NPU vs iGPU vs GPU for Local AI: Which Hardware Should You Choose?
Local AI can keep prompts, documents and outputs on your own computer, but the label AI PC does not tell you which processor will actually run your workload. A modern system may offer a neural processing unit (NPU), an integrated GPU (iGPU), a discrete GPU, or all three. Each is designed for a different balance of speed, memory, power and software support.
Quick answer: Choose a discrete GPU for the fastest local generative AI and larger models; choose an NPU for efficient, sustained AI features that explicitly support it; choose an iGPU for smaller models and lighter AI workloads when compact size, cost and power matter. The best option is the accelerator your software can use, with enough usable memory for the model.
Microsoft's current Windows ML guidance makes the same workload distinction: NPUs target battery-efficient sustained inference, GPUs target high-throughput image, video and generative AI, and CPUs remain the universal fallback. In practice, that means there is no single winner for every local AI task.
NPU, iGPU and discrete GPU at a glance
| Factor | NPU | Integrated GPU (iGPU) | Discrete GPU |
|---|---|---|---|
| Primary strength | Efficient, sustained neural-network inference | Compact, affordable parallel compute | Maximum throughput and mature AI acceleration |
| Memory | Usually accesses system or shared memory through the platform | Shares system RAM | Uses dedicated VRAM |
| Best fit | Background AI, Windows AI features, supported small models | Small models, media AI, experimentation | LLMs, image generation, video AI, heavier development |
| Main limitation | Application and model support can be narrow | Shared memory bandwidth and capacity | Higher price, power draw, heat and chassis size |
| Mini PC fit | Excellent when integrated into the processor | Common and space-efficient | Uncommon in very small systems; more typical in larger PCs |
What does 'local AI' mean?
Local AI means the model runs on your computer instead of sending every prompt or file to a remote cloud service. After the model is downloaded, supported applications can work offline, reduce recurring cloud inference costs and keep sensitive inputs on the device. Microsoft describes these privacy, offline and cost benefits as core advantages of Windows ML.
Local AI covers very different workloads: a background-blur model, speech transcription, document summarisation, a small local chatbot, image generation and model fine-tuning do not need the same hardware. Before comparing chips, identify the application, model format, precision, memory requirement and supported execution provider.
What is an NPU, and when is it the best choice?
An NPU is a specialised accelerator for neural-network operations. Its main advantage is efficiency: it can run supported AI inference continuously without asking the CPU or GPU to do all the work. That makes it well suited to always-on features such as camera effects, audio cleanup, OCR, translation, summarisation and other compact models designed for the NPU.
An NPU is the best choice when your software explicitly lists support for your processor's NPU execution provider, the workload runs frequently or in the background, and low power or low heat matters more than maximum generation speed. This is especially useful in a Mini PC, where every watt adds to cooling demand.
The limits of NPU TOPS
TOPS means trillions of operations per second, but a TOPS figure is not a universal speed rating. It does not tell you whether a particular model will load, how many tokens per second an LLM will generate, how much memory is available, or whether the application can address the NPU at all. Precision, model architecture, memory movement, runtime and drivers all matter.
Microsoft currently defines a Copilot+ PC around an NPU capable of at least 40 TOPS. By comparison, AMD specifies the Ryzen 9 8945HS at up to 16 NPU TOPS and up to 39 overall platform TOPS. Those are different measures. A PC with a 16-TOPS NPU may accelerate compatible Ryzen AI workloads, but it does not meet the 40+ NPU TOPS Copilot+ threshold.
What is an iGPU, and when is it enough for local AI?
An integrated GPU is built into the processor package rather than installed as a separate graphics card. Like a discrete GPU, it can process many operations in parallel, but it normally shares system memory with Windows and the CPU. That saves space, cost and power - exactly why iGPUs are so common in Mini PCs.
An iGPU is a sensible middle ground for experimenting with small or quantised language models, AI-assisted photo or video features, computer vision and other workloads supported by Windows ML or the application's GPU backend. It also keeps the machine useful for display output, media processing and light gaming when AI is not running.
The trade-off is shared memory. The model, operating system and applications compete for the same RAM, so memory capacity and bandwidth can become as important as the graphics core. For this reason, a 32GB dual-channel Mini PC is generally a more flexible local-AI starting point than a low-memory configuration, although the actual model requirement must still be checked. For a deeper capacity guide, see how much RAM you need in 2026.
What is a discrete GPU, and why is it usually fastest?
A discrete GPU is a separate processor with dedicated video memory (VRAM). Its large parallel compute resources, high memory bandwidth and mature AI software stacks make it the normal choice for demanding local generative AI. NVIDIA's current local-AI guidance positions GeForce RTX systems for developing and testing small AI models, with available VRAM and model size treated as key selection factors.
Choose a discrete GPU when your priority is higher token throughput, faster image generation, larger model capacity, repeated batch inference, AI-assisted video production or development with frameworks optimised for GPU acceleration. The cost is a larger power supply, more heat, more fan noise and usually a bigger enclosure than a conventional Mini PC.
A discrete GPU is not automatically the right purchase for a light private assistant or occasional transcription. If a compact model already meets your needs on an NPU or iGPU, the extra performance may not justify the price and energy use.
