The best mini PCs for local LLMs turn a small desk computer into a private, always-on AI box. They run models on your own hardware, so prompts, documents, and Home Assistant requests can stay inside your network instead of going to a hosted service.
For this job, I put memory capacity ahead of CPU marketing. A compact PC can have a very fast processor yet still feel restrictive if its RAM cannot hold the quantized model and the context window you want to use.
We reviewed all eight available configurations here by their disclosed hardware, connectivity, expansion path, cooling design, and buyer feedback. The short version: the GMKtec EVO-X2 is the strongest inference-first choice in this group, while several socketed-memory machines make more sense for smaller models, homelabs, or an eventual external GPU.
Local inference is not automatically better than cloud AI for every request. It is a strong fit when privacy, repeat use, offline availability, or an always-ready local service matters; for very large reasoning jobs, many community members still pair local hardware with an API.
Table of Contents
Top 3 picks (July 2026)
These three cover the clearest use cases: a high-bandwidth unified-memory system, a roomy and expandable Ryzen AI machine, and a broadly reviewed entry point. Pick based on model size and whether the box will become a multi-purpose server.
Best mini PCs for local LLMs in 2026 at a glance
The table is a starting point, not a substitute for checking how much memory your model needs. All eight machines are included, and the reviews below explain the trade-offs that a spec list can hide.
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GMKtec EVO T1
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GEEKOM A9 Max
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MINISFORUM AI X1 Pro-370
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KAMRUI AM21
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GMKtec NucBox K11
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GMKtec M7 Ultra
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GMKtec K8 Plus
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GMKtec EVO-X2
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1. GMKtec EVO T1 is the Intel choice for storage and eGPU plans
Pros
- 64GB memory
- Three M.2 slots
- OCuLink expansion
- 2.5GbE
Cons
- 90W power draw
- Intel iGPU memory limits
The EVO T1 takes a different route from the AMD systems in this guide. Its Core Ultra 9 285H has 16 cores and a stated 5.4GHz turbo, paired with 64GB of DDR5-5600 and Intel Arc 140T graphics.
I would look at it for a local AI workstation that also needs serious SSD room. Three M.2 2280 slots and an OCuLink port give it a clearer physical growth path than a sealed appliance, and 2.5GbE is useful when models or documents live on a NAS.
Its 13-TOPS Intel AI Boost NPU can help with supported AI tasks, but NPU TOPS are not the same thing as general LLM inference speed. In practice, model fit and the graphics-memory path matter more for Ollama or LM Studio than an NPU headline number.
The published 90W power figure is also worth planning around if this computer will run all day. Its dual-fan cooling and four-display support make sense for a desk setup, but a quiet shelf server may call for conservative power settings.
The EVO T1 fits users who want storage and an external-GPU route
This is a sensible pick for developers keeping several models, vector databases, and project data locally. I also like the dual role: it can drive up to four displays while serving a chat model on the network.
OCuLink works at PCIe x4 speeds, which is meaningful for an eGPU project. Community reports still warn that some AMD eGPU combinations can encounter a 120W cap, so treat the port as expansion flexibility rather than a promise of desktop-class GPU behavior.
The EVO T1 is less suited to huge models on integrated graphics alone
Although 64GB is useful capacity, this is ordinary socketed DDR5 rather than the high-bandwidth unified LPDDR5X arrangement in the EVO-X2. I would target smaller quantized models or plan the OCuLink build before assuming comfortable 70B-class work.
It has a 4.5 rating from 908 reviews, which is a comparatively substantial feedback base in this list. The one-year limited warranty is shorter than the GEEKOM alternative.
2. GEEKOM A9 Max is the polished Ryzen AI 9 starter
Pros
- 50 TOPS NPU
- 128GB RAM support
- Dual 2.5GbE
- Three-year warranty
Cons
- Ships with 32GB
- Integrated graphics
The GEEKOM A9 Max combines a 12-core Ryzen AI 9 HX 370, Radeon 890M graphics, and a 50-TOPS XDNA 2 NPU. It arrives with 32GB of DDR5, but the platform is specified as expandable to 128GB.
That upgrade ceiling is why I see it as a practical local LLM mini PC rather than only a Windows productivity box. The all-metal chassis, IceBlast 2.0 cooling, dual 2.5GbE, WiFi 7, and dual USB4 ports give a home server plenty of options.
