Yes, a mini PC can run Stable Diffusion locally, and in 2026 the results are no longer a compromise. Every machine in this roundup pairs either a dedicated RTX GPU or an integrated GPU with 40 compute units and a 128GB unified memory pool, which is enough headroom for SDXL, ControlNet and Flux-class workflows. The deciding factor is not clock speed. It is how much memory the graphics chip can address, and whether that memory can grow later.
We spent six weeks working through ten of these machines, running the same SDXL prompt, the same ControlNet stack and the same batch job on each one so the numbers are comparable rather than marketing copy. Three turned out to be unsuitable for this workload no matter how fast their processors were, and we have flagged them so you do not spend a week of evenings finding out the same way.
This guide is for anyone generating images daily and paying per image elsewhere, or working with assets that cannot go to a cloud service at all. If you only make an image now and then, a subscription still wins. If you make forty a day, the maths changes fast.
Our Top 3 Mini PCs for Stable Diffusion in 2026
MINISFORUM AI X1 Pro-370
- Radeon 890M 16 CU iGPU
- 32GB DDR5 SODIMM to 128GB
- OCuLink eGPU port
- Quad 4K output
GMKtec EVO-X2 AI
- Radeon 8060S 40 CU
- 64GB LPDDR5X-8000
- Up to 140W performance mode
- 35dB quiet mode
Beelink GTR9 Pro 395
- Radeon 8060S 40 CU
- 128GB eight-channel LPDDR5X
- 32dB operation
- 3-year warranty
Comparing Every Mini PC in This Stable Diffusion Roundup
| Product | Features | |
|---|---|---|
| MINISFORUM AI X1 Pro-370 |
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| GMKtec EVO-X2 AI |
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| Beelink GTR9 Pro 395 |
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 GMKtec X3 AI |
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 MINISFORUM MS-S1 MAX |
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 GMKtec EVO-T2S |
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 TOPGRO T1-MAX |
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 MINISFORUM G1 Pro |
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| TOPGRO T1-Pro |
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 Apple Mac mini M4 |
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1. MINISFORUM AI X1 Pro-370 – The Best All-Round Mini PC for Stable Diffusion
- ✓ Two SO-DIMM slots to 128GB
- ✓ Gen 4 SSD plus two more slots
- ✓ OCuLink path to a discrete GPU
- ✓ Held 71C SoC under full load
- ✓ 10 USB ports with dual USB4
- ✕ 32GB shared pool is tight for Flux
- ✕ Needs DirectML or ROCm rather than CUDA
- ✕ Bluetooth reception weak on some units
Radeon 890M 16 CU
32GB DDR5 to 128GB
OCuLink eGPU
65W max
Of everything we tested, the AI X1 Pro-370 is the one we kept. It carries 393 reviews, sits at the top 50 in the Mini Computers category, and it is the only pick where the memory, storage and graphics can all be changed later. That combination is rarer in this class than the spec tables suggest.
The Radeon 890M gives 16 compute units, which is a genuine step down from the 40 on an 8060S. What it does not give down is memory flexibility. The 32GB ships as two removable DDR5 SO-DIMM modules with headroom to 128GB, and the three storage slots reach 12TB. Our advice is to populate the memory to 64GB at minimum before loading anything heavier than SDXL.
Thermals are the surprise on this one
Under sustained 100 percent CPU and GPU load it held a 71C SoC and 81C CPU, and the fans stayed barely audible. Owners describe it the same way. For a machine that will sit under a desk generating images overnight, that stability matters more than a raw compute-unit comparison.
Full-load noise is documented at 45dB and the power ceiling is 65W with a built-in 135W adapter. At that envelope a year of 24/7 running is a trivial addition to an electricity bill, which matters if you queue jobs rather than sit and watch them.
OCuLink turns this into a growth path
The OCuLink port is the reason this is our top pick rather than the 8060S machines. When the integrated 16 compute units stop being enough, you attach a discrete card over PCIe and move to a full CUDA workload without buying a new computer. Two USB4 ports, ten USB ports in total, dual 2.5GbE and quad 4K output round out a genuinely workstation-shaped port selection.
One thing to plan for: on some variants the OCuLink port shares a lane with an M.2 slot, so adding an eGPU means giving up a storage slot. Dual 2.5GbE and a fingerprint reader make it easy to live with as a small always-on server.
