Best AI mini PCs for running local LLMs in 2026
Running a large language model on your own hardware instead of sending every prompt to a cloud API has become a realistic option for small teams, mostly because of one change: mini PCs built around AMD’s Ryzen AI Max+ and Intel’s Core Ultra chips now offer enough unified memory to load genuinely large models without a separate graphics card. That matters if you handle client data you don’t want leaving the building, or if API costs for constant LLM use are starting to add up. We looked at three mini PCs that span the range from a serious 70B-model machine down to a cheaper option for smaller 7B to 14B models. This is one corner of a much wider category, and our hardware buying guide for mini PCs, storage, networking and displays covers the rest of what a small business needs beyond just AI inference hardware.
Key takeaways
- Best for large models The GMKtec EVO-X2 uses AMD’s Ryzen AI Max+ 395 with 128GB of unified LPDDR5X, enough to run heavily quantized 70B-class models entirely in memory.
- Best balanced pick The Beelink GTi15 Ultra pairs Intel’s Core Ultra 9 285H with 96GB of DDR5, a solid middle ground for 30B-class models with strong single-core performance for everything else.
- Best budget entry point The GMKtec EVO-X1 uses the older but still capable Ryzen AI 9 HX 370, a sensible starting point for 7B to 14B models before committing to pricier hardware.
Our picks at a glance
GMKtec EVO-X2: best for large local models
The EVO-X2 is built around AMD's Ryzen AI Max+ 395, the chip most of 2026's local-LLM enthusiast community has settled on because of how its unified memory architecture works. Rather than splitting a fixed chunk of VRAM away from system RAM the way a discrete GPU does, the Max+ 395 lets you allocate a large share of its 128GB of LPDDR5X directly to the integrated GPU. In practice that means this mini PC can load quantized versions of 70B-parameter models entirely in memory, something that would otherwise need a multi-GPU workstation costing several times as much.
That capability comes at a real price. This is not an impulse buy for the 128GB/2TB configuration, per UK pricing tracked by Most Wanted Gamers' review, and GMKtec's direct pricing has moved around with stock and regional promotions, so it's worth checking the current figure before ordering. Inference speed on large models is also noticeably slower than what you'd get from a proper discrete GPU with the model fitting in dedicated VRAM. This machine makes big models possible on a desk, not fast in the way a data centre GPU is fast.
The honest trade-off here is patience versus capability. If your use case can tolerate a 70B model responding in tens of tokens per second rather than the near-instant response of a hosted API, and keeping that data entirely off someone else's servers matters to you, the EVO-X2 is one of the few consumer devices that makes it possible at all.
Beelink GTi15 Ultra: best balanced pick
The GTi15 Ultra takes a different route to a similar goal. It runs Intel's Core Ultra 9 285H, paired in this configuration with 96GB of DDR5 and a 2TB SSD, and on Amazon UK at the time of writing, it's priced below the EVO-X2 while still comfortably handling 30B-class models and smaller. Intel's NPU also picks up some inference workloads that support it, which takes pressure off the CPU and integrated GPU for certain tasks.
Where this machine has an edge over the AMD-based EVO-X2 is everyday responsiveness for non-LLM work. The Core Ultra 9 285H's single-core performance is strong, so if this mini PC is also going to run your actual business software, a CRM, spreadsheets, video calls, alongside occasional local LLM use, it won't feel like a machine built for one narrow job. It also runs noticeably cooler under sustained load than the higher-wattage AMD chip in the EVO-X2, which matters if it's sitting on a desk rather than in a ventilated server room.
The ceiling is lower, though. 96GB of DDR5 system memory, even with a good chunk allocated to the iGPU, won't comfortably run the largest 70B-class quantized models the way the EVO-X2's LPDDR5X and Strix Halo architecture can. For most small businesses experimenting with local LLMs for internal documentation search, drafting, or code assistance, that ceiling is unlikely to matter.
GMKtec EVO-X1: best budget entry point
Before spending two grand on a machine built for 70B models, it's worth asking whether you actually need one. A lot of genuinely useful local LLM work, drafting support responses, summarising documents, running a coding assistant, happens comfortably on 7B to 14B parameter models, and that's where the older Ryzen AI 9 HX 370 in the EVO-X1 still holds up. This configuration ships with 32GB of LPDDR5X and a 1TB SSD, enough headroom to run smaller quantized models with room to spare for the rest of Windows or Linux running alongside it.
Pricing is the one area to be careful with here. GMKtec's own listed price for this configuration is set in the US market, and UK Amazon stock for this specific configuration was intermittent at the time of writing, so treat that US listing as a reference point rather than a confirmed UK price, and check current availability and local pricing before ordering.