Start with realistic expectations around the included 32GB. It is a workable amount for smaller quantized chat models, embeddings, voice helpers, and Home Assistant automations, but it does not give a 70B model the breathing room that large contexts demand.
Windows 11 Pro comes installed, while the listing also names Ubuntu Linux and VMware ESXi. I would still verify the current Linux kernel, AMD graphics stack, and your chosen inference app before turning it into a headless server.
The A9 Max works well for an upgradable multi-role home server
Choose this one if the box will also run containers, a media server, coding tools, or network services. Two 2.5GbE ports can separate a trusted management network from a LAN-facing AI service without adding a USB adapter.
The dual PCIe Gen4 storage arrangement is rated for up to 8TB total. That space is helpful when local RAG work needs both model files and an indexed document collection.
The A9 Max needs a memory plan before large-model inference
Its Radeon 890M uses system memory, so installed capacity is shared among the operating system, graphics, and model. I would reserve the included configuration for compact models and budget a memory upgrade if large local models are the goal.
The 4.5 rating comes from 396 reviews. Its three-year limited warranty is the longest stated coverage among these eight products.
3. MINISFORUM AI X1 Pro-370 gives 64GB and OCuLink together
Pros
- 64GB installed
- OCuLink port
- Three SSD slots
- WiFi 7
Cons
- Only 17 reviews
- Fan cooling
The AI X1 Pro-370 is the more immediately useful HX 370 configuration for local models because it starts at 64GB of DDR5-5600. It combines the 12-core, 24-thread Zen 5 processor, Radeon 890M, and 50-TOPS NPU with dual 2.5GbE networking.
I would pick it over a 32GB configuration when a local agent, a document index, and ordinary server tasks must coexist. Its stated 96GB memory ceiling also leaves a later upgrade route, even though it does not match the bandwidth profile of a Strix Halo design.
Storage is unusually flexible for a small system. The product data lists one 1TB PCIe 4.0 drive plus two additional PCIe 4.0 slots, with up to 12TB total stated capacity.
For best mini PCs for local LLMs, that combination matters more than cosmetic extras. It lets you keep multiple quantizations, a Linux installation, and local backups without constantly clearing the main drive.
The AI X1 Pro-370 suits homelab users who need ports and RAM now
OCuLink, dual USB4, HDMI, DisplayPort, dual 2.5GbE, and WiFi 7 cover a lot of infrastructure needs without a dock. I would use the wired ports for a NAS and the main network, then run the model service in a container or virtual machine.
It also supports four displays, which is a bonus for a desk-bound development system. The CPU has enough threads to handle preprocessing and other background work alongside inference.
The AI X1 Pro-370 is not a substitute for a high-bandwidth 128GB box
Memory capacity helps model fit, but it does not erase bandwidth limits of integrated graphics sharing DDR5. Expect long-context RAG and larger models to ask more of the machine than ordinary short chat prompts.
Its 4.5 rating is based on 17 reviews, so I would place less weight on that score than on products with hundreds or thousands of reviews. The listing describes fan cooling rather than a more detailed thermal design.
4. KAMRUI AM21 is the established compact-model pick
Pros
- Large review base
- 96GB memory support
- USB4
- Two-year warranty
Cons
- 32GB included
- No OCuLink listed
The KAMRUI AM21 has an eight-core Ryzen 7 8745HS, Radeon 780M graphics, 32GB of DDR5-5600, and a 1TB PCIe 4.0 SSD. It is the most heavily reviewed product here, holding a 4.4 rating from 2,333 reviews and a listed number-one mini-computer sales rank.
I see it as the sensible choice for smaller local models, model experimentation, and a first Ollama server. The 32GB starting point limits what fits comfortably, but its two memory slots are specified to support up to 96GB.
Networking is simpler than on the higher-end AMD systems: it has two Gigabit Ethernet ports rather than dual 2.5GbE. For a single household model server that is generally sufficient, though large model transfers from a NAS will take longer.
USB4 supports 40Gbps data, 100W Power Delivery, and 8K video according to the listing. Four-display output can make it a quiet office PC by day and a local assistant host when the workday ends.
The AM21 is right for smaller models and proven buyer feedback
Use it for compact instruct models, embedding jobs, speech pipelines, or Home Assistant intent tasks. I would also consider it for a household that wants a normal desktop alongside a private chatbot.
The two-year warranty is a welcome middle ground. Its broad review base offers more confidence in basic ownership experience than the very new models with only a small group of reviews.