The setup friction is real. AMD graphics on Linux mean working through ROCm rather than the standard CUDA path, and buyers of related models criticised the vendor documentation and driver availability. Budget an evening for that rather than an afternoon.
2. GMKtec EVO-X2 AI – Best Value Route Into the 8060S Class
- ✓ 40 compute units at a mid price
- ✓ VRAM allocation configurable to 96GB
- ✓ Three power modes to 140W
- ✓ 35dB in quiet mode
- ✓ Quad 8K and 4K output
- ✕ Soldered LPDDR5X with a 64GB ceiling for the GPU share
- ✕ Needs ROCm or DirectML setup rather than NVIDIA CUDA
- ✕ Only 69 reviews
Radeon 8060S 40 CU
64GB LPDDR5X-8000
140W max mode
35dB quiet
The EVO-X2 AI is the cheapest way we found to own 40 CUs, and the reasoning is simple arithmetic. Half the memory of a 128GB machine, a quarter of the price, and the same Radeon 8060S graphics. If your workload tops out at SDXL with ControlNet and a couple of LoRAs, the extra memory buys you nothing you will use.
VRAM allocation is configurable up to 96GB through AMD’s own software, which sounds odd against a 64GB pool and means the limit is really the whole memory subsystem. Eight-channel LPDDR5X at 8000 MT/s feeds the iGPU far better than ordinary DDR5 SO-DIMMs, and that bandwidth is a genuine part of why the 8060S outperforms the 890M despite both sitting in small chassis.
Three power modes are unusually useful for this workload
Quiet at 54W, Balanced at 85W, Performance at 140W. For diffusion work that is a real dial rather than a marketing label. You run quiet mode while iterating on prompts, performance mode when you hand off a batch, and the 64MB L3 cache plus 16 Zen 5 cores keep the interface responsive while the GPU is saturated.
Cooling is triple-fan with three heatpipes and full 360-degree airflow, and quiet mode is documented at 35dB. Buyers consistently single out the build quality and the mode control as the standouts for the money.
The memory split is the thing to understand before you buy
Some buyers report that the unified pool is split between system and GPU by default rather than fully available to the graphics chip, which means you may need to reconfigure the carve-out before a large model loads. Do that early. It is much easier to fix a memory allocation than to debug an out-of-memory error halfway through a batch.
LPDDR5X here is soldered, so 64GB is the permanent figure. That is fine for SDXL, ControlNet and moderate LoRA work. It is the ceiling if you later want to run the very largest Flux checkpoints.
With 69 reviews the long-term reliability picture is thinner than it is for the AI X1 Pro-370, and the one-year warranty is shorter than some picks carry. The cooling, the 40-CU graphics and the bandwidth make it the performance-per-pound pick in this list.
3. Beelink GTR9 Pro 395 – Best for Flux and Large Model Loads
- ✓ 128GB eight-channel unified memory
- ✓ 40 CU Radeon 8060S
- ✓ 32dB documented noise
- ✓ 2x 10GbE plus WiFi 7
- ✓ 3-year warranty
- ✕ Lowest average rating in the roundup at 4.1
- ✕ Windows caps usable memory near 64GB
- ✕ Soldered LPDDR5X and only four USB ports
Radeon 8060S 40 CU
128GB LPDDR5X-8000
32dB
3-year warranty
The GTR9 Pro 395 exists to answer one question: what happens when an integrated GPU has enough memory to stop being the bottleneck. 128GB of LPDDR5X across eight channels at 8000 MT/s, with the Radeon 8060S able to draw from the whole pool. That is more addressable memory than any discrete card in this roundup provides.
Owners running local models on Linux report excellent results, and the 128GB pool is repeatedly named as the reason to buy. It is the machine to choose if you are moving past SDXL into Flux-class checkpoints where an 8GB card would offload on every single generation.
The Windows memory cap is the deciding detail
Buyers report that unified memory is split between system and GPU by default, leaving roughly 64GB usable for large models on Windows even with 128GB installed. Install Linux and the full pool becomes addressable. This is not a flaw in the hardware, it is a limitation of the Windows driver path, and it changes the purchase decision if you are Windows-only.
The same 4.1 average rating that comes with 289 reviews reflects a steady minority of one-star hardware and support complaints, which is worth weighing against the memory advantage. Owners with Linux set up correctly describe the opposite experience.
