Ollama, llama.cpp & On-Device AI

Best Mini PCs for Local AI & LLMs

Run private chatbots, coding assistants and image models at home. For LLMs, memory size and bandwidth matter far more than the NPU's TOPS figure — from 7B models on a Radeon 780M to 70B+ on a 128 GB Strix Halo.

What matters for local ai & llms

Memory capacity sets the model size

A 4-bit quantised model needs roughly 0.6 GB per billion parameters plus context: ~5 GB for 8B, ~10 GB for 14B, ~20 GB for 32B and ~42 GB for 70B. Buy the RAM for the largest model you want to run.

Memory bandwidth sets the speed

Token generation is bandwidth-bound. Dual-channel DDR5 (~90 GB/s) is fine for 7B–14B models; Strix Halo's 256-bit bus (~256 GB/s) is what makes 30B–70B models usable.

The GPU, not the NPU, runs most LLMs

Ollama, llama.cpp and LM Studio run on the iGPU via ROCm or Vulkan. NPUs (XDNA 2, Intel NPU) are useful for smaller vendor-supported models and Windows AI features, but rarely for general LLM inference today.

Software support

AMD iGPUs often need an HSA_OVERRIDE_GFX_VERSION tweak for ROCm or a Vulkan backend; Intel uses OpenVINO or IPEX-LLM. Check your toolchain supports the exact GPU.

Recommended chip categories

Entry: 7B–14B models

Radeon 780M with 32–64 GB DDR5 runs quantised 7B–14B models at conversational speeds — see our Ollama tutorial.

High-Performance & eGPU

Ryzen 7 8845HS / 7840HS Beast

AMD Zen 4 Hawk Point (8C/16T, 5.1GHz)
OCuLink 64Gbps Full USB4 Dual 2.5GbE
Graphics (iGPU)Radeon 780M (12 CUs RDNA 3)
Memory ArchitectureDual DDR5 5600MHz SODIMM
Storage Exp.2x PCIe 4.0 NVMe (Raid 0/1 support)
Idle Power11W – 16W
Est. Barebone Cost$490 - $680
Best Suited For

Native 1080p AAA gaming, external eGPU docking, Ollama local 7B-13B LLM inference.

Mid: up to ~32B

Radeon 890M and up to 128 GB of SODIMM RAM fit larger models; OCuLink adds a discrete-GPU path.

Flagship AI & Workstation

Gorgon Point Ryzen AI 9 HX 470

AMD Zen 5 (12C/24T, 55 TOPS NPU)
Dual USB4 40Gbps OCuLink Dual 2.5GbE
Graphics (iGPU)Radeon 890M (16 CUs, up to 3.1 GHz)
Memory ArchitectureDDR5-5600 SODIMM or LPDDR5X (Up to 128GB)
Storage Exp.2–3x M.2 PCIe 4.0 x4 (chassis dependent)
Idle Power6W – 10W
Est. Barebone Cost$720 - $1,400
Best Suited For

Local LLM inference, 1080p–1440p gaming with upscaling, software builds and creative workloads in a compact desktop.

Intel / OpenVINO

Arc B390 with XMX and a 50 TOPS NPU — strong on OpenVINO-optimised models with very low idle power.

Intel AI & Graphics Flagship

Intel Panther Lake Core Ultra X9 388H / X7 358H

16 Cores (4P + 8E + 4 LPE, 50 TOPS NPU)
Thunderbolt 4 / 5 Dual 2.5GbE WiFi 7
Graphics (iGPU)Intel Arc B390 (12 Xe3-cores)
Memory ArchitectureLPDDR5X-9600 soldered (X-series) or DDR5-7200 SODIMM
Storage Exp.2x M.2 PCIe 4.0 / 5.0 (chassis dependent)
Idle Power5W – 10W
Est. Barebone Cost$850 - $1,600
Best Suited For

Quick Sync media servers and editing, efficient always-on workstations, 1080p gaming on the iGPU, on-device AI with OpenVINO.

Best: 70B–120B models

Up to 128 GB unified memory on a 256-bit bus — the only mini PC that holds 70B-class models entirely in GPU-addressable RAM.