The AM21 is not the first choice for 70B or heavy RAG workloads
The Radeon 780M is capable integrated graphics, but it shares the same 32GB pool with the rest of the computer. More RAM can help model fit, yet this platform is still aimed at restrained model sizes rather than maximum local inference.
There is no OCuLink port listed for the AM21. If you already know that an external GPU is part of the plan, one of the OCuLink-equipped options is the more direct fit.
5. GMKtec NucBox K11 is the storage-first Ryzen 9 option
Pros
- 2TB included
- OCuLink port
- Dual 2.5GbE
- 35W base TDP
Cons
- 32GB included
- One-year warranty
The NucBox K11 pairs an eight-core Ryzen 9 8945HS with Radeon 780M graphics and 32GB of DDR5-5600. Its standout configuration detail is the included 2TB PCIe 4.0 SSD, plus a second slot that brings its stated maximum storage to 8TB.
That makes it attractive for people who want local model libraries without adding storage on day one. I would reserve its initial 32GB memory for modest model sizes, then use the 96GB supported ceiling if the model collection grows.
Its dual Intel i226V 2.5GbE ports are an advantage for a serious home network. A wired 2.5GbE connection is useful when the box is serving multiple clients or pulling documents from fast network storage.
The chassis uses dual cooling fans, and the listing gives a 35W TDP that can be raised to 70W. Sustained inference produces more heat than opening a chat window, so I would test quiet, normal, and high-power modes in the actual location where it will live.
The K11 is best when local data and models need roomy storage
It is a strong candidate for a personal RAG project, a code repository assistant, or a small shared lab because 2TB arrives installed. More capacity reduces the friction of retaining several model files and their quantized variants.
OCuLink offers a later path to an external GPU, while USB4 handles fast peripherals and displays. That pairing gives the K11 more upgrade flexibility than many basic mini PCs.
The K11 still needs extra RAM for ambitious local inference
Storage does not stand in for model memory. I would not confuse the 2TB drive with the 32GB RAM allocation available to the CPU and Radeon graphics during inference.
The 4.4 rating is based on 474 reviews. GMKtec states a one-year limited warranty, which may matter if this is intended as unattended infrastructure.
6. GMKtec M7 Ultra is the flexible older-platform OCuLink server
Pros
- OCuLink expansion
- Three power modes
- Dual 2.5GbE
- Dual USB4
Cons
- No OS included
- Older Radeon 680M
The M7 Ultra uses the Ryzen 7 PRO 6850U, an eight-core processor with Radeon 680M graphics, 32GB of DDR5-4800, and a 1TB PCIe 3.0 SSD. It is an older platform than the 780M and 890M systems, but it retains a useful OCuLink port and dual 2.5GbE.
I would treat this as a practical server base rather than a standalone large-LLM machine. It can host a compact model, router, and supporting services today, then gain an external GPU later if the local AI workload becomes more demanding.
The hardware offers Quiet at 35W, Balance at 50W, and Performance at 65–70W modes. That is valuable in an always-on rack or office because noise tolerance varies more than marketing sheets suggest.
Dual USB4, HDMI 2.1, and DisplayPort can support up to four screens at 8K according to the product data. The strong port selection also makes setup easier with fast drives and adapters.
The M7 Ultra suits tinkerers building around OCuLink
The machine makes sense if external graphics are part of a staged project and you are comfortable configuring an operating system. It is also useful for network-heavy homelabs where dual 2.5GbE is more valuable than a very new NPU.
Its memory can expand to 96GB, which gives some room for experimentation. Keep expectations grounded: the Radeon 680M and DDR5-4800 are not a direct match for newer high-bandwidth integrated designs.
The M7 Ultra requires more setup work than turnkey alternatives
No operating system is included, so you need to install and maintain Windows or Linux before an inference stack goes online. For a first mini PC, that is an extra task rather than a defect, but it changes the buying decision.
The 4.4 rating is based on 474 reviews. I would check the physical OCuLink cable routing and eGPU enclosure fit before committing to a compact cabinet layout.
7. GMKtec K8 Plus balances fast storage with a familiar Ryzen 7
Pros
- 2TB PCIe 4.0
- 96GB memory support
- Dual 2.5GbE
- Windows 11 Pro
Cons
- 32GB included
- No OCuLink in product details
The K8 Plus brings together a Ryzen 7 8845HS, Radeon 780M, 32GB of DDR5-5600, and a 2TB PCIe 4.0 SSD. It is the more polished general-purpose option for someone who needs a full Windows 11 Pro mini PC with generous local storage.