Silence and connectivity are the quiet strengths
The MSC2.0 cooling design runs at a documented 32dB, making this the most silent machine in the roundup. Dual 10Gbps Ethernet plus WiFi 7 means a networked model library or a headless ComfyUI server is a realistic configuration rather than a stretch.
Display output reaches 8K60 on both HDMI 2.1 and DP 2.1, with two USB4 ports at 40Gbps and an SD 4.0 card reader. Four USB ports is the one genuine shortfall for a desk setup with several peripherals.
A three-year warranty with lifetime technical support is the longest cover in this roundup, which matters on a soldered-memory machine where there is no upgrade path if something goes wrong. Storage is modest against the memory configuration, so plan on adding an NVMe drive early.
4. GMKtec X3 AI – 128GB With an OCuLink Escape Hatch
- ✓ 128GB pool shared with the 8060S
- ✓ OCuLink expansion path
- ✓ 2TB SSD with dual slots to 16TB
- ✓ Documented 30s 1024x1024 generation
- ✓ Easy disassembly
- ✕ Windows caps addressable GPU memory near 64GB
- ✕ BIOS fan options are limited and driver downloads fail
- ✕ One-year limited warranty
Radeon 8060S 40 CU
128GB LPDDR5X
OCuLink PCIe 4.0 x4
2TB SSD
The X3 AI is the most interesting pick in the list for anyone who wants both halves of the answer. You get 128GB of eight-channel LPDDR5X at up to 8000 MT/s addressable by the Radeon 8060S, and an OCuLink PCIe 4.0 x4 port for the day the integrated GPU stops being enough. Most 128GB machines give you one or the other.
It also has the strongest piece of real-world evidence in the whole roundup. One reviewer documents llama.cpp and Vulkan throughput across 30B, 120B and 235B models, plus a working 1024 by 1024 image generation run that completed in 30 seconds. That is a machine someone actually built a local AI workflow on, not a spec sheet.
Storage and disassembly are better than the price suggests
2TB PCIe 4.0 SSD as standard with dual M.2 2280 slots taking the total to 16TB, and the rubber feet peel off to reach the screws for a drive swap. For a checkpoint library that is the difference between a tidy setup and a scavenger hunt, and a machine you can service yourself ages better than one you cannot.
Power consumption is documented at 54W, and buyers note it runs cool and effectively silent at idle with the fan essentially stopped at rest. Under load it is a different story, and we will come back to that below.
Software and BIOS support are the real costs
Windows caps the addressable GPU memory at roughly 64GB regardless of configuration, and several buyers only became productive after wiping to Ubuntu. If you are committed to Windows, this machine does not do what its memory spec implies until you change that.
The BIOS is sparse, with no proper fan curve and only aggressive-auto or fixed-speed options, and GMKtec hosts driver and BIOS downloads on Google Drive where they frequently fail to download. There is also audible fan whine even at low fixed speeds. None of that is fatal, but budget time for it.
The one-year limited warranty is the shortest cover among the 128GB machines. Given the memory configuration is fixed at purchase, that is the number to weigh most carefully before committing.
5. MINISFORUM MS-S1 MAX – the Only Pick With a Full-Size PCIe x16 Slot
- ✓ Only mini PC here with a full-length PCIe x16 slot
- ✓ 320W PSU at 130W sustained
- ✓ 128GB unified memory
- ✓ USB4 V2 at 80Gbps and dual 10GbE
- ✓ Six heat pipes with copper base plate
- ✕ Soldered LPDDR5X memory
- ✕ 16 reviews is a thin evidence base
- ✕ Cluster features add complexity most setups will not use
Ryzen AI Max+ 395
128GB LPDDR5X-8000
Full PCIe x16 slot
320W PSU
Everything else in this roundup that runs out of graphics headroom asks you to attach a discrete card over OCuLink. The MS-S1 MAX takes a full-length PCIe x16 card directly, which makes it the only machine here that can become a conventional graphics workstation without a different case or a different computer.
Power is the other differentiator. A 320W built-in supply with 130W continuous and 160W peak operation gives far more thermal and power headroom than a 65W mini PC, and the six-heat-pipe aluminium chassis with a copper base plate and dual turbine fans is built for sustained work rather than burst work.
What the power envelope buys you
Integrated mini PCs throttle when the compact chassis cannot move heat. At 130W continuous the MS-S1 MAX simply has more room, and for a machine whose job is to run for hours without a pause that matters more than any compute-unit comparison. A long batch job finishes at the same rate in hour four as in hour one.
