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Independent reviews of GPUs, Mini PCs, and AI accessories — benchmarked for LLM inference, Stable Diffusion, and local AI workloads.
The Apple Mac Mini M4 Pro is the best compact AI workstation for local LLM inference in 2026. With up to 64GB of unified memory accessible at 273GB/s and a 14-core CPU, it can run 70B parameter models quantized to 4-bit with no external GPU required.
The GMKtec NucBox M5 Pro is the best budget entry point for local AI inference in 2026. Powered by an AMD Ryzen 9 processor with Radeon 780M integrated graphics, it runs 7B models via Ollama and supports Windows 11 with full CUDA-compatible tooling via ROCm.
The NVIDIA RTX 4090 is the fastest consumer GPU for local AI in 2026. With 24GB of GDDR6X VRAM at 1,008 GB/s bandwidth and 16,384 CUDA cores, it runs 70B quantized models at 15–25 tokens/second and generates SDXL images in under 2 seconds — no other consumer GPU comes close.
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Three principles that determine local AI performance
01
VRAM or unified memory determines which models you can run. 7B needs ~4–8 GB; 13B needs ~8–16 GB; 70B needs ~40 GB+.
02
Tokens/second scales with memory bandwidth. RTX 4090 at 1,008 GB/s runs 70B roughly 15× faster than a budget mini PC at 68 GB/s.
03
macOS + Ollama is zero-friction. NVIDIA on Windows/Linux has the broadest software support. AMD GPU = Linux + ROCm only.