The Top 10 Mini PCs for Local LLMs in 2026

Mini PCs used to be associated with simple jobs: web browsing, office work, media playback, light servers and perhaps some emulation.
2026 has changed that picture.
A new generation of compact machines can now run surprisingly large language models locally, without sending every prompt to a cloud service.
That means your code, documents, conversations and private data can remain on your own machine.

But there is an important catch.
BUYING A MINI PC FOR LOCAL LLMs IS NOT THE SAME AS BUYING ONE FOR NORMAL COMPUTER USE.
For office work, you might care about CPU speed, SSD performance and battery life.
For local AI, memory becomes the elephant sitting in the room.
A machine with a 16-core processor and 16GB of RAM can be completely useless for a model that requires 40GB of memory.
A machine with a less impressive CPU but 64GB or 128GB of fast unified memory can run models that the smaller machine simply cannot load.
That is why the 2026 local-LLM market is particularly interesting.
AMD's Ryzen AI Max+ 395 and systems such as the GMKtec EVO-X2, Framework Desktop and MINISFORUM MS-S1 MAX have made enormous amounts of GPU-accessible memory available in compact machines.
NVIDIA has taken a different route with the DGX Spark, combining 128GB of unified memory with a Blackwell GPU architecture.
Apple continues to offer another interesting approach through unified-memory Macs.

THE FIRST QUESTION IS NOT 'HOW FAST IS THE CPU?'
The first question should be:
WHAT MODEL DO YOU WANT TO RUN?
A 7B model and a 70B model are completely different animals.
Typical 4-bit model sizes are roughly:
- 3B–4B → Around 2–3GB.
- 7B–8B → Around 4–6GB.
- 13B–14B → Around 8–10GB.
- 30B–34B → Around 18–22GB.
- 70B → Around 40GB or more.
- 120B-class models → Can require 70GB+ depending on quantization.
- 400B-class models → Far beyond what a normal single mini PC can comfortably handle.

These are approximate figures because quantization, context size, architecture and runtime overhead all change the actual memory requirement.
THE RAM IS ALSO YOUR VRAM
This is the most important concept to understand about many modern AI mini PCs.
A traditional desktop with an RTX GPU might have 32GB of system RAM and a graphics card containing 12GB, 16GB or 24GB of VRAM.
The LLM can only use the GPU's VRAM for the portion being accelerated.
A Ryzen AI Max+ 395 system works differently.
The CPU and GPU share a large unified memory pool.
A 128GB configuration can therefore give the integrated Radeon GPU access to a huge amount of memory.
That does not magically turn the machine into a 128GB discrete GPU, but it removes one of the biggest limitations of traditional consumer GPUs: fixed VRAM capacity.
AMD's Ryzen AI Max+ 395 systems can expose up to 96GB of the 128GB memory pool to the GPU in normal configurations, making very large local models possible on compact hardware. Framework documents the 128GB configuration as having 128GB total memory with up to 96GB dedicated to the GPU. AMD has also demonstrated multi-node LLM inference using four 128GB Framework Desktop systems.

MEMORY CAPACITY VS MEMORY SPEED
Having enough memory is only half of the equation.
LLM inference is heavily dependent on moving model data through memory.
That means memory bandwidth matters enormously.
For example:

- DDR5-5600 → Much lower bandwidth.
- LPDDR5X-8000 → Much higher bandwidth.
- 128GB high-bandwidth unified memory → A very different proposition from 128GB of ordinary system RAM.
The Ryzen AI Max+ 395 platform uses a 256-bit LPDDR5X interface and can reach approximately 273GB/s of memory bandwidth.
This is one of the reasons Strix Halo systems can produce much higher token-generation rates than ordinary mini PCs with conventional SO-DIMM memory.
The important lesson is simple:
MORE RAM determines whether the model fits.
MORE BANDWIDTH helps determine how quickly the model runs.

THE NPU IS NOT EVERYTHING
Modern mini PCs advertise enormous AI TOPS numbers.
Do not automatically assume that the highest TOPS number means the fastest local LLM.
An NPU is designed for specific AI workloads and excellent power efficiency.
But in 2026, popular local-LLM software such as Ollama still relies heavily on GPU acceleration for AMD systems rather than simply handing the entire workload to the NPU.
So when buying a machine specifically for LLM inference, pay attention to:

- GPU architecture.
- GPU compute resources.
- Memory capacity.
- Memory bandwidth.
- Software support.
- ROCm/CUDA/Metal compatibility.
- Thermal performance.
The NPU is useful, but it should not be the first specification you use to rank an LLM machine.