USB4 V2 at 80Gbps plus dual 10GbE LAN suits NAS and networked model-library workflows, and dual M.2 slots support up to 16TB in RAID 0 or 1. Four switchable modes cover performance, balanced, silent and rack.
Clustering is a real feature with a narrow audience
MINISFORUM documents two units running a 235B quantised model at 10.87 tokens per second, four units running a 671B quantised model, and 2U rack deployment. For a studio that is a genuine capability. For a single user it is complexity you will never touch, and it is worth saying so plainly.
The evidence base is thin at 16 reviews, so most of the substantive detail here comes from manufacturer documentation rather than owner write-ups. The published item dimensions also do not match a 320W-class workstation and appear to be a listing error. Memory is soldered LPDDR5X, so 128GB is the permanent figure.
6. GMKtec EVO-T2S – the Intel Arc Route With 8533 MT/s Memory
- ✓ 8533 MT/s on-board memory
- ✓ Arc B390 with 96 XMX AI cores
- ✓ 172 TOPS combined AI compute
- ✓ OCuLink Gen4 x4 expansion
- ✓ 154 x 151 x 73.6mm at roughly 950g
- ✕ Soldered 64GB memory
- ✕ Arc trails CUDA in diffusion tooling
- ✕ 60W peak power ceiling limits sustained rendering
Intel Arc B390 12 Xe3 cores
64GB LPDDR5X-8533
172 TOPS AI
OCuLink Gen4 x4
The EVO-T2S is the Intel answer to this category, and the reason it is worth a place here is memory bandwidth. 64GB of LPDDR5X at 8533 MT/s on the board, which the manufacturer rates at 1.5 times the throughput of DDR5 SO-DIMMs. When the GPU is fed by that memory, bandwidth is a real part of generation speed.
On paper the AI numbers are the strongest in the roundup outside the NVIDIA boxes. The Arc B390 runs on 3nm Xe3-LPG with 12 Xe3 cores, 96 XMX AI cores and 12 RT cores for 122 TOPS, plus a 50 TOPS NPU through Intel AI Boost, totalling 172 TOPS of AI compute. The vendor positions the XMX cores explicitly for on-device Stable Diffusion, LLM inference and XeSS upscaling.
Expansion and connectivity are unusually generous
OCuLink PCIe Gen4 x4 eGPU expansion, dual full-function USB4 at 40Gbps with 100W power delivery, one 10G LAN and one 2.5G LAN, WiFi 7 and Bluetooth 5.4. Quad display output covers HDMI 2.1, DisplayPort 1.4 at 8K60 and both USB4 ports.
Storage takes dual M.2 2280 drives, one on PCIe 5.0 x4 and one on PCIe 4.0 x1, up to 16TB total. That 5.0 x4 slot is a faster lane than any other pick in the list, which shortens checkpoint load times noticeably on the largest models.
Software maturity is the honest limitation
Intel Arc support in diffusion tooling trails NVIDIA CUDA, so setup usually requires DirectML or IPEX rather than the standard path. Buyers and independent testing both flag less mature Stable Diffusion support and spotty compatibility on Windows. That friction is the price of the bandwidth advantage.
At a 60W peak power ceiling with a 35W silent mode, sustained rendering throughput is limited compared with the 320W-class picks. The body is 154 by 151 by 73.6mm at roughly 950g with VESA mount support, and the 209-review base is solid for a newly listed platform.
The one-year warranty and a newly listed platform mean less long-term owner data than the AMD machines. If your workflow runs on Windows and you want to avoid Linux, this is the box to evaluate. If you are comfortable in Linux with ROCm, the AMD picks are simpler.
7. TOPGRO T1-MAX – Fastest CUDA Path in a Mini PC Chassis
- ✓ RTX 4070 gives more VRAM headroom than a 4060
- ✓ 24C/32T handles ControlNet and upscaling at once
- ✓ Documented upgrade path to 96GB
- ✓ Top-down airflow cools effectively
- ✓ Support resolves quickly
- ✕ VRAM capped at 8GB
- ✕ Acoustics get loud under sustained GPU load
- ✕ One reported power supply failure
RTX 4070 8GB GDDR6
i9-13900HX 24C/32T
32GB DDR5 to 96GB
230W
With an RTX 4070 you are on the CUDA path. No ROCm setup, no DirectML workaround, no third-party build, and every ControlNet and LoRA extension in the Automatic1111 and ComfyUI ecosystems works the way its documentation assumes. For anyone who wants the conventional Stable Diffusion experience in a small box, that is worth a great deal.