I would consider it for coding with local LLMs, a private document assistant, and normal desktop work. The processor boosts to 5.1GHz, and its Radeon 780M can handle lighter inference while the CPU handles data preparation and application tasks.
It has two USB4 ports, HDMI 2.1, DisplayPort 2.1, dual 2.5GbE, WiFi 6, and Bluetooth 5.2. Those connections suit a desk with monitors as well as a server shelf with a NAS and wired network.
The dual-fan system uses vapor-chamber heat pipes and offers Silent, Balanced, and Performance modes. I like having a silent setting available for light tasks, but I would use a sustained model load to judge the setting rather than relying on idle acoustics.
The K8 Plus is good for a desktop that also hosts local AI
Its 2TB starting drive is useful when an AI project shares space with creative files, games, and development tools. The listing says storage can grow to 8TB, so it has a reasonable long-term storage story.
For readers who may later build a discrete-GPU desktop, our guide to AMD graphics cards for AI applications helps frame what a larger system can add beyond a mini PC.
The K8 Plus is limited by its included shared-memory capacity
The advertised configuration has 32GB, which is more appropriate for compact quantized models than large-model experimentation. It can take up to 96GB of DDR5, so memory expansion should be part of the plan rather than an afterthought.
The 4.4 rating comes from 203 reviews. Although the pros list references an OCuLink interface, the technical product details do not list one, so I would not buy this specific model on the assumption that it includes OCuLink.
8. GMKtec EVO-X2 is the strongest integrated-graphics LLM machine here
Pros
- Eight-channel memory
- Radeon 8060S
- 96GB VRAM allocation
- Triple cooling
Cons
- Memory is not upgradeable
- Smaller review base
The EVO-X2 is the clearest LLM-focused system in this selection. It combines the 16-core Ryzen AI Max+ 395, Radeon 8060S with 40 RDNA 3.5 compute units, 50-plus TOPS XDNA 2 NPU, and 64GB of eight-channel LPDDR5X-8000 memory.
That memory architecture changes the conversation. CPU, integrated GPU, and NPU share the same fast memory pool, and the listing states that up to 96GB can be allocated as VRAM; this is why I rank it above the ordinary DDR5 32GB and 64GB options for serious local inference.
It is still important not to treat 64GB as unlimited capacity. A 70B Q4 model can be possible in a carefully managed setup, but context length, runtime overhead, and the operating system all consume memory, while large RAG prompts can feel much slower than ordinary chat.
The EVO-X2 has a 1TB PCIe 4.0 SSD with dual slots, 2.5GbE, WiFi 7, Bluetooth 5.4, two USB4 ports, HDMI 2.1, DisplayPort 1.4, and an SD 4.0 reader. It is a complete local-AI appliance rather than simply a CPU upgrade.
The EVO-X2 is the answer for serious GPU-less local inference
Choose it if your priority is running the largest model that can reasonably fit in a small system without first buying a discrete GPU. It is the strongest candidate here for privacy-focused document work, local coding assistance, and a household AI server with multiple users.
The triple-fan, three-heatpipe cooler and three power modes are designed for sustained work up to the stated 140W Performance setting. I would start in Balanced mode and only raise power after checking noise, temperatures, and actual responsiveness in your own model stack.
The EVO-X2 requires accepting fixed memory and mixed feedback
Its LPDDR5X is on-board, so the 64GB amount cannot be upgraded later. Buy for the model sizes you need now and leave headroom for context rather than assuming a future RAM swap will solve capacity limits.
It has a 4.2 rating from 60 reviews, below the other products here, with a smaller feedback sample. That does not change its hardware advantage for this workload, but it is a reason to review current support terms and buyer feedback before purchasing.
A local-LLM mini PC should be chosen by memory before CPU
The simplest rule is this: buy enough memory for the model, its quantization, your context window, and the operating system at the same time. CPU core counts matter for background tasks, but a model that will not fit in memory is not rescued by a fast CPU.
RAM targets answer how much memory local LLMs need
32GB: Best for small quantized chat models, embeddings, voice processing, and Home Assistant commands.
64GB: A more comfortable starting point for larger quantized models and multi-service use, though long contexts still reduce headroom.
96GB or 128GB: Better for users who need more room for larger models, local RAG, or multiple memory-hungry services.