The 80 TOPS NPU on the HX 370 is a genuine asset for anything built to use it specifically, but most local LLM tooling in 2026 still leans on the GPU or CPU rather than the NPU, so don't buy this chip expecting NPU acceleration to be the main event. Think of the EVO-X1 as the machine to learn on, and to prove out whether local LLM use actually fits your workflow, before deciding whether the EVO-X2's extra memory is worth the jump.
|
Best for large models
GMKtec EVO-X2 (128GB)
|
Best balanced pick
Beelink GTi15 Ultra (96GB)
|
Best budget entry point
GMKtec EVO-X1 (32GB)
|
|
|---|---|---|---|
| Processor | AMD Ryzen AI Max+ 395 | Intel Core Ultra 9 285H | AMD Ryzen AI 9 HX 370 |
| Memory | 128GB LPDDR5X (unified) | 96GB DDR5 | 32GB LPDDR5X |
| Storage | 2TB SSD | 2TB SSD | 1TB SSD |
| Realistic model ceiling | Quantized 70B-class | Quantized 30B-class | Quantized 7B-14B-class |
| NPU included | Yes | Yes | Yes |
| Check Price | Check Price | Check Price |
How to choose an AI mini PC for local LLMs
Model size in memory is the single biggest factor in what you can run locally. Unified memory architectures like AMD’s Ryzen AI Max+ series let you allocate a large share of system RAM to the GPU, which is why they’ve become the default choice for large local models.
Local LLM inference keeps the CPU and GPU under load for longer stretches than typical desktop tasks, so a mini PC that throttles under sustained load will slow down mid-conversation.
Most local LLM tooling, including Ollama, LM Studio and llama.cpp, runs on Windows and Linux, but driver support for a given chip’s GPU acceleration varies by platform and changes over time.
Large model files, sometimes tens of gigabytes each, need to load from disk into memory. A slow SSD adds real waiting time every time you switch models.
Frequently asked questions
How much RAM do I need to run a 70B parameter model locally?
A 70B model quantized to around 4-bit precision typically needs roughly 40 to 50GB of memory just to load, plus overhead for context and the operating system. A 128GB unified memory machine like the GMKtec EVO-X2 gives comfortable headroom; anything under 64GB will struggle.
What is unified memory and why does it matter for local LLMs?
Unified memory means the CPU and integrated GPU share the same pool of RAM rather than the GPU being limited to a small, separate block of dedicated VRAM. Chips like AMD’s Ryzen AI Max+ series can allocate a large portion of that shared pool to the GPU, which is what makes running very large models possible without a discrete graphics card.
Do I need a discrete GPU to run local LLMs well?
Not necessarily. A discrete GPU with enough VRAM is generally faster, but the VRAM ceiling on consumer graphics cards is often lower than what these unified-memory mini PCs offer, which is why they’ve become popular specifically for larger models rather than for raw speed.
Can these mini PCs run Ollama or LM Studio?
Yes, all three run Windows and can run Ollama, LM Studio or llama.cpp directly. Performance and GPU acceleration support vary by chip and by how current your graphics drivers are, so check the specific tool’s documentation for compatibility with your chip before buying.
What does quantization actually mean for model quality?
Quantization reduces the precision of a model’s numbers to shrink its memory footprint, which is what makes running large models on consumer hardware possible at all. Lower-bit quantization, such as 4-bit, saves the most memory but can noticeably affect output quality on some tasks, so it’s a real trade-off rather than a free lunch.
Is a gaming laptop a better choice than a mini PC for this?
A gaming laptop with a high-VRAM discrete GPU can be faster for models that fit within its VRAM, but most laptop GPUs top out well below the unified memory capacity of chips like the Ryzen AI Max+ 395. For genuinely large local models, these mini PCs currently offer more usable memory per pound than most laptops in the same price range.
Which AI mini PC should you buy?
- The GMKtec EVO-X2's 128GB of unified memory is currently one of the few realistic ways to run 70B-class models on a desk.
- The Beelink GTi15 Ultra balances strong everyday performance with enough memory for most 30B-class local models.
- The GMKtec EVO-X1 is a sensible, cheaper way to test whether local LLM use fits your workflow before committing to pricier hardware.
Buy based on the models you actually intend to run, not the biggest number on the spec sheet. If you genuinely need 70B-class models running locally and can accept slower inference, the GMKtec EVO-X2 is one of the few consumer devices that makes it possible. If your work fits comfortably in 30B and under, the Beelink GTi15 Ultra is the better everyday machine. If you’re not sure yet whether local LLM use fits your workflow, start with the cheaper EVO-X1 and upgrade once you know.
