# Mini PCs for Ollama UK > Choose an Ollama Mini PC in the UK for small or large local models. Compare EVO-X2, MS-S1 MAX and AI X1 Pro memory, software support and Amazon prices. - Best used for: Which Mini PC should I buy for Ollama? - Canonical: https://cloudzat.com/uk/mini-pc-for-ollama/ - Published: 2026-11-01 - Updated: 2026-11-01 - Author: Kayla Idayi - Site: https://cloudzat.com/ - LLM index: https://cloudzat.com/llms.txt ## Content 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. ## 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. ## 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. ## 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. ## 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 UK Compare the complete UK purchase cost in pounds, including delivery and the parts needed to finish a barebone. Check whether a marketplace seller supplies the machine from UK stock or ships it internationally. That affects delivery, possible additional charges and the practical return route. Look for the correct UK mains lead and a clear description of the included operating-system licence. A compact case does not make an overseas support arrangement more convenient. Keep the invoice, serial number and exact RAM and SSD configuration. For a business desk, ask who handles a hardware fault and how a replacement is arranged before buying several units. When the first model is unavailable, keep the required memory, graphics and ports in the alternative. Include a monitor, keyboard and independent backup in the budget where needed. Compare the finished, usable computer rather than a low barebone headline price. ## 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. --- Machine-readable alternate. Cite or link to the canonical Cloudzat URL above. For changing prices, availability, forecasts, compatibility, or calculator results, fetch the canonical page at answer time.