Which processor is best for each local AI workload?
| Workload | Best starting point | Why |
|---|---|---|
| Windows AI features and supported background inference | NPU | Efficient sustained operation; feature support is the deciding factor |
| Small local chatbot or document assistant | iGPU or NPU | Compact models may fit shared memory; verify the runtime and model build |
| Image generation | Discrete GPU | High parallel throughput and dedicated VRAM usually matter most |
| Speech transcription and noise reduction | NPU or GPU | NPU favours efficiency; GPU favours speed and broader tool support |
| Large LLM or long context | Discrete GPU | Usable VRAM and memory bandwidth become critical |
| AI development and testing | Discrete GPU | Broader framework support and faster iteration |
| Occasional inference with maximum compatibility | CPU fallback | Runs widely, but is normally slower than supported acceleration |
Decision rule: workload support first, memory second, sustained power and cooling third, headline TOPS last.
How to Choose an AI Mini PC
Choosing an AI Mini PC starts with the software and model you plan to run—not the processor’s advertised TOPS. Before comparing systems, check five factors.
1. Confirm accelerator support.
Find out whether your application supports the system’s NPU, integrated GPU or discrete GPU. Without a compatible execution provider, the workload may fall back to the CPU regardless of the hardware specification.
2. Check usable memory.
An iGPU shares system RAM with Windows and other applications, while a discrete GPU uses dedicated VRAM. For smaller local models and general experimentation, 32GB of system memory offers more flexibility than 16GB, but the model’s published requirements should remain the deciding factor.
3. Match the accelerator to the workload.
Choose an NPU for efficient background inference, an iGPU for lighter AI workloads and compact systems, or a discrete GPU when image generation, larger language models or maximum throughput is the priority.
4. Consider sustained cooling.
Local inference can run for long periods. A Mini PC therefore needs sufficient cooling to maintain processor and graphics performance without excessive throttling or noise.
5. Buy for your non-AI workloads too.
A stronger CPU may matter more for compilation, virtual machines and productivity, while a stronger iGPU also improves media processing and light gaming. AI capability should be one part of the purchasing decision—not the only specification considered.
Where NIPOGI Models Fit
For buyers who want NPU and iGPU capability in one compact system, the NiPoGi G1 combines a Ryzen AI NPU, Radeon 780M graphics and 32GB DDR5 memory. The NiPoGi E3B is a lower-cost iGPU-led option, while the NiPoGi H2 is better suited to CPU-heavy productivity. Check software compatibility and current configurations before choosing a model specifically for local AI.
Five checks before you buy
Software support. Does the application list your NPU or GPU execution provider, driver and operating-system version?
Usable memory. Will the model and its context fit in VRAM or shared system memory after Windows and other applications take their share?
Precision and model build. INT8, INT4 and other quantised models can change memory use and compatibility; use the build recommended by the runtime.
Sustained cooling. Local AI can run for minutes or hours. Mini PC performance depends on the processor's configured power and the chassis cooling design.
Your non-AI workload. Buy for the whole computer. A stronger iGPU may improve media and gaming, while a stronger CPU may matter more for compilation, virtual machines and general work.
Processor suffixes can also change power and cooling expectations. The Ryzen U vs HS Mini PC guide is a useful next step when comparing efficient always-on systems with higher-performance compact PCs.
Frequently asked questions
Is an NPU better than a GPU for local AI?
Not in every workload. An NPU is usually more efficient for supported, sustained inference, while a discrete GPU normally delivers greater throughput and broader support for generative AI. The application's execution provider and memory requirement determine the real winner.
Can an iGPU run a local LLM?
Yes, if the model is small enough for available shared memory and the runtime supports the iGPU. Quantised small language models are more realistic than large models, and performance depends heavily on memory bandwidth, drivers and software optimisation.
Do I need a Copilot+ PC to run AI locally?
No. Microsoft states that Windows ML can accelerate local inference on supported NPUs, GPUs and CPUs, and Foundry Local can use GPU or CPU paths on systems without a Copilot+ NPU. Some Windows AI APIs and features do require Copilot+ hardware, so check the specific feature rather than treating Copilot+ as a requirement for all local AI.
Is 16 TOPS enough for local AI?
It can be enough for compatible NPU workloads, but the number alone cannot predict model speed or compatibility. Microsoft's Copilot+ category requires at least 40 NPU TOPS, while processors below that threshold may still offer useful vendor-supported AI acceleration.
How much RAM does an AI Mini PC need?
There is no universal figure because model size, quantisation and context length vary. For a general-purpose Mini PC that will experiment with smaller local models, 32GB offers more flexibility than 16GB; larger models may require more system memory or dedicated VRAM. Always check the model's published requirement.
Final verdict
For local AI, the best hardware is not the component with the largest marketing number. It is the accelerator that your software supports, backed by enough memory and a cooling system that can sustain the workload.
Choose an NPU for efficient, always-on AI features and supported small-model inference. Choose an iGPU for compact, affordable experimentation and lighter AI alongside everyday graphics. Choose a discrete GPU for maximum speed, larger models and serious generative-AI development.
For most Mini PC buyers, a balanced processor, 32GB or more of appropriate memory, fast storage and verified software support will deliver more value than chasing TOPS alone.
Sources and fact-check references
Microsoft Learn - What is Windows ML?
Microsoft Learn - Phi Silica on non-Copilot+ PCs
Microsoft Learn - Windows AI FAQ
AMD - Ryzen 9 8945HS official specifications
Intel - CPU vs GPU and NPU roles
NVIDIA Developer - Build Local AI with NVIDIA GPUs