OLLAMA, LM STUDIO AND LLAMA.CPP
A good local AI machine is only as useful as its software support.
Popular options include:

- Ollama → Extremely easy way to download and run local models.
- LM Studio → Friendly graphical interface for local models.
- llama.cpp → Highly flexible and widely used inference engine.
- ROCm → AMD's GPU computing ecosystem.
- CUDA → NVIDIA's mature GPU computing platform.
- Metal → Apple's GPU acceleration framework.
This is where NVIDIA still has a major advantage.
CUDA has an enormous software ecosystem, and many AI projects are developed with NVIDIA hardware in mind.
AMD hardware can be extremely capable, particularly with large unified-memory systems, but the software experience can require more experimentation.

THE TOP 10 MINI PCs FOR LOCAL LLMs IN 2026

1. GMKTEC EVO-X2 AI
The GMKtec EVO-X2 AI is arguably the machine that best demonstrates why mini PCs have suddenly become interesting for serious local AI.
It combines AMD's Ryzen AI Max+ 395 with 16 Zen 5 CPU cores, a Radeon 8060S GPU with 40 RDNA 3.5 compute units, an XDNA 2 NPU and 128GB of LPDDR5X-8000 unified memory.
The important part is not the 16-core CPU.
It is the 128GB memory pool.
That enormous pool gives the integrated GPU access to enough memory for models that simply do not fit inside ordinary consumer GPU VRAM.
KEY HARDWARE

- AMD Ryzen AI Max+ 395.
- 16 cores / 32 threads.
- Radeon 8060S.
- 40 RDNA 3.5 compute units.
- XDNA 2 NPU.
- 128GB LPDDR5X-8000 unified memory.
- 2TB NVMe SSD in the listed configuration.

PROS

- 128GB unified memory is the major attraction.
- Capable of running very large local models.
- Extremely high memory bandwidth.
- Strong CPU performance.
- Strong integrated GPU.
- Compact compared with a conventional AI workstation.
- Excellent for developers experimenting with large models.

CONS

- Expensive compared with ordinary mini PCs.
- Memory is soldered and cannot be upgraded later.
- AI workloads consume substantially more power than normal mini-PC workloads.
- Cooling becomes important during sustained inference.
- AMD software support is not as universally mature as NVIDIA CUDA.
- Massive overkill for someone who only wants a 7B or 8B assistant.

WHO SHOULD BUY IT

AI developers, researchers, power users and anyone who specifically wants to run 32B–70B-class models locally.

WHO SHOULD SKIP IT

If your normal workload is an 8B coding assistant, spending this much money on 128GB is unnecessary.

2. MINISFORUM MS-S1 MAX

The MS-S1 MAX uses the same Ryzen AI Max+ 395 platform, but it takes a different approach from the tiny EVO-X2.
Instead of concentrating purely on compactness, Minisforum builds expansion into the machine.
The system can be configured with up to 128GB of LPDDR5X-8000 unified memory and includes a PCIe expansion slot, allowing considerably more flexibility than a conventional ultra-small mini PC.
KEY HARDWARE

- Ryzen AI Max+ 395.
- 16 cores / 32 threads.
- Radeon 8060S.
- 40 GPU compute units.
- Up to 128GB LPDDR5X-8000.
- Up to 50 TOPS NPU.
- Up to 126 TOPS total AI performance according to Minisforum.
- Dual 10GbE.
- Wi-Fi 7.
- PCIe expansion.

PROS

- Huge 128GB unified-memory configuration.
- Excellent memory bandwidth.
- PCIe expansion provides much more flexibility.
- Dual 10GbE is excellent for AI servers and home labs.
- Multiple NVMe options.
- Strong cooling system.
- Suitable for rack or cluster deployments.

CONS

- Larger than conventional ultra-compact mini PCs.
- More expensive than ordinary Ryzen mini PCs.
- Higher power consumption under sustained AI workloads.
- Soldered unified memory cannot be upgraded.
- AMD software ecosystem still requires more attention than CUDA.

WHO SHOULD BUY IT

People who want a serious local AI machine that can eventually become part of a larger homelab or AI cluster.