Unified-Memory AI Workstation

Strix Halo Ryzen AI Max+ 395

AMD Zen 5 (16C/32T, 50 TOPS NPU)
Dual USB4 40Gbps 5GbE / 10GbE PCIe x4 slot (select models)
Graphics (iGPU)Radeon 8060S (40 CUs, up to 2.9 GHz)
Memory ArchitectureLPDDR5X-8000 256-bit, soldered (64GB or 128GB)
Storage Exp.2x M.2 PCIe 4.0 x4
Idle Power10W – 18W
Est. Barebone Cost$1,500 - $2,800
Best Suited For

Local LLM inference on 30B–120B models, ROCm/AI development, 1440p gaming without a discrete GPU, heavy multi-core builds.

Target specification

Memory32 GB minimum; 64 GB for 32B models; 128 GB for 70B+
GraphicsRadeon 780M / 890M / 8060S or Arc B390
BandwidthDual-channel DDR5-5600+ or LPDDR5X; 256-bit for large models
Storage1 TB+ NVMe — model files are 5–80 GB each
ExpansionOCuLink if you may add a discrete GPU with dedicated VRAM

Avoid

  • Buying on NPU TOPS — most local LLM tools do not use the NPU.
  • 16 GB configurations — the OS plus a 7B model leaves no headroom.
  • Soldered-RAM models in a smaller size than you will need; they cannot be upgraded.
Check before you buy
  • Size the RAM for your largest target model plus 8–16 GB for the OS and context.
  • Raise the UMA frame buffer in BIOS, or the GTT limit on Linux, so the GPU can address enough memory.
  • Check ROCm or Vulkan support for the exact gfx target before buying.
  • Prefer Linux for the widest backend support and largest GPU-addressable memory.

Mini PCs for local ai & llms

GMKtec

GMKtec K8 Plus

AMD Ryzen 7 8845HS (8C/16T, up to 5.1 GHz)
OCuLink PCIe 4.0 x4 Dual USB4 40Gbps Dual 2.5GbE HDMI 2.1 + DisplayPort 2.1
GraphicsAMD Radeon 780M (12 CUs, RDNA 3, up to 2.7 GHz)
Memory2x DDR5-5600 SODIMM (Up to 128GB)
Storage2x M.2 2280 PCIe 4.0 (Up to 16TB)
Idle Power13W – 15W
Price$409.99 barebone · $919.99 (32GB/1TB)
Chip Category

Ryzen 7 8845HS / 7840HS Beast

MINISFORUM

MINISFORUM AI X1 Pro-470

AMD Ryzen AI 9 HX 470 (12C/24T, up to 5.2 GHz)
OCuLink Dual USB4 40Gbps Dual 2.5GbE Wi-Fi 7
GraphicsAMD Radeon 890M (16 CUs, RDNA 3.5)
Memory2x DDR5-5600 SODIMM (Up to 128GB)
Storage3x M.2 2280 PCIe 4.0 (Up to 12TB)
Idle Power5.5W – 9W
Price$719 barebone · €1,399 (32GB/1TB)
Chip Category

Gorgon Point Ryzen AI 9 HX 470

Beelink

Beelink SER10 MAX

AMD Ryzen AI 9 HX 470 (12C/24T, up to 5.2 GHz)
10GbE LAN USB4 40Gbps (PD + DP) HDMI 2.1 DisplayPort 1.4
GraphicsAMD Radeon 890M (16 CUs, RDNA 3.5, 3.1 GHz)
Memory2x DDR5-5600 SODIMM (32GB or 64GB)
Storage2x M.2 2280 PCIe 4.0 x4 (Up to 8TB)
Idle Power~8W idle (Robtech, via Starry Hope)
Price$1,399 (32GB/1TB) · $1,699 (64GB/1TB)
Chip Category

Gorgon Point Ryzen AI 9 HX 470

GMKtec

GMKtec EVO-T2

Intel Core Ultra X7 358H (16C/16T, up to 4.8 GHz, Intel 18A)
2x USB4 40Gbps 10GbE LAN 2.5GbE LAN OCuLink (PCIe 4.0 x4)
GraphicsIntel Arc B390 (12 Xe cores, 2.5 GHz, 122 TOPS INT8)
MemoryLPDDR5X-8533 onboard (64GB)
Storage2x M.2 2280 (PCIe 5.0 x4 + PCIe 4.0 x1, up to 8TB each)
Idle PowerNot published
Price$2,399.99 (64GB/1TB)
Chip Category

Intel Panther Lake Core Ultra X9 388H / X7 358H

Tutorials

Other use cases