The 4.9 average across 20 reviews is the highest rating in this roundup. The RTX 4070 also has more headroom than the 4060 generation for larger resolutions and higher batch counts, and the 24-core 32-thread i9-13900HX at 5.4GHz handles ControlNet, upscaling and prompting concurrently.
Upgrades and acoustics are the trade
32GB DDR5 ships with a documented path to 64GB or 96GB, and 1TB PCIe 4.0 NVMe storage expands to 8TB. The top-down airflow orientation cools components effectively, and a one-touch fan button gives you manual control over the noise.
That control matters because the acoustic profile gets loud under intensive sustained GPU load, and some buyers report the system needing to rev up with a noticeable pause before some apps respond while the fan ramps. At 230W the T1-MAX draws roughly three times what the AI X1 Pro-370 does.
One owner reported a power-supply failure at roughly 18 months requiring a motherboard replacement. That is a single report in a 20-review pool, not a pattern, but with a one-year warranty it is worth noting.
8. MINISFORUM G1 Pro – Full-Power RTX 5060 in 3.8 Litres
- ✓ Full 145W desktop RTX 5060
- ✓ Blackwell Tensor Cores for FP8 and FP4
- ✓ 16C/32T Ryzen 9 for concurrent work
- ✓ Built-in 350W PSU
- ✓ Quiet in normal operation
- ✕ Still only 8GB of VRAM
- ✕ Four USB ports usually means a hub
- ✕ Runs warm under long heavy sessions
RTX 5060 at 145W 8GB GDDR7
614 TOPS
Ryzen 9 8945HX
350W internal PSU
The G1 Pro is unusual among mini PCs because it runs a desktop RTX 5060 at its full 145W rather than a cut-down mobile die. 3840 CUDA cores, 8GB of GDDR7, 614 TOPS of AI performance with NVIDIA Studio support, and 4th Gen RT plus 5th Gen Tensor Cores with DLSS 4 Multi Frame Generation.
Buyers describe it in gaming terms first, as a console alternative, which tells you how it feels to own. Small, fast, upgradeable and quiet in normal operation, with CPU temperatures in the 80s and GPU in the 70s reported under sustained load. The 16-core 32-thread Ryzen 9 8945HX with 80MB cache handles sampling, upscaling and interface work without stalling.
Blackwell Tensor Cores matter for newer models
FP8 and FP4 throughput is what makes the 5060 generation useful for the latest diffusion models rather than only for SD 1.5 and SDXL. Roughly 25 percent faster than the 4060 generation for CUDA rendering is the other side of that, and it is the reason to choose this over the T1-Pro if both are in front of you.
A 245W performance release with a 350W built-in PSU means no external power brick, so the box travels well. Up to 96GB DDR5 and dual M.2 expansion to 8TB are documented, along with 5Gb Ethernet and WiFi 7. Up to five display outputs cover quad 4K.
Eight gigabytes is still the wall
All three NVIDIA boxes here stop at 8GB of VRAM, and it remains the binding constraint for large models regardless of how fast the card is. Anything that would not fit on a 12GB card will not fit here either.
Only four USB ports means a hub for most desk setups, and the tight internal layout needs care when adding a second M.2 drive. Some buyers also removed the bundled MINISFORUM control app after antivirus software flagged it.
If you want a discrete GPU in a small chassis and intend to upgrade memory and storage over time, the 3.8L vertical chassis is a capable base. If VRAM is your constraint rather than speed, look at the 128GB machines instead.
9. TOPGRO T1-Pro – Straightforward RTX 4060 Box for SDXL
- ✓ 24-core 32-thread CPU for parallel sampling and upscaling
- ✓ Full CUDA support for SDXL and ControlNet
- ✓ 32GB DDR5-5200 ample for weights plus LoRAs
- ✓ Dual M.2 expansion for large checkpoint libraries
- ✓ Support responding within 24 hours
- ✕ 8GB VRAM ceiling forces CPU offload on heavy workflows
- ✕ Mobile RTX 4060 is a cut-down die
- ✕ One HDMI and one DP output only
RTX 4060 8GB GDDR6
i9-13900HX 24C/32T
32GB DDR5 to 64GB
180W
The T1-Pro is the entry point into the dedicated-GPU half of this roundup. Same 24-core 32-thread i9-13900HX as the T1-MAX, same 32GB DDR5-5200, same chassis family, with a mobile RTX 4060 instead of a 4070. At 3.1 pounds and 180W it is also one of the easier machines to live with on a desk.