3. FRAMEWORK DESKTOP WITH RYZEN AI MAX+ 395 AND 128GB

THE REPAIRABLE AI MACHINE
Framework takes the Strix Halo concept and adds something unusual to the AI market: serviceability.
The Framework Desktop can be configured with the Ryzen AI Max+ 395 and 128GB of LPDDR5X-8000 memory.
Framework lists up to 96GB of that 128GB pool as dedicated GPU memory in its standard configuration.
The system also uses a Mini-ITX motherboard and provides two PCIe 4.0 x4 M.2 slots, 5Gb Ethernet and Wi-Fi 7.
Framework has gone further than simply selling a fast mini PC. AMD has demonstrated a four-node cluster of 128GB Framework Desktop systems running a very large local model using ROCm and llama.cpp RPC.

KEY HARDWARE
- Ryzen AI Max+ 395.
- 16 cores / 32 threads.
- Radeon 8060S.
- Up to 128GB LPDDR5X-8000.
- Up to 96GB dedicated GPU allocation.
- 5Gb Ethernet.
- Wi-Fi 7.
- Two M.2 PCIe 4.0 slots.

PROS
- Excellent local-LLM performance.
- 128GB unified-memory option.
- Strong memory bandwidth.
- More serviceable philosophy than most mini PCs.
- Standard Mini-ITX motherboard format.
- Two NVMe slots.
- Excellent platform for Linux experimentation.
- Can be used as a building block for AI clusters.

CONS
- Not as tiny as the smallest mini PCs.
- 128GB memory is soldered.
- Expensive at high configurations.
- AMD ROCm can require more configuration than CUDA.
- AI workloads can generate substantial heat and power consumption.

WHO SHOULD BUY IT

Developers who care about Linux, repairability, experimentation and eventually building a larger local AI system.

4. BEELINK GTR9 PRO

THE HOME-LAB FRIENDLY STRIX HALO MACHINE
The Beelink GTR9 Pro is another compact system built around AMD's Ryzen AI Max+ 395 platform.
Its attraction is similar to the EVO-X2: large unified memory and a powerful integrated Radeon GPU.
But Beelink's design emphasizes connectivity and home-lab usefulness.
With the right 128GB configuration, it becomes a very interesting machine for people who want to run large local models while also using the system as a general-purpose server.

PROS
- Ryzen AI Max+ 395 performance.
- 128GB unified-memory configurations.
- Excellent local AI potential.
- Strong CPU performance.
- Large model capability without a discrete GPU.
- Attractive option for home labs.
- Good networking options on high-end configurations.

CONS
- Expensive compared with ordinary Beelink systems.
- Unified memory is not user-upgradeable.
- Cooling matters under sustained AI workloads.
- ROCm software support is not as straightforward as CUDA.
- Buying a 128GB configuration for small models wastes a lot of potential.

WHO SHOULD BUY IT
Home-lab users who want one machine capable of serving as an AI box, development workstation and general server.

5. BOSGAME M5 AI

THE STRIX HALO ALTERNATIVE
The Bosgame M5 AI is another 2026 mini-PC option built around AMD's high-end Ryzen AI Max+ architecture.
It is particularly interesting because it brings the same fundamental idea into another chassis: combine a powerful Zen 5 CPU, large Radeon integrated graphics and a massive unified-memory pool.
The result is a machine that can be used for local LLMs, AI image generation and general desktop workloads without needing a conventional graphics card.

PROS
- Ryzen AI Max+ platform.
- Large unified-memory configurations.
- Strong integrated Radeon graphics.
- Excellent local AI potential.
- Compact compared with a conventional desktop workstation.
- Strong general-purpose performance.

CONS
- Expensive at high memory capacities.
- Soldered memory.
- Availability can vary by market.
- AMD software support requires more experimentation than CUDA.
- Not the best choice if you specifically need NVIDIA CUDA.

WHO SHOULD BUY IT
Someone who wants Strix Halo performance but finds another manufacturer or configuration more attractive.

6. MINISFORUM AI X1 PRO

THE SWEET SPOT FOR MOST AI DEVELOPERS
Not everyone needs 128GB.
This is where the MINISFORUM AI X1 Pro becomes interesting.
It uses the Ryzen AI 9 HX 370, Radeon 890M graphics and an XDNA 2 NPU.
Unlike the 128GB Strix Halo machines, it uses DDR5 SO-DIMM memory, allowing the user to upgrade the RAM.
MiniPCLab lists 32GB and 64GB configurations and positions the machine primarily for 7B–13B models, with larger models possible depending on quantization and workload.

