When we deploy AI locally for businesses, the first question is always about hardware. In 2026, the answer is changing. Neural Processing Units (NPUs) (dedicated AI chips built into laptops, phones, and edge devices) are taking over always-on, low-power inference. They draw a few watts where a desktop GPU draws hundreds, which matters for devices that run all day.
This is not about replacing GPUs entirely. It is about knowing when each makes sense, and deploying the right hardware for the right task.
The Core Difference
GPUs throw thousands of general-purpose cores at a problem in parallel. They’re flexible, powerful, and can handle anything from gaming to training 70B models. But they’re power-hungry.
NPUs have dedicated multiply-accumulate hardware baked into silicon: the exact mathematical operation at the heart of every neural network. Having it in hardware instead of software instructions on general-purpose cores makes a massive difference in throughput per watt.
xychart-beta
title "NPU performance claimed by each vendor (TOPS)"
x-axis ["Snapdragon X2 Elite", "AMD Ryzen AI 300", "Intel Core Ultra 200V", "Snapdragon X Elite", "Apple M4"]
y-axis "TOPS (vendor figure)" 0 --> 90
bar [80, 50, 48, 45, 38]TOPS figures are each vendor’s own peak number, measured under different precisions and conditions, so treat them as a rough ranking, not a benchmark.
NPU vs GPU: When to Use Which
| Workload | Best Accelerator | Why |
|---|---|---|
| Always-on voice/camera AI | NPU | Ultra-low power, continuous inference |
| OS-level AI assistant | NPU | Background processing, efficient |
| Light inference (<7B models) | NPU | A few watts instead of a desktop GPU’s hundreds |
| Image generation (FLUX, SD) | GPU | Compute-dense, parallel operations |
| Large model inference (27B+) | GPU | Needs VRAM bandwidth |
| Video AI processing | GPU | High throughput required |
| Fine-tuning/training | GPU | Memory + compute intensive |
| RAG document Q&A | NPU (small model) or GPU (large) | Depends on model size |
Rule of thumb: If the model fits in 8GB and runs continuously, NPU wins. If you need a 27B+ model or are generating images, GPU wins.
The Power Equation
This is where low-power hardware changes the economics of edge AI. Maximum power draw, from each manufacturer’s own specifications:
| Device | Maximum power draw | Source |
|---|---|---|
| NVIDIA Jetson Orin Nano Super | 25 W (top power mode) | NVIDIA |
| Mac mini (M4) | 4 W idle, 65 W maximum, whole machine | Apple |
| GeForce RTX 3080 | 320 W, graphics card alone | NVIDIA |
Worked example, at an assumed EUR 0.20 per kWh and running flat out 24/7 (a worst case; real inference loads are lower): 25 W is 219 kWh a year, about EUR 44; 65 W is 569 kWh, about EUR 114; 320 W is 2,803 kWh, about EUR 561 for the card alone. Put in your own tariff and duty cycle.
For businesses running AI inference 24/7 (customer support bots, document processing, security cameras) that difference is worth including in the hardware decision.
2026 NPU Landscape
| Platform | NPU TOPS (vendor figure) | Best For |
|---|---|---|
| Qualcomm Snapdragon X2 Elite | 80 (Qualcomm) | Laptops, always-on AI |
| AMD Ryzen AI 300 | 50 | Workstations, hybrid |
| Intel Core Ultra 200V (Lunar Lake) | 48 (120 platform TOPS with GPU and CPU) | Enterprise laptops |
| Qualcomm Snapdragon X Elite | 45 (Qualcomm product brief) | Laptops |
| Apple M4 Neural Engine | 38 (Apple) | Mac mini deployments |
| NVIDIA Jetson Orin Nano Super | 67 sparse, GPU + accelerators (NVIDIA) | Embedded edge devices |
Apple’s approach is different: CPU, GPU and Neural Engine share unified memory, so there is no copying between chips.
How This Affects Local Deployments
One caveat before buying for the NPU: today’s local LLM runtimes mostly do not use it. Ollama and llama.cpp run models on the GPU (Metal on a Mac, CUDA on NVIDIA) or on the CPU. The NPU is used by operating-system features and by apps built on the vendor’s own framework. For LLMs, the GPU and memory size still decide what you can run.
Mac mini M4
- GPU (unified memory): runs Qwen 2.5 7B, Gemma 3 4B and, with enough memory, DeepSeek R1 14B through Ollama’s Metal backend
- Neural Engine (38 TOPS): used by macOS and Core ML apps, not by Ollama
- Power: 65 W maximum for the whole machine (Apple)
NVIDIA Jetson Orin Nano Super
- GPU + accelerators (67 sparse TOPS): suited to computer vision and small models
- Power: up to 25 W
- Best for: manufacturing inspection, retail cameras, always-on monitoring
Desktop GPU (e.g. RTX 3080)
- GPU: for LoRA fine-tuning and image generation
- Not ideal for 24/7 inference: 320 W for the card alone
Check Your Hardware’s AI Capabilities
Run these commands to see what your device can do:
# macOS: Check Neural Engine and GPU cores
system_profiler SPDisplaysDataType | grep -A5 "Chipset\|Metal\|Total"
# Check if Ollama can use your hardware
ollama run qwen3:8b --verbose 2>&1 | grep "metal\|cuda\|cpu"
# Quick benchmark: measure tokens per second
time ollama run qwen3:8b "Write a 100-word product description" --verboseMeasure on your own machine: the --verbose output prints the eval rate in tokens per second. Third-party figures for similar boards are collected in our hardware catalog.
The Business Case
Whether a local box pays for itself against a cloud API depends on your volume, your API prices and your electricity tariff, so we do not quote a generic payback period. Our cloud vs local break-even guide has a short script that computes it from your own numbers.
What’s Coming Next
The NPU race is accelerating:
- October 2025: Apple said its M5 chip delivers over 4x the peak GPU compute for AI of the M4, with a neural accelerator in each GPU core
- AI PCs: the major laptop makers now ship NPU-equipped “Copilot+” models
- Qualcomm: the Snapdragon X2 Elite raised its NPU from 45 to 80 TOPS
- Market: Grand View Research, a market-research firm, projects the edge AI market to reach USD 118.69 billion by 2033, growing 21.7% a year from 2026
The trend is clear: inference is moving to NPUs while GPUs focus on training and heavy generation. For SME deployments, this means cheaper, quieter, more efficient AI hardware every year.
Ready to deploy AI on the right hardware? Schedule a free 15-minute assessment: we’ll match your workload to the optimal device.
Related: Hardware Guide | Quantization Guide | Cloud vs Local Costs | Best Local LLMs
Sources: Apple M4 | Apple M5 | Mac mini power (Apple) | Snapdragon X2 Elite (Qualcomm) | Jetson Orin Nano Super (NVIDIA) | RTX 3080 (NVIDIA) | Edge AI Market (Grand View Research)
Next steps
- Compare the power draw of different hardware manufacturers.
- Check the NPU TOPS figures for the latest Snapdragon and AMD platforms.
- Review how current runtimes like Ollama use your GPU or CPU instead of the NPU.
- Evaluate the business case for local hardware based on your specific electricity tariff and volume.
Related reading
- Edge AI Hardware Guide 2026: Jetson vs Mac Mini vs NUC, Real Specs, Real Costs
- Fine-Tune AI Models on Your Own Hardware: The LoRA Guide for SMEs
- Quantization Explained: Run 70B AI Models on Consumer Hardware
Work with us
We size the model and the machine by measuring, not by guessing. If you want to see your own task running on real hardware, book a 15-minute call or see how we work in consulting.