If 2024 was the year of the "AI PC" and 2025 the year of agents, 2026 is turning out to be the year local inferenceLocal InferenceRunning AI models on your own hardware becomes a serious purchasing decision. The reason has a three-letter name: NPU (Neural Processing UnitNPU (Neural Processing Unit)A chip specialized in accelerating neural network inference with maximum energy efficiency), the specialised chip that runs neural networks at a fraction of a GPU's power draw. In this guide we review the five most relevant NPU units of 2026 and 2027, ordered from pocket to datacenter, and what each one means for a company that wants AI without giving up its data.
1. Tiiny AI Pocket Lab: an AI supercomputer in 300 grams
Unveiled at CES 2026, the Tiiny AI Pocket Lab is the most radical proof of how far miniaturisation has come: a 12-core ARM v9.2 CPU paired with a discrete ~160 TOPSTOPS (Tera Operations Per Second)Unit that measures the raw performance of an AI accelerator: trillions of operations per second NPU, 80 GB of LPDDR5X and a 1 TB SSD in a 300-gram device drawing about 65 W. The headline? It runs LLMs of up to 120 billion parameters completely offline, with one-click deployment of open models such as GPT-OSS, Llama, Qwen or Mistral. Starting at around $1,400, it puts a "private AI lab" within reach of any professional.
2. NVIDIA Jetson AGX Orin / Thor: the embedded standard
In robotics, computer vision and industry, the Jetson family remains the reference: up to 275 TOPS in the AGX Orin and a generational leap with Thor for robots and autonomous machines. Its advantage isn't just silicon but the most mature software ecosystem on the market (CUDA, TensorRT, Isaac). It is the safe choice when AI has to live inside a machine.
3. Hailo-10H: generative AI at the edge on 3 watts
Israel's Hailo has achieved something remarkable with the Hailo-10H: 40 TOPS INT8 with 8 GB of dedicated RAM at around 3 W. Available as M.2 and USB modules and as the brain of 2026's Raspberry Pi AI HAT+ 2, it brings small LLMs and vision models to devices where only a microcontroller used to fit. For industrial IoT and retail, it is currently the TOPS-per-watt ratio to beat.
4. Axelera AI Metis: industrial vision at 214 TOPS
Europe's Axelera AI targets industrial vision with Metis, an M.2/PCIe accelerator delivering up to 214 TOPS INT8 at 3.5 to 9 W. Sub-100 ms latency without stealing RAM from the host system: exactly what production lines, logistics and automated quality control ask for.
5. Qualcomm AI200 and AI250: the NPU reaches the rack
The most important move for the datacenter comes from Qualcomm: its AI200 (2026) and AI250 (2027) accelerators, built on rack-scale Hexagon NPUs. The AI200 packs 768 GB of LPDDR memory per card with direct liquid cooling; the AI250 introduces a near-memory architecture promising over 10x the effective bandwidth. The stated goal: the best cost per inference per watt on the market, going head to head with NVIDIA and AMD racks.
And the sixth: the NAiOS rack NPU unit
At NAiOS we believe this wave changes the rules of enterprise AI, and we don't intend to just watch it: NAiOS Labs is preparing its own custom rack-mounted unit built on NPUs, designed to be installed in dedicated datacenters. It is the natural evolution of our Dedicated on-site mode: modern AI hardware (GPU/NPU) inside the premises — the client's or NAiOS's — evaluated case by case by NAiOS Labs, with maximum sovