Best Mini PCs for Local AI and Local LLMs in 2026
Running a local LLM comes down to one spec: memory. Here are the mini PCs that hold the biggest models, ranked for local AI.
The best mini PC for local AI is the one with enough memory to hold the model you want to run — because on a small machine, memory is the wall you hit first. Whether the box uses Apple unified memory, an AMD Ryzen AI Max+ 395 with 128GB of shared LPDDR5X, or an NVIDIA GB10 chip, the rule is the same: the model has to fit before speed even matters.
This guide ranks mini PCs for one job — running local large language models — in 2026. We sort them by the memory that decides which models fit, then by ecosystem and value. If you want the AMD boxes compared head to head, see our dedicated Ryzen AI mini PC guide; to size a specific model to a machine before buying, use the local AI hardware calculator.
Trying to run a private LLM on a mini PC without buying the wrong box? At Layer3Labs, we help small businesses size the model, pick the machine, and stand it up on-prem so your data never leaves the office.
Book a ConsultationThe Best best mini PC for local LLMs, Ranked
The EVO-X2 is the cheapest route to a full 128GB of unified memory, which is exactly what large local models need. Built on the AMD Ryzen AI Max+ 395, it runs 70B-class models comfortably and can even load very large mixture-of-experts models, while still working as a normal x86 desktop.
View on Amazon →- AMD Ryzen AI Max+ 395 (16 Zen 5 cores)
- Radeon 8060S iGPU (40 CUs, RDNA 3.5)
- 128GB LPDDR5X-8000 unified memory
- Up to 96GB assignable to the GPU
- 128GB unified memory at the lowest price
- Runs very large quantized models
- Doubles as a full desktop PC
- AMD ROCm tooling is less mature than CUDA
- The biggest models load but run slowly
The DGX Spark is a purpose-built local-AI machine using the NVIDIA GB10 Grace Blackwell superchip, with full CUDA support and 128GB of unified memory. It handles models up to around 200 billion parameters and comes preloaded with the NVIDIA AI stack, making it the smoothest box for serious development — at a premium price.
View on Amazon →- NVIDIA GB10 Grace Blackwell superchip
- 20-core Arm CPU plus Blackwell GPU
- 128GB LPDDR5X unified memory
- Full CUDA and NVIDIA AI software stack
- CUDA — the widest AI tooling support
- Handles models up to ~200B parameters
- Tiny and preloaded for AI work
- Much pricier than the AMD boxes
- An Arm AI appliance, not a general office PC
The Framework Desktop packs the same Ryzen AI Max+ 395 into a repairable, standard-form-factor machine for people who like to service and reconfigure their hardware. It reaches up to 128GB of unified memory and offers the same local-AI power as the value boxes with a cleaner, upgrade-friendly ethos.
View on Amazon →- AMD Ryzen AI Max+ 395
- Configurable, repairable design
- Up to 128GB LPDDR5X unified memory
- Standard ports and form factor
- Repairable and service-friendly
- Same Strix Halo power as the EVO-X2
- Clean, well-supported build
- Costs more once fully configured
- Memory is soldered — buy enough upfront
The GTR9 Pro pairs the 128GB Ryzen AI Max+ 395 platform with dual 10GbE networking and vapor-chamber cooling, so it stays quiet under sustained inference. For a home-lab or always-on AI server role, the networking and thermals set it apart from the value boxes.
View on Amazon →- AMD Ryzen AI Max+ 395
- 128GB LPDDR5X-8000 unified memory
- Dual 10GbE networking
- Vapor-chamber cooling, quiet under load
- Best networking for a home lab
- Quiet and well cooled
- Full 128GB unified memory
- ROCm tooling less mature than CUDA
- Premium over the value boxes
The Mac mini shares memory between CPU and GPU, so it runs local models well through Metal and MLX in a tiny, silent, efficient box. It caps at 64GB of unified memory, which suits mid-size models rather than the very largest — for 128GB on Apple Silicon you step up to a Mac Studio.
View on Amazon →- Apple M4 Pro with unified memory
- Up to 64GB unified (shared CPU and GPU)
- Runs local models via Metal and MLX
- Very efficient and silent
- Excellent performance per watt
- Great for mid-size models
- Tiny and completely silent
- Caps at 64GB — step up to a Mac Studio for 128GB
- macOS, not Windows, for the office
The MS-01 is the rare mini PC with a PCIe slot that accepts a half-height GPU, so you can add real CUDA VRAM instead of relying on shared memory. For anyone who already owns a GPU or wants NVIDIA tooling in a small box, it is the flexible, expandable choice.
View on Amazon →- High-core Intel workstation CPU
- PCIe slot for a half-height GPU
- Add real GPU VRAM for local AI
- Dual 10GbE, multiple NVMe slots
- Put a real CUDA GPU inside
- Very expandable for the size
- 10GbE for a server role
- GPU choice limited by half-height size
- Louder and warmer under load
Mini PCs for local AI at a glance
| Machine | Best for | Memory for models | Ecosystem |
|---|---|---|---|
| GMKtec EVO-X2 | Big models, best value | 128GB unified | ROCm / Vulkan |
| NVIDIA DGX Spark | AI development | 128GB unified | CUDA |
| Framework Desktop | Configurable / repairable | Up to 128GB unified | ROCm / Vulkan |
| Beelink GTR9 Pro | Home lab / networking | 128GB unified | ROCm / Vulkan |
| Mac mini M4 Pro | Small, silent, mid models | Up to 64GB unified | Metal / MLX |
| Minisforum MS-01 | Add your own GPU | RAM + GPU VRAM | CUDA (via GPU) |
How to Choose a Mini PC for Local AI
Choosing a mini PC for local AI starts and ends with memory. A local model has to fit in RAM or, better, in memory the GPU can reach — so the size of the model you want decides the machine, not the other way around. Pick the model first, then buy the box that holds it with room to spare.
After memory, two things matter: the ecosystem and the value. NVIDIA means CUDA, which almost every AI tool supports out of the box. AMD Ryzen AI Max+ 395 and Apple Silicon reach big memory for far less money, but their tooling needs a little more setup. Size the model with the hardware calculator, then match memory, ecosystem, and budget in that order.
- Memory is the wall — The model must fit; buy more unified memory or VRAM than you think you need.
- Unified memory is the cheap path to big models — Apple and Ryzen AI Max+ share memory with the GPU, so large models fit without a costly discrete card.
- CUDA is the smoothest tooling — If you value plug-and-play AI software, an NVIDIA machine has the widest support.
- Quantization buys headroom — A 4-bit model needs far less memory than full precision, letting a smaller box punch above its weight.
How Big a Model Can Each Mini PC Run?
How big a model a mini PC can run depends almost entirely on its memory. As a rough guide for 2026: a 128GB unified-memory box runs 70B-class models comfortably and can even load very large mixture-of-experts models, though the biggest slow down. A 64GB machine handles up to about 30B smoothly and 70B at tighter quantization.
Speed matters too, not just fit. A model that loads but runs at a few tokens per second is fine for batch jobs and painful for chat. The 128GB Strix Halo boxes, for example, can load a 235B model but generate only around ten tokens per second — usable for some tasks, slow for others. Always check both: does it fit, and is it fast enough for how you will use it?
Rough tokens-per-second bands by model size give a sharper picture than fit alone. These are approximate, 4-bit-quantized figures from public benchmarks and vendor testing, meant as a planning range rather than a guarantee (actual throughput shifts with backend, context length, and prompt size). A Ryzen AI Max+ 395 box typically runs a 7B model around 35-45 tok/s, a 13B model around 20-30 tok/s, a 30B model around 12-18 tok/s, and a 70B model around 5-9 tok/s. The NVIDIA DGX Spark's CUDA stack tends to run faster at the same sizes: roughly 50-70 tok/s at 7B, 35-45 tok/s at 13B, 20-28 tok/s at 30B, and 10-15 tok/s at 70B. A Mac mini M4 Pro on Metal/MLX lands close to the Ryzen boxes through 30B, but its 64GB memory cap means a 70B model only fits at the tightest quantization and runs in the low single digits of tokens per second, if it loads at all.