KEY HARDWARE
- Ryzen AI 9 HX 370.
- 12 cores / 24 threads.
- Radeon 890M.
- 16 RDNA 3.5 compute units.
- XDNA 2 NPU.
- 32GB or 64GB DDR5 SO-DIMM.
- 1TB NVMe in the referenced configuration.

PROS
- Much cheaper than 128GB Strix Halo systems.
- Upgradeable DDR5 memory.
- Strong 890M integrated GPU.
- 50 TOPS-class NPU.
- Excellent for 7B–13B models.
- Good balance between AI and normal desktop use.
- Lower power consumption than the Max+ 395 systems.

CONS
- 64GB memory ceiling is far more restrictive than 128GB unified systems.
- Memory bandwidth is much lower than Strix Halo.
- Not ideal for 70B models.
- No large PCIe expansion platform for a future dedicated GPU.
- NPU support does not automatically translate into faster Ollama inference.

WHO SHOULD BUY IT
Developers who want a serious local coding assistant without spending $1,800 or more.
For many people, this is actually the smarter purchase than a 128GB machine.

7. MINISFORUM UM790 PRO 64GB

THE BUDGET HOMELAB AI MACHINE
The UM790 Pro is older than the latest AI Max machines, but that does not make it useless.
With 64GB of DDR5 and the Radeon 780M, it can handle smaller local models while also serving as a general-purpose home server.
It is particularly attractive when local LLM inference is only one of several jobs.
You can run:
- Ollama.
- Docker.
- Home Assistant.
- Plex.
- NAS services.
- Development environments.
- Small local AI models.

PROS
- Much cheaper than high-end AI mini PCs.
- 64GB memory configuration.
- User-upgradeable SO-DIMM RAM.
- Radeon 780M can accelerate smaller models.
- Low idle power consumption.
- Excellent homelab platform.
- Can run many services besides AI.

CONS
- No dedicated NPU.
- Much slower than the Ryzen AI Max+ platform for LLM inference.
- 70B models are impractical as a primary workload.
- Older CPU/GPU architecture.
- Not the machine to buy if LLM inference is your main priority.

WHO SHOULD BUY IT
Homelab users who want a cheap server that can also run small local AI models.

8. APPLE MAC MINI M4 PRO 64GB

THE UNIX-STYLE ALTERNATIVE
Apple's Mac mini takes a completely different route from the Windows mini-PC ecosystem.
Instead of a traditional CPU plus discrete GPU arrangement, Apple uses unified memory and an integrated GPU tightly connected to the processor.
A 64GB M4 Pro Mac mini therefore becomes surprisingly interesting for local LLM workloads.
The advantage is memory bandwidth and a mature software environment around Apple's Metal framework.
The disadvantage is that you are entering Apple's ecosystem, and many AI tools remain more deeply optimized for CUDA.

KEY CHARACTERISTICS
- Apple M4 Pro.
- Unified memory.
- 64GB configuration available.
- High memory bandwidth.
- Metal GPU acceleration.
- Very compact chassis.
- Excellent efficiency.

PROS
- Excellent unified-memory architecture.
- Very compact.
- Quiet operation.
- Excellent energy efficiency.
- Strong CPU performance.
- Excellent software experience.
- Metal acceleration works well with supported AI applications.
- Excellent everyday desktop computer as well as an AI machine.

CONS
- Memory cannot be upgraded.
- CUDA software does not run natively.
- Some AI projects are still NVIDIA-first.
- Maximum practical model size depends heavily on the memory configuration.
- Higher-memory configurations become expensive.

WHO SHOULD BUY IT
Mac users, developers working with Metal-compatible AI software, and people who want a compact machine that doubles as an excellent everyday computer.

9. NVIDIA DGX SPARK

THE MINI AI SUPERCOMPUTER
This is where the definition of 'mini PC' becomes a little blurry.
The NVIDIA DGX Spark is much closer to a dedicated AI workstation than an ordinary mini PC, but it deserves a place in this conversation because NVIDIA specifically designed it as a compact personal AI system.
And unlike the AMD systems, it comes with NVIDIA's Blackwell architecture and CUDA ecosystem.
The big story is not merely the raw performance.
It is CUDA.
For AI developers, CUDA compatibility can be worth an enormous amount of money because so many frameworks, libraries and research projects are built around NVIDIA GPUs.

