Answer first: Choose the accelerator your application actually supports. NPUs can be efficient for packaged, supported workloads; iGPUs offer shared-memory flexibility; eGPUs provide discrete GPU capability but add cost, bandwidth limits and compatibility work.
Last reviewed: October 13, 2026. Mini PC Land has not hands-on tested every configuration discussed here. Recommendations are based on documented platform capabilities, official compatibility information and workload requirements. Confirm the exact model and configuration before buying.
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- Match capacity to the real workload.
- Verify the exact processor, ports and upgrade limits.
- Check software support before buying for acceleration.
- Keep backups and a recovery path.
NPU: efficient but application-dependent
An NPU is designed for neural-network operations at low power. Its advertised TOPS is not a universal application benchmark. The model format, precision, operators, compiler and application must all support the hardware. Buy for a named supported workflow, not for the NPU number alone.
iGPU: accessible shared memory
Integrated graphics use system memory, which can allow a model larger than the VRAM of an entry GPU. The tradeoff is shared bandwidth and competition with the CPU and operating system. Runtime support varies: some paths use Vulkan, SYCL, HIP or platform-specific APIs. Verify the exact device and backend.
eGPU: capability with connection costs
A discrete GPU in an external enclosure provides dedicated VRAM and mature acceleration when the software supports it. USB4 or Thunderbolt connections offer fewer PCIe lanes than a desktop slot. OCuLink can provide a more direct link but is less convenient and usually not hot-plug friendly. Enclosure, power supply and cable quality add cost.
Software decides the winner
llama.cpp lists multiple accelerator backends, including CUDA, HIP, Metal, Vulkan and SYCL. AMD publishes a changing ROCm compatibility matrix. Treat those documents as compatibility gates. A fast accelerator with no supported runtime is slower than a modest device that works reliably.
Decision table
Choose an NPU for an application that explicitly supports it and values efficiency. Choose an iGPU for experimentation, modest models and shared-memory flexibility. Choose an eGPU when you need a supported discrete GPU, already own compatible hardware or must keep the mini PC form factor. Choose a desktop when maximum GPU bandwidth, cooling and upgradeability dominate.
Measure the complete system
Compare time to first token, generation speed, maximum workable context, power at the wall and sustained behavior—not only theoretical compute. Include enclosure and power-supply cost when judging an eGPU against a desktop tower.
Read Best Mini PCs for Local AI and Mini PC vs Desktop for Local AI.
Frequently asked questions
Are NPU TOPS comparable across vendors?
Not reliably. Precision, supported operations, software and measurement methods differ.
Can an iGPU use all system RAM?
No. The OS and applications need memory, and firmware or driver limits may restrict graphics allocation.
Is OCuLink faster than USB4 for an eGPU?
It can expose more direct PCIe bandwidth in some implementations, but real performance and compatibility depend on the exact system, link and GPU.
Sources and update notes
This guide uses official documentation where a platform capability is stated. Compatibility changes, so follow the linked source and verify the current release before purchase or deployment.