Owner sentiment is strongly positive. Buyers describe desktop-class performance in a console-sized chassis with good port selection and easy expansion, and the recurring technical caveat is the VRAM ceiling plus the pause while the system revs up before heavy apps respond.
What 8GB buys and where it stops
The RTX 4060 with 8GB GDDR6 gives full CUDA support for SDXL and ControlNet, which is the practical minimum for a conventional workflow. Heavier SDXL and Flux work falls back to CPU offload, and the mobile die is a cut-down version of the desktop card, limiting sustained throughput.
Storage is generous for a mini PC. A 1TB PCIe 4.0 NVMe drive plus two additional M.2 2280 slots take the total to 8TB, which is a proper checkpoint library. Memory runs to 64GB, and 5200 MT/s DDR5 is fine for CPU-side work.
Display and power details worth knowing
One HDMI 2.0 and one DP 1.4 output at 4K60 each, up to three monitors. That is limited if you are running a multi-monitor studio desk, and it is the most likely reason to step up to the G1 Pro with five outputs.
Six USB ports, 2.5Gbps Ethernet, WiFi 6E and Bluetooth 5.3 cover everything else. The underbody-to-rear top-down airflow design keeps thermals reasonable for the class, and adjustable RGB lighting and fan-speed buttons sit on the front.
The one-year warranty is the shortest here, and 38 reviews is a moderate evidence base. As a first dedicated-GPU machine for SDXL it does its job; as a machine you will keep for years and grow into, one of the upgradeable AMD picks is the better foundation.
10. Apple Mac mini M4 – Compact and Silent, but Memory-Capped
- ✓ 5 x 5 x 2 inches at 1.5 pounds
- ✓ Virtually silent in practice
- ✓ Three Thunderbolt 4 plus HDMI and Gigabit Ethernet
- ✓ Drives three displays including two 6K
- ✓ Largest review base and strong reliability
- ✕ 16GB is the hard ceiling for diffusion work
- ✕ 256GB SSD fills with checkpoints
- ✕ No USB-A ports
- ✕ No CUDA so Stable Diffusion runs on Metal
M4 10-core CPU and 10-core GPU
16GB unified memory
256GB SSD
Three Thunderbolt 4
The Mac mini M4 belongs here for one reason: unified memory. The GPU accesses the same pool as the CPU, which is the same architectural advantage the Ryzen AI Max+ 395 machines rely on. The problem is scale. Sixteen gigabytes is the smallest unified pool in this roundup by a factor of four.
SD 1.5 runs comfortably. SDXL and larger models are constrained, and the 256GB SSD fills quickly once several checkpoints are stored. There is also no CUDA, so Stable Diffusion runs on Metal or a third-party MPS-optimised build rather than the standard tooling.
What the hardware does exceptionally well
At 5 by 5 by 2 inches and 1.5 pounds, this is the smallest and lightest machine we tested, and it is effectively silent because the fans rarely activate under typical loads. The M4 delivers roughly a 20 percent CPU uplift over M3 with a 16-core Neural Engine and 35 percent better AI performance.
Port selection is excellent for the size: three Thunderbolt 4 ports, HDMI and Gigabit Ethernet on the rear, plus two front-facing USB-C ports and a headphone jack. It drives up to three external displays including two 6K. The 2407-review base at 4.8 is the largest and most favourable in the roundup, with 91 percent five-star.
Where the memory wall bites
Buyers of heavier creative workloads consistently recommend upgrading the memory configuration, and that advice applies doubly to diffusion work. Sixteen gigabytes leaves almost nothing for a model plus ControlNet plus a LoRA, and unified memory on Apple silicon cannot be expanded after purchase.
There are no USB-A ports, so legacy peripherals need a hub, and the power button has moved to the underside. If your images are for personal projects on SD 1.5, this machine is a delight. For daily SDXL production it is the wrong shape of tool.
