Best Mini PC for Local AI and LLM Inference in 2026

A mini PC can run local language models well if you size it by memory. Capacity decides which models fit, and memory bandwidth largely decides how fast they answer.

The Minisforum MS-S1 Max is our pick for large models. The Beelink SER9 Pro is the value choice for small ones, the Minisforum UM890 Pro is the upgradeable middle ground, and the Beelink Mini S13 is a low-cost learning box.

A mini PC won’t match a desktop GPU for speed on models that fit in the GPU’s memory. Its advantage is a quiet, efficient box that can hold a larger model than a 16 GB graphics card.

Match the tier to the models you want to run

If you need 10GbE or PCIe expansion for an accelerator, read the 10GbE mini PC guide. For general server duty, see the home server guide.

What matters for local AI on a mini PC

Memory capacity decides what fits. At 4-bit quantization, weights need roughly 0.6 GB per billion parameters, plus room for context and the operating system (the FAQ below lists typical sizes). A model that doesn’t fit either won’t load or spills into swap and slows to a crawl. Buy for the largest model you actually plan to use.

Memory bandwidth sets the speed ceiling. Each generated token reads the model’s weights from memory, so with the same model and software, a machine with twice the bandwidth can generate roughly twice as fast. The theoretical peaks in this guide range from about 26 GB/s on the S13 to 256 GB/s on the MS-S1 Max, calculated from each system’s memory specification.

The integrated GPU performs most of the inference computation. llama.cpp can run models on Radeon integrated graphics through its Vulkan backend, which is how the AMD picks here get most of their speed. How much system memory the GPU can use depends on BIOS and driver settings.

The NPU depends on the runtime. FastFlowLM, which AMD integrated into its Lemonade Server, runs LLMs on XDNA 2 NPUs (Strix, Strix Halo, Kraken, and Gorgon Point chips) on Windows and Linux. It doesn’t support first-generation XDNA NPUs, and general-purpose runtimes such as llama.cpp target the CPU and GPU. Of the picks here, only the MS-S1 Max has an NPU that FastFlowLM supports.

Our pick: Minisforum MS-S1 Max

The Minisforum MS-S1 Max uses AMD’s Ryzen AI Max+ 395 (“Strix Halo”), which combines 16 Zen 5 cores, a 40-compute-unit Radeon 8060S, and up to 128 GB of LPDDR5X-8000 on a 256-bit bus. The CPU and GPU share that memory, so a large share of it can hold model weights for the GPU.

That capacity is the reason to buy it. A 70B-class model at 4-bit quantization needs about 40 GB for weights alone. That’s out of reach for a 16 GB graphics card but fits here with room for context. Its XDNA 2 NPU is also supported by FastFlowLM if you want to experiment with NPU inference.

The trade-offs are cost, power, and speed. Minisforum’s performance mode runs the chip at 130W, and the memory is soldered. A desktop GPU with more bandwidth, such as the RTX 5060 Ti 16GB at 448 GB/s, is faster for models that fit in its memory. Several other mini PCs use the same Ryzen AI Max+ 395, so compare memory configurations, ports, and price before choosing.

The dual 10GbE ports help with copying large model files and other lab traffic. Serving chat responses to a few clients on your network needs very little bandwidth.

For GPU-first inference, see our best GPU for local LLMs guide.

Best value: Beelink SER9 Pro

The Beelink SER9 Pro is the affordable entry point for running models on an integrated GPU. Its Ryzen 7 H 255 has eight Zen 4 cores and Radeon 780M graphics, and Beelink sells it with 24 or 32 GB of LPDDR5X-6400 and a 1 TB SSD.

Buy the 32 GB version. The memory is soldered, and 32 GB leaves room for models up to about 14B at 4-bit alongside the operating system. The H 255 has no NPU, so NPU runtimes don’t apply to this machine.

At about 102 GB/s, the SER9 Pro’s theoretical memory bandwidth is a little higher than the UM890 Pro’s, which helps token generation on the same model. It also doubles as a capable home server.

Upgrade path: Minisforum UM890 Pro

The Minisforum UM890 Pro is the flexible choice. Its two DDR5-5600 SO-DIMM slots take up to 96 GB, so you can start at 32 GB and add memory later for larger models.

With 64 GB or more installed, a 70B-class model at 4-bit fits in memory, but expect slow generation on about 90 GB/s of bandwidth. That works for batch jobs such as summarization and is less pleasant for interactive chat.

Its OCuLink port provides a PCIe 4.0 x4 link to an external GPU enclosure, which is the most direct way to add a desktop GPU to a mini PC. FastFlowLM doesn’t support its first-generation XDNA NPU.

Budget pick: Beelink Mini S13

The Beelink Mini S13 is not an inference machine, but it’s an inexpensive way to learn the local AI toolchain. Its Intel N150 runs small quantized models on the CPU from a single channel of DDR4. Intel lists one memory channel and DDR4-3200 support, which works out to at most about 26 GB/s, so responses are slow.

That’s enough to install Ollama or llama.cpp, compare small models, and build API integrations that you later move to faster hardware. With 16 GB of memory, stick to small models. Its dual 2.5GbE and second M.2 slot also make it a useful always-on node for other home-lab services. Beelink’s own store doesn’t list the S13, so these specifications come from the Amazon listing sold by Beelink.

Where a Mac mini fits

Apple’s Mac mini with M5 Pro offers up to 64 GB of unified memory with 307 GB/s of bandwidth. That’s more bandwidth than the MS-S1 Max but half its maximum memory. It’s worth comparing if you prefer macOS and your models fit in 64 GB. Memory is fixed at purchase: the linked Amazon listing is the 24 GB model, and Apple sells the 48 GB and 64 GB versions build-to-order.

Realistic expectations

Token generation speed depends on the model, quantization, context length, runtime, and drivers, so we don’t publish speed figures. As a planning rule, speed scales with memory bandwidth for a given model. The MS-S1 Max has about 2.5 times the SER9 Pro’s bandwidth and about 10 times the S13’s.

For interactive chat, a small model that answers quickly is often more useful than a large one that barely fits. Buy the memory for the largest model you really need, then choose the fastest memory you can afford.

Our recommendation

If you want to run large models in a compact box, buy the Minisforum MS-S1 Max with the memory you need. For small models at a much lower cost, the 32 GB Beelink SER9 Pro is the best value. If you want to grow into larger models or add an external GPU, choose the Minisforum UM890 Pro. To learn the tools first, start with the Beelink Mini S13 or another box from our N100/N150 guide.

For fine-tuning, fast 70B inference, or many concurrent users, use a GPU. See our best GPU for local LLMs guide.