10 Best Mini PCs for Local LLMs in 2026: Compact Picks for On-Device AI

Running an LLM on your own desk can keep prompts private and avoid cloud fees, but a compact PC needs enough memory for both the model and its workloads. The 10 listings here focus on AMD Ryzen AI Max+ 395 systems, many advertised with 128GB of RAM.

Because similar specifications do not guarantee identical configurations, check the seller’s exact memory, storage, operating system, ports, and return policy before buying.

Best 10 Mini PCs for Local LLMs Picks for 2026

Best 64GB-Class Option – GMKtec EVO-X2 AI Mini PC

Among mini pcs for local llms, the GMKtec EVO-X2 is a compact choice for buyers prioritizing the Ryzen AI Max+ 395 and its Radeon 8060S integrated graphics. The supplied notes describe a 64GB shared-memory pool and position it for models such as DeepSeek 32B.

Best For: Running local models on a compact AMD system while also using multiple displays.

Pros:

• 16-core, 32-thread Ryzen AI Max+ 395 with a 40-CU Radeon 8060S GPU.
• LPDDR5X-8000 shared memory gives the integrated GPU access to the stated 64GB pool.
• Dual USB4, HDMI 2.1, DisplayPort 1.4, 2.5GbE, and Wi-Fi 7 support a flexible desk setup.

Cons:

• The supplied notes do not establish how much memory can be allocated to the GPU on this configuration.
• The stated 35dB noise figure applies to Quiet Mode, not necessarily sustained AI workloads.

The 64GB pool is the key buying consideration: check the exact listing configuration and your model’s memory needs before counting on a particular quantization or context length.

Best for Large Model Memory – GEEKOM A9 Mega AI Workstation

For shoppers comparing mini pcs for local llms, the GEEKOM A9 Mega stands out for its specified 128GB of LPDDR5X memory and 2TB SSD. Its Ryzen AI Max+ 395 and Radeon 8060S can support sizable local workflows, though model compatibility depends on the software, drivers, and quantization used.

Best For: Developers who want substantial shared memory and fast NAS connectivity for private, on-device work.

Pros:

• 128GB of LPDDR5X-8000 memory, with up to 96GB allocatable to the GPU under supported settings.
• 2TB PCIe Gen4 SSD and two M.2 slots for storing models and projects.
• Dual 10GbE, dual USB4, Wi-Fi 7, and support for four displays.
• Specified three-year limited warranty.

Cons:

• The advertised support for select 128B 4-bit models is conditional, not a guarantee for every model or setup.
• GPU memory allocation requires supported settings.

Choose this one primarily for its memory capacity and networking. Before purchasing for a specific large model, verify its quantized size and the inference software you intend to use.

Best Versatile Desk Setup – GMKtec AI Mini PC Ryzen AI Max+ 395

The GMKtec AI Mini PC is another Ryzen AI Max+ 395 entry among mini pcs for local llms. Its supplied notes describe a Radeon 8060S GPU, a 64GB shared-memory pool, and outputs for a four-screen workspace, making it worth considering if your AI tasks share a desk with everyday computing.

Best For: A multi-monitor desktop that also runs compatible local LLMs through apps such as LM Studio.

Pros:

• 16-core, 32-thread processor and 40-CU integrated GPU.
• LPDDR5X-8000 memory and a stated 64GB pool available to the integrated graphics.
• HDMI 2.1, DisplayPort 1.4, and dual USB4 provide several display connections.

Cons:

• The supplied notes do not clearly distinguish this listing’s configuration from the other GMKtec EVO-X2 listing.
• The Quiet Mode noise rating does not establish noise levels during sustained inference.

Compare the exact memory, storage, and price shown on this ASIN with the other EVO-X2 listing; the supplied descriptions alone do not establish a meaningful performance difference.

Best for Expansion and Clustering – MINISFORUM MS-S1 MAX

Of these mini pcs for local llms, the MINISFORUM MS-S1 MAX has the clearest focus on expansion. It pairs a Ryzen AI Max+ 395 with 128GB of unified LPDDR5X memory, dual 10GbE, an 80Gbps USB4 V2 connection, and a full-length PCIe x16 slot.

Best For: Advanced users planning a high-memory inference workstation or a multi-unit local AI setup.

Pros:

• 128GB of LPDDR5X-8000 shared memory for demanding model workloads.
• Full-length PCIe x16 slot, dual M.2 slots, dual 10GbE, and USB4 V2 offer expansion and fast connectivity.
• Designed for sustained operation, with a specified 130W continuous power mode and multiple fan modes.
• Supports rack-oriented, multi-unit configurations.

