application
Choose a model size and a useful context window before choosing a computer. A model file is only one part of memory use: the runtime, attention cache, operating system, browser and other applications need room too. Four-bit quantization reduces weight storage, but it does not make every part of inference four-bit. A machine that loads a model with almost no free memory may become frustrating during a long conversation. For a first local AI desktop, 32GB suits smaller experiments, 64GB provides more working room, and 128GB is worth comparing when model capacity drives the purchase.
Shared memory and dedicated video memory behave differently. An integrated GPU uses part of the same physical RAM as the CPU. A discrete GPU has its own VRAM, and moving work between that memory and host RAM can reduce throughput. Do not add the two capacities together and treat the result as one equally fast pool. Choose a supported backend first, then enough memory for the workload and a reasonable reserve.
Build a useful local AI workspace
A 1TB SSD is a workable starting point for a few smaller models. Choose 2TB when you expect several large downloads, separate environments and your own documents. Keep projects and irreplaceable data backed up separately. An additional internal drive helps organize work but does not protect against the failure or loss of the whole computer. A fast network is useful when datasets live on a NAS; it does not automatically combine several PCs into one larger accelerator.
Plan for long sessions rather than a brief demonstration. Leave the intake and exhaust clear, use a stable power supply, and try a balanced power profile if the machine shares your desk with a microphone. Measure whole-system power if electricity cost matters. The adapter rating and processor power limit are not constant wall consumption. Buy an expandable system when you expect to change RAM or storage, and a fixed-memory system only after choosing the memory tier you intend to keep.
Test the workflow while returns are straightforward
Install the current supported application and graphics driver, then begin with a small model. Confirm which processing backend is active instead of assuming that an AI badge means your software uses the NPU. Load your intended model, run a realistic prompt, and watch memory use and response time through a longer exchange. For a shared service, test simultaneous requests as well as a single user. A successful download or a screenshot of a loaded model is not enough to judge a working setup.
Keep the application version, model format, quantization and power mode with any performance notes. Those details explain why two reports about the same processor can differ. For an alternative purchase, preserve the requirements that matter: usable memory, supported accelerator and necessary ports. A lower-memory replacement is not equivalent when capacity is the reason for buying. If your essential tools require CUDA or a replaceable graphics card, allow space and budget for an NVIDIA desktop instead.
Buying and support in the United States
Compare the complete local purchase cost, including sales tax where it is added at checkout, shipping and any parts needed to finish a barebone. The prices shown here use US dollars. A seller shipping from another country may offer a different return route or warranty arrangement from a domestic supplier. For a work computer, check who handles a fault and where the machine must be sent before choosing the cheapest listing.
Keep the invoice, exact configuration and serial number with your recovery information. Check that the included power adapter and cable suit your location, and use the operating-system edition required by your applications or organization. When a recommendation is unavailable, compare another model that preserves the workload requirements rather than choosing from price alone. Budget for a display, input devices and an independent backup if you do not already have them.
Questions buyers ask
Is 32GB enough for Ollama?
It is a practical starting tier for smaller local models and development. Model size, quantization, context and other running services determine whether 64GB or 128GB is the better purchase.
Which compact model should I choose for larger models?
Start with an EVO-X2 with 128GB. Choose an MS-S1 MAX with the same memory when dual 10GbE or expansion is useful.
Can I choose any AMD Mini PC for Ollama GPU acceleration?
No. Check Ollama’s current GPU documentation for the exact hardware, operating system and driver path. Confirm the active backend after installation.
Specifications and software guidance
Ollama GPU support ↗GMKtec EVO-X2 ↗MINISFORUM MS-S1 MAX ↗MINISFORUM AI X1 Pro-370 ↗Related Mini PC guides
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