- 128GB unified — 70B comfortably; very large MoE models load but run slower.
- 64GB unified — up to ~30B smoothly; 70B at tighter quantization.
- 32GB or less — small-to-mid models (7B to 14B) and quantized 30B.
- Always check tokens per second — Fitting a model is not the same as running it fast enough to use.
- 7B models — ~35-70 tok/s across the Ryzen AI Max+ 395, DGX Spark, and Mac mini tiers; the fastest, most comfortable size on any of them.
- 13B models — ~20-45 tok/s on the same three tiers; still fluid for chat and coding-assistant use.
- 30B models — ~12-28 tok/s; noticeably slower than 13B but usable.
- 70B models — ~5-15 tok/s on Ryzen AI Max+ 395 and DGX Spark; the Mac mini's 64GB cap makes 70B impractical.
Mac Mini vs Ryzen AI Max+ vs DGX Spark for Local LLMs
For running local LLMs, the choice comes down to Apple unified memory, an AMD Ryzen AI Max+ 395 box, or the NVIDIA DGX Spark. The DGX Spark wins on tooling and raw AI compute — full CUDA and up to 200B-parameter models — but costs the most. The Ryzen AI Max+ 395 boxes hit 128GB unified memory for far less, and stay useful as normal x86 PCs. A Mac mini is the smallest and most efficient, but caps at 64GB.
- NVIDIA DGX Spark — Best tooling (CUDA) and biggest models; highest price; an Arm AI appliance, not a general PC.
- Ryzen AI Max+ 395 (EVO-X2, Framework, GTR9) — 128GB unified for much less; doubles as a full x86 desktop; ROCm tooling is improving.
- Apple Mac mini M4 Pro — Smallest, silent, most efficient; great to 64GB; macOS and no path to 128GB.
Local AI Agents Ask for 24GB of Discrete NVIDIA VRAM
Unified memory and discrete VRAM are not interchangeable for local agent software. The tiers above size a model against pooled memory that the CPU and GPU share. [Perplexity](https://www.perplexity.ai) Portable Computer, the local agent stack it announced with [NVIDIA](https://www.nvidia.com) on 25 August 2026, asks for a discrete NVIDIA card instead. The reported floor is 24GB of VRAM, an RTX 3090 or newer, and 32GB is cited as the official recommendation. It runs on Linux today, and Perplexity has stated Windows support for September 2026.
[Apple](https://www.apple.com) silicon is unsupported by Portable Computer whatever the memory size. A 64GB Mac mini M4 Pro clears the model-size bar in the tiers above and still cannot run the software. If agent software is the reason you are buying, read its GPU requirement on the [Perplexity Portable Computer page](https://www.perplexity.ai/hub/products/portable-computer) before you use the memory tiers on this page to pick a box.
Power Draw and Electricity Cost Running a Mini PC 24/7
A 128GB Ryzen AI Max+ 395 mini PC draws roughly 65-140W under sustained local-model inference, and far less at idle (often under 20W between requests). That is a fraction of what a full AI workstation tower pulls: a tower built around a discrete GPU can draw 500-1000W under sustained load, mostly from the GPU itself. These are typical ranges from vendor spec sheets and public reviews, not a guarantee for any specific unit, since actual draw shifts with the model, quantization, and how hard the box is pushed.
That gap matters most for a machine meant to stay on. A 100W-class mini PC left running around the clock costs a few dollars a month at typical U.S. residential electricity rates, while a 700W-class tower run the same way costs several times more and needs real cooling and circuit headroom besides. For an always-on local AI box (an agent, a small inference server, a home-lab model), the Ryzen AI Max+ 395 tier is cheap to run 24/7 in a way a full tower is not. See our [best AI workstations](/gear/best-ai-workstations) roundup for when the extra power draw is worth it: bigger models, faster generation, and room for a discrete GPU upgrade.
- Ryzen AI Max+ 395 (128GB) — roughly 65-140W under sustained inference, well under 20W idle.