Cons:

• Claimed 235B and 671B cluster demonstrations require multiple units and should not be read as single-unit capabilities.
• The supplied notes give conflicting storage specifications, so confirm the installed drive and expansion limits on the listing.

This is the most infrastructure-oriented choice here. Its extra ports and cluster features matter most if you will actually use them; otherwise, prioritize the memory and software requirements of your intended models.

Flexible GPU Memory – GMKtec EVO-X2 Ryzen AI Max+ 395

Among mini pcs for local llms, the GMKtec EVO-X2 stands out for its Ryzen AI Max+ 395, Radeon 8060S integrated graphics, and shared 128GB memory pool. That combination is particularly relevant when a model needs more memory than a typical dedicated GPU provides.

Best For: Buyers who want a compact local-LLM machine that also handles gaming and multi-monitor work.

Pros:

• 16 Zen 5 cores and 40 RDNA 3.5 graphics compute units.
• Fast onboard LPDDR5X-8000 memory; the integrated GPU can access the shared pool.
• Dual USB4, 2.5GbE, Wi-Fi 7, and four-display support.

Cons:

• Onboard LPDDR5X memory is not a conventional upgradeable SODIMM setup.
• The supplied notes do not specify included SSD capacity.

The shared-memory design is the main reason to consider this model for larger local workloads. Check the exact memory and storage configuration before buying, and treat the notes’ large-model performance claim as workload-dependent.

Networked Workstation – MINISFORUM MS-S1 Max

If your search for mini pcs for local llms includes fast networked storage, the MINISFORUM MS-S1 Max deserves a look. It pairs a Ryzen AI Max+ 395 and Radeon 8060S with 128GB of LPDDR5-8000 memory, a 2TB SSD, and two 10GbE ports.

Best For: A local-AI workstation connected to high-speed network storage and multiple displays.

Pros:

• 128GB memory and a preinstalled 2TB PCIe 4.0 SSD.
• Two 10GbE ports suit fast wired connections to compatible network equipment.
• Additional SSD slot, internal PCIe slot, and five listed video outputs.

Cons:

• The onboard LPDDR5 memory is not described as user-upgradeable.
• The internal PCIe x16 slot runs at PCIe 4.0 x4, not x16 bandwidth.

This is the most connectivity-focused option here, especially if model files live on a fast NAS. Its expansion options are useful, but check physical compatibility before planning an add-in card.

Ready-to-Store Models – BOSGAME M5 AI Mini PC

The BOSGAME M5 is a practical pick among mini pcs for local llms if you want substantial memory and storage in the listed configuration. Its Ryzen AI Max+ 395 and Radeon 8060S share 128GB of LPDDR5X-8000 memory, while a 2TB SSD leaves room for model files from day one.

Best For: Buyers who want a compact local-LLM setup with 128GB memory and a preinstalled 2TB drive.

Pros:

• 128GB onboard memory, with the notes specifying up to 96GB dynamic allocation for graphics.
• Preinstalled 2TB PCIe 4.0 SSD and a second M.2 slot.
• Dual USB4, 2.5GbE, Wi-Fi 7, and HDMI and DisplayPort outputs.

Cons:

• Onboard memory is capped at the listed 128GB rather than offering a stated RAM upgrade path.
• 2.5GbE is slower than the dual 10GbE ports listed for the MINISFORUM option.

The M5 balances a large shared-memory pool with useful out-of-box storage. Model size and response speed will still depend on quantization and software, so do not read the seller’s 70B+ claim as a guaranteed result for every setup.

Best for GPU Expansion – GMKtec X3 AI Mini PC

Among mini pcs for local llms, the GMKtec X3 stands out for its Ryzen AI Max+ 395, 128GB of LPDDR5X memory, and OCuLink port for an optional external GPU. Its included 2TB PCIe 4.0 SSD gives you room to start building a local model library.

Best For: Buyers who want substantial shared memory now and the option to add external GPU acceleration later.

Pros:

• 128GB of LPDDR5X memory gives local models a large shared memory pool.
• OCuLink PCIe 4.0 x4 supports an external GPU dock.
• Includes a 2TB SSD, 2.5GbE, and Wi-Fi 7.

Cons:

• LPDDR5X memory is installed on-board, so do not count on a RAM upgrade.
• External GPU acceleration requires a separate dock and GPU.

The X3 is the flexible pick if you want a capable all-in-one system without giving up a dedicated path to stronger graphics. Treat specific model-size and speed claims as workload-dependent.