KEY HARDWARE
- NVIDIA Grace Blackwell.
- 20-core Arm CPU.
- Blackwell GPU.
- Fifth-generation Tensor Cores.
- Fourth-generation RT Cores.
- 128GB unified LPDDR5x memory.
- Up to 1 PFLOP FP4 AI performance.

PROS
- NVIDIA CUDA ecosystem.
- Blackwell architecture.
- 128GB unified memory.
- Excellent AI software compatibility.
- Designed specifically for local AI development.
- Strong Tensor Core acceleration.
- Excellent for AI researchers and developers.

CONS
- Expensive.
- ARM-based CPU environment can introduce compatibility considerations.
- Not a normal consumer mini PC.
- Unified memory is not user-upgradeable.
- Less attractive for someone who simply wants an inexpensive local chatbot.

WHO SHOULD BUY IT
AI developers who specifically need CUDA, TensorRT, NVIDIA tooling and a compact machine for serious AI experimentation.

10. GMKTEC K8 PLUS

THE PRACTICAL ENTRY POINT
The GMKtec K8 Plus represents a completely different end of the market.
It uses a Ryzen 7 8845HS with Radeon 780M graphics and supports substantial DDR5 memory expansion.
It is not remotely in the same class as a 128GB Ryzen AI Max+ system for large-model inference.
But that is exactly the point.
You do not need a $2,000 machine to experiment with local AI.
For smaller models, coding assistants and general-purpose development, a machine like the K8 Plus can make far more financial sense.

KEY HARDWARE
- AMD Ryzen 7 8845HS.
- 8 cores / 16 threads.
- Radeon 780M.
- DDR5 SO-DIMM memory.
- Up to 96GB supported in some configurations.
- NVMe storage.
- USB4.
- OCuLink.
- Dual 2.5GbE on supported configurations.

PROS
- Much cheaper than high-end AI mini PCs.
- Upgradeable memory.
- Good CPU performance.
- Radeon 780M can accelerate smaller models.
- OCuLink provides an external-GPU upgrade path.
- Good general-purpose computer.
- Useful for experimenting with Ollama and LM Studio.

CONS
- Radeon 780M is far slower than Radeon 8060S.
- No huge unified-memory pool.
- Large models become slow quickly.
- No CUDA unless an external NVIDIA GPU is added.
- Not suitable for serious 70B-class single-machine inference.

WHO SHOULD BUY IT
Someone entering local AI who wants a useful computer first and an AI experimentation machine second.

MODEL-SIZE TIERS
7B–8B MODELS

This is the easiest category.
You can run models such as:

- Llama 3.2 8B.
- Mistral 7B.
- DeepSeek-R1 8B.
- Small Qwen models.

A 32GB machine is already comfortable here.
You do not need a monster workstation.
GOOD CHOICES

- MINISFORUM AI X1 Pro.
- UM790 Pro.
- GMKtec K8 Plus.
- Many other 32GB Ryzen mini PCs.

13B–14B MODELS
This is where 32GB remains useful, but 64GB gives you much more breathing room.
Examples include:

- Code Llama 13B.
- Qwen coder-class models.
- Other 13B–14B reasoning and coding models.

GOOD CHOICE
MINISFORUM AI X1 Pro with 64GB.

30B–34B MODELS
Now memory becomes much more important.
A 64GB system can make this practical, but bandwidth and GPU acceleration begin to matter significantly.
GOOD CHOICES

- Ryzen AI 9 HX 370 systems with 64GB.
- Ryzen AI Max+ 395 systems.
- Mac mini with sufficient unified memory.

70B MODELS
This is where the market changes dramatically.
A 70B model at 4-bit quantization can require roughly 40GB just for the model weights, before accounting for runtime overhead, context and operating-system memory.
A 32GB machine is out.
A 64GB machine may load it with compromises depending on the runtime and offload strategy.
A 128GB unified-memory system is a much more comfortable proposition.
GOOD CHOICES

- GMKtec EVO-X2 AI.
- MINISFORUM MS-S1 MAX.
- Framework Desktop 128GB.
- Other Ryzen AI Max+ 395 128GB systems.
- NVIDIA DGX Spark.