- Full AI workstation tower — roughly 500-1000W under sustained load with a discrete GPU.
- Cheap to run 24/7 — a mini PC left on around the clock costs a small fraction of what a tower costs in electricity.
Warranty and Support: What to Expect from GMKtec- and Beelink-class Brands
GMKtec, Beelink, and Minisforum typically back these boxes with a 1-2 year limited warranty handled through the reseller or Amazon, not a direct manufacturer support line. That is standard for the category, not a red flag specific to any one brand — expect a return-and-replace process through your point of purchase rather than a dedicated business support desk.
That is a real trade-off against workstation-class business machines like the HP Z2 Mini G1a or an ASUS-class business line, which add on-site service options, longer warranties, and a direct enterprise support contract. A single unit or small home-lab purchase rarely needs that; a fleet rollout across a business usually does, and is worth pricing against the workstation option before you buy in volume.
Frequently Asked Questions
- The best mini PC for local LLMs in 2026 is a 128GB unified-memory machine — an AMD Ryzen AI Max+ 395 box like the GMKtec EVO-X2 for value, or the NVIDIA DGX Spark for the widest tooling. Both hold large models that used to need a discrete GPU. Choose the AMD box for price and general-purpose use, the DGX Spark for CUDA development. For a cheaper option if you don't need the full 128GB, see our [best AI mini PCs](/gear/best-ai-mini-pcs) roundup for value picks like the GEEKOM A8 and Beelink SER8.
- You need enough memory to hold the whole model plus its context. As a rough 2026 guide: 16GB runs small 7B models, 32GB handles a quantized 30B, 64GB fits 70B at tight quantization, and 128GB runs 70B comfortably with room for larger models. Unified memory counts, since the GPU can use it. Size the exact model with the hardware calculator first.
- Yes, a mini PC can run a 70B model if it has enough memory. A 128GB unified-memory box runs 70B-class models comfortably; a 64GB machine can run them at tighter quantization and lower speed. The limit is memory and tokens per second, not the mini PC form factor itself.
- For local AI, a Ryzen AI Max+ 395 box is better if you want the most memory for the money — 128GB unified versus the Mac mini cap of 64GB. A Mac mini is better if you want the smallest, quietest, most efficient machine and run mid-size models. Match the choice to the model size you need and your operating system.
- Yes, but image and video generation lean much harder on raw GPU compute than chat-model inference, so memory capacity alone matters less here. The NVIDIA DGX Spark is the smoothest option since most diffusion tooling targets CUDA first. The Minisforum MS-01 is the only box on this list where you can add a real discrete GPU for full speed. The AMD Ryzen AI Max+ 395 boxes and the Mac mini can run diffusion models, but expect noticeably slower generation than on a desktop with a dedicated GPU.
- Generally no — a single mini PC runs desktop AI apps like Ollama and LM Studio as single-user tools, so one box serves one person at a time. See our best AI mini PCs for business FAQ for what a small team needs instead to share one model across the office.
- No, not the memory — on Ryzen AI Max+ 395 boxes like the EVO-X2, Framework Desktop, and GTR9 Pro, the unified memory is soldered at the factory, so the capacity you buy is the capacity you keep. See our [Ryzen AI mini PCs](/gear/ryzen-ai-mini-pcs) FAQ on memory for what that means and why you need to size the model before you order.
- It depends on your volume and how sensitive the data is, not on the mini PC category alone. See our [best AI mini PCs](/gear/best-ai-mini-pcs) FAQ on mini PC vs renting cloud GPU compute for the full cost and data-sensitivity trade-off.
- Yes, if the box has enough memory to keep the coding model resident and a fast NVMe SSD for context and codebase retrieval — the picks above cover both. See our [best mini PC for AI agents and AI coding](/gear/best-ai-mini-pcs#mini-pc-for-ai-agents-and-coding) section for the specifics on memory, SSD speed, and always-on power draw for agent workloads.
Want to run local AI on the right hardware?
Layer3 Labs helps small and mid-size businesses stand up private, on-device AI — from sizing the model to picking the mini PC and wiring it into your workflow, so your data stays in the office.
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