Best Linux-Ready Pick – NIMO Ryzen AI Max+ 395 Mini PC

If you are comparing mini pcs for local llms and prefer a Linux starting point, this NIMO system comes with Linux pre-installed. It pairs a Ryzen AI Max+ 395 with 128GB of LPDDR5X-8000 memory, a combination suited to experimenting with models that need a large shared memory pool.

Best For: Developers who want a Linux-ready local AI desktop with fast networking and storage expansion.

Pros:

• Pre-installed Linux lets you begin setup without first replacing Windows.
• 128GB of LPDDR5X memory and a 256-bit bus support memory-intensive workloads.
• Dual 2.5GbE ports, dual USB4 ports, and two M.2 PCIe 4.0 SSD slots add connectivity and storage options.

Cons:

• The supplied listing does not establish an included SSD capacity; check the configuration before buying.
• Software support for GPU or NPU acceleration depends on the tools and drivers you use.

This is a practical choice for a Linux-based model-serving or development setup. Its connectivity is also useful if the mini PC will share space with a homelab rather than serve only as a desktop.

Best for Multi-Display Setups – NIMO Linux AI Mini PC

This NIMO is another Linux-preinstalled option among mini pcs for local llms, with a Ryzen AI Max+ 395 and 128GB of LPDDR5X-8000 memory. Its stated support for four displays makes it worth considering if model testing shares your desk with code, logs, and other development tools.

Best For: Developers who want a local AI workstation connected to several monitors.

Pros:

• Large, fast shared memory pool suits memory-heavy local workloads.
• Supports up to four displays through HDMI 2.1, DisplayPort, and dual USB4.
• Dual M.2 PCIe 4.0 slots provide room to expand model and project storage.

Cons:

• The supplied notes describe the same core specifications as the other NIMO listing, so confirm the exact configuration before choosing between them.
• Actual local LLM acceleration depends on software compatibility, not the NPU rating alone.

Choose this listing for the stated Linux and multi-monitor feature set, but compare its configuration and price with the other NIMO ASIN before ordering; the supplied descriptions do not establish a hardware difference.

How We Picked the Best Mini PCs for Local LLMs

This roundup focuses on the 10 selected listings, all built around the AMD Ryzen AI Max+ 395. We prioritized the specifications most relevant to local inference: advertised RAM capacity, memory type, storage, operating system, and connectivity. We also looked for configuration details worth checking before purchase, such as whether memory is fixed or upgradeable. A shared processor does not mean identical performance: power limits, cooling, firmware, and software setup can differ. Treat these as buying criteria, not as claims that every system has been benchmarked under the same conditions.

Quick Comparison

The clearest comparison is configuration, not brand. Listings that explicitly advertise 128GB RAM are the most straightforward starting point if you want room for larger quantized models or longer contexts. Check whether each listing actually includes that capacity; product families may have multiple variants. A 2TB SSD gives more space for model files than a smaller drive, while an extra M.2 slot can make expansion easier. If Linux is your intended environment, the NIMO listings advertise it; verify the installed distribution and driver support. Two NIMO entries appear identical in the provided list, so compare their seller pages rather than assuming they are different machines.

Key Buying Factors for Mini PCs for Local LLMs

Memory Capacity Comes First

Model weights, context caches, and the operating system all need memory. More RAM lets you attempt larger models or longer prompts, but capacity alone does not guarantee a particular speed. On these integrated-GPU systems, confirm how memory can be allocated to graphics and what your chosen inference software supports. LPDDR5X memory is typically soldered, so check upgradeability before relying on a future RAM upgrade.

Match Software to the Hardware

Confirm that your preferred tools run the model and quantization you want on the operating system you plan to use. CPU inference is an option, but GPU acceleration depends on compatible software and drivers. Do not treat an advertised AI NPU specification as proof that your LLM application will use the NPU.

Plan for Heat, Storage, and Connections

Sustained inference can run for minutes or hours, making cooling and noise more important than peak processor specifications. Look for credible sustained-load testing when available. Budget SSD space for several model versions, and check M.2 expansion if you expect your library to grow. Review USB, display, and networking ports against your actual setup; ports rarely improve inference speed, but missing ones add adapters and cost.

Who Should Buy Which Mini PCs for Local LLMs?

Exploring local AI: Choose the least expensive verified configuration that fits the models you plan to run, and confirm its RAM before checkout. Trying larger quantized models: Start with a clearly specified 128GB configuration and check software compatibility, memory allocation, and sustained-load results. Keeping many models on hand: Favor a confirmed 2TB SSD or accessible storage expansion. Building a Linux-first setup: Consider listings with Linux included, but verify the distribution and acceleration support. Since several entries use closely related hardware and some names repeat, compare the exact SKU, seller, warranty, and return terms rather than buying by headline alone.