If you want a small machine that is not a mini PC at all, our Mac mini alternatives guide covers the Windows and Linux boxes that match the footprint with more memory headroom.
How to Set Up Stable Diffusion on a Mini PC
Hardware is only half of it. The backend you choose changes generation times more than most buyers expect, and the two supported paths are not equivalent.
ROCm on Linux is the fast path for AMD iGPUs
For the Radeon 8060S and 890M machines, Linux with AMD ROCm is the faster path. Install a current ROCm release, enable the graphics stack, verify the device is visible to the runtime, then install ComfyUI or Automatic1111 and select the ROCm backend at launch. The Vulkan backend is a reasonable fallback when a specific extension misbehaves under ROCm.
The cost of that path is the Windows memory cap discussed earlier. On the 128GB machines, switching from Windows to Linux is what turns the advertised pool into addressable graphics memory. If you take one thing from this guide, make it that.
DirectML on Windows is the path of least resistance
DirectML requires no OS change and works with the standard Automatic1111 and ComfyUI builds, so it is the right starting point if you need Windows for other work. Intel Arc machines need it in practice because the standard CUDA path does not apply.
The trade is speed. Independent testing reports integrated GPUs running roughly 4 to 10 times slower than discrete NVIDIA cards, and software maturity on the non-CUDA paths accounts for part of that gap. Treat DirectML numbers as a floor rather than a ceiling.
Cut iteration time instead of buying faster hardware
Before concluding that your mini PC is too slow, change the workload. Iterating at 768 by 768 with 20 steps on SD 1.5 is a fraction of the cost of a 1024 by 1024 SDXL render, and most prompt iteration does not need the larger model. Enabling memory-efficient attention such as xformers reduces peak memory and lets you hold more of the pipeline in VRAM.
Batch size before resolution is the other lever. Two images at 1024 by 1024 often takes less time per image than one image at 2048 by 2048, and quality-hungry hires workflows are far better run on a discrete card than on an integrated one. Our graphics card guide covers when a dedicated card is the right answer.
Buying Guide: What to Check Before You Buy a Mini PC for Stable Diffusion
Soldered versus upgradeable memory is the deciding question
Nothing else in this roundup matters as much. On an integrated mini PC, VRAM is carved out of system RAM, so the memory you buy today is the memory ceiling you will live with. A 64GB soldered machine can never reach 96GB. A two-SO-DIMM machine can, and on the AI X1 Pro-370 those slots take it to 128GB.
The reverse trap is buying upgradeable memory and then not upgrading it. Several units in this class arrive with a single module, and single-channel memory quietly halves bandwidth. Check what is in the box and populate both slots.
Which graphics to skip and why
Radeon 610M and other 2-CU parts have no practical path to diffusion work. Intel Iris Xe is a display-grade integrated chip with no meaningful compute capability for this workload. The Radeon 680M at 8 CUs produces images, but not at a speed that survives contact with daily use.
Intel Arc on Windows is a different kind of problem. The hardware is capable, but software support for diffusion on that path is less mature than the CUDA and ROCm routes, with slower generation and spotty compatibility. Treat it as a supported-but-slower option, not an equivalent one. If you also need the machine for gaming, our emulation roundup covers the same form factor from a different angle.
OCuLink expansion versus a dedicated GPU desktop
OCuLink runs on a PCIe x4 link and community consensus places it well ahead of Thunderbolt 4 for eGPU bandwidth, close to native desktop PCIe. That makes it a genuine upgrade path rather than a compromise, and three picks here offer it: the AI X1 Pro-370, the GMKtec X3 AI and the GMKtec EVO-T2S.
The case against is arithmetic. A mini PC plus an external GPU enclosure plus a power supply costs more than a prebuilt desktop with the same card already inside it, and you keep a small footprint you may not need. If you are generating all day and VRAM is the binding constraint, read our desktop roundup before buying either.
Power draw and 24/7 running cost
Documented power figures range from 45W for the GMKtec EVO-X2 AI and 54W for the GMKtec X3 AI up to 245W for the MINISFORUM G1 Pro. The MS-S1 MAX draws the most at 130W continuous with a 320W supply behind it. For a machine queueing overnight batches, a 54W box costs a trivial amount of electricity a year, and a 245W box costs noticeably more but still less than a tower.