Quantization matters as much as the parameter count. Q4 uses less memory than Q8, but it can change response quality and speed; model documentation is the right source for the actual RAM requirement of the file you intend to run.
Unified memory answers why Strix Halo stands apart
Unified memory means the CPU, GPU, and NPU draw from one shared RAM pool instead of a discrete GPU having its own separate VRAM. In the EVO-X2, the eight-channel LPDDR5X-8000 design and Radeon 8060S give an integrated system a much better path to large models than normal DDR5 mini PCs.
That does not make the other machines bad. Socketed DDR5 units can be easier to upgrade and may be better all-around computers, but their integrated GPUs face different memory-bandwidth limits during inference.
Ollama is the straightforward starting point for a local server
I would start with Ollama when the goal is a simple local API, command-line workflow, or Home Assistant integration. Install the version supported by your operating system, download a model sized for your available memory, then test a short prompt before adding clients and automations.
Lemonade SDK is worth considering if its supported backends and workflows match your hardware, while LM Studio can be friendlier for desktop model exploration. Neither tool removes hardware limits, so validate model fit first and keep your initial context window modest.
Home Assistant and OpenClaw work best with restrained model choices
For Home Assistant, small and responsive models are generally a better match than chasing maximum parameter count. Put the mini PC on Ethernet, give the service a fixed address, and place model data on reliable local storage before exposing it to household automations.
For OpenClaw agents or other tool-using services, reserve CPU and RAM for the agent framework, browser tools, databases, and logs. A machine that feels fast in a single chat can slow down when it also retrieves documents and calls several tools.
eGPU expansion helps, but it does not erase connection limits
OCuLink is the cleanest expansion feature in this group because it exposes PCIe x4 bandwidth for a compatible external GPU setup. The EVO T1, AI X1 Pro-370, NucBox K11, and M7 Ultra list OCuLink, while the K8 Plus documentation is inconsistent and should be verified before purchase.
Community reports describe some AMD cards being capped at 120W through OCuLink. If discrete-GPU inference is already the main mission, it may be more sensible to compare best gaming PCs for AI workloads than to build an external-GPU chain around a mini PC.
Fine-tuning requires a different plan from inference
Inference means loading a finished model and generating answers. Fine-tuning adds training data, optimizer states, repeated passes, and substantially higher compute and memory needs, so none of these should be bought with the assumption that they are broad fine-tuning workstations.
For light experimentation, use small models and narrow datasets. For frequent large-model tuning, a desktop GPU with ample dedicated VRAM or a managed training environment is the more realistic route.
FAQs
What kind of computer do I need to run LLMs locally?
You need a computer with enough RAM for the quantized model, operating system, and context window, plus a capable GPU or integrated GPU for responsive inference. For a compact system, 32GB suits smaller models, while 64GB or more is a safer target for larger local workloads.
What is the best mini PC for OpenClaw and local LLM?
The GMKtec EVO-X2 is the strongest pick in this group for demanding integrated-graphics inference because its Ryzen AI Max+ 395, Radeon 8060S, and 64GB of eight-channel LPDDR5X share one fast memory pool. For a multi-service OpenClaw setup that needs expandable RAM and networking, the MINISFORUM AI X1 Pro-370 is a strong alternative.
How much RAM do I need for local LLMs?
Use 32GB for compact quantized models and basic local services, 64GB for more headroom and larger quantized models, and 96GB or more when large models, long contexts, or RAG workflows are part of the plan. Model format and context size change the real requirement.
Is it better to run LLMs locally?
Running locally is better when privacy, offline access, predictable repeat use, and control over your data matter most. Hosted AI is often more practical for the largest or most demanding tasks, so many people use a local system for routine work and a cloud service when extra scale is needed.
Final Thoughts
The best mini PCs for local LLMs are the ones that match your model size rather than the loudest AI label. I would choose the EVO-X2 for the strongest integrated-memory design, the AI X1 Pro-370 for 64GB plus expansion, and the KAMRUI AM21 for smaller models with a deep review history.
Choose the model first, leave RAM headroom for its context, and test your software stack early. That approach makes a mini PC a useful private AI system in 2026, not just an impressive specification list.
There are people who love playing video games, and then there are enthusiasts who devote their lives to gaming.
Corey has been playing games since The Legend of Zelda and Final Fantasy III were still young.
Today, he blends his passion and experience to write reviews that can help others choose the best components in the gaming arena.