Noise follows the same shape. Documented figures run from 32dB on the Beelink GTR9 Pro 395 and 35dB in quiet mode on the EVO-X2 AI, up to 45dB at full load on the AI X1 Pro-370. If the machine will sit in a studio rather than a cupboard, check the acoustic figure for the mode you will actually run. Our fanless mini PC guide covers the silent-only end of the market.
Frequently Asked Questions
Can a mini PC really run Stable Diffusion?
Yes. Every machine in this roundup runs Stable Diffusion and SDXL locally. Integrated GPUs such as the Radeon 890M and 8060S generate images, just more slowly than a discrete RTX card. Independent testing puts integrated GPUs roughly 4 to 10 times slower, so a 1024 by 1024 SDXL image can take 30 to 60 seconds instead of 8 to 12.
Which mini PC is best for Stable Diffusion?
The MINISFORUM AI X1 Pro-370 is our overall pick because its memory and storage are both upgradeable and it has an OCuLink port for a future discrete GPU. The GMKtec EVO-X2 AI is the performance value choice, giving 40 compute units for far less money. The Beelink GTR9 Pro 395 wins outright if you need a 128GB memory pool for Flux-class models.
How much RAM do I need for Stable Diffusion on a mini PC?
16GB handles SD 1.5 with some offloading on SDXL. 32GB makes SDXL and a ControlNet stack comfortable. 64GB fits Flux-class models and heavier multi-model workflows. 128GB removes the offload tax entirely. On an integrated mini PC this matters more than usual because graphics memory is carved out of the same pool.
Is ROCm better than DirectML for Stable Diffusion?
ROCm on Linux is the faster path for AMD integrated GPUs and gives access to the full memory pool on 128GB machines. DirectML on Windows needs no OS change and works with standard Automatic1111 and ComfyUI builds, but generation is slower and Intel Arc support on that path is less mature. Choose Linux if you can, Windows if you cannot.
Can I run SDXL on a budget mini PC?
Yes, with expectations set. A Radeon 890M machine with 32GB of memory runs SDXL at roughly 30 to 45 seconds per 1024 by 1024 image, which is fine for overnight batches and frustrating for interactive prompt iteration. Cutting to 768 by 768, 20 steps and SD 1.5 while you iterate is the fastest way to make a slow machine feel usable.
What is the difference between SD 1.5 and SDXL performance?
SDXL costs roughly two to three times the compute of SD 1.5 because it renders at 1024 by 1024 instead of 512 by 512 and uses a heavier text encoder. That gap widens on integrated GPUs where every extra step is felt. Iterating on SD 1.5 at 768 by 768 and switching to SDXL only for final renders is the standard way to work on a slow machine.
Is Linux required for Stable Diffusion on mini PCs?
No. DirectML on Windows runs ComfyUI and Automatic1111 with the standard builds, which is the least difficult starting point. Linux with ROCm is faster on AMD integrated GPUs and is the only way to address the full memory pool on 128GB machines, where Windows caps usable graphics memory near 64GB. Both paths work; only one unlocks the full hardware.
Do I need a discrete GPU for Stable Diffusion?
Not for SD 1.5 or SDXL batch generation. A 40-compute-unit integrated GPU handles those fine given patience. You do want a discrete card for interactive iteration at speed, for LoRA training, or for models that will not fit in the memory share an integrated GPU gets. An OCuLink port lets you add one later.
Final Verdict: Which Mini PC Should You Buy in 2026
If you want the best mini PC for Stable Diffusion that can still grow, buy the MINISFORUM AI X1 Pro-370. Upgradeable memory, upgradeable storage, an OCuLink port and thermals that held under a full load in our testing make it the pick we would put on a desk without hesitating. Populate both memory slots to 64GB before you start.
If you care only about generation speed per pound of spend, the GMKtec EVO-X2 AI gives you 40 compute units and 8000 MT/s memory for a fraction of the cost of a 128GB machine, and the 140W performance mode keeps long batches from throttling. If your models are large enough that offloading ruins the workflow, the Beelink GTR9 Pro 395 and GMKtec X3 AI are the 128GB answers, and both are markedly better on Linux than on Windows.
Go discrete if you iterate interactively all day. The TOPGRO T1-MAX and MINISFORUM G1 Pro put a real CUDA card in a small chassis and cut generation time to a range that changes how you work, though 8GB of VRAM remains their limit. Whichever you choose, run it on Linux if you can, and check the memory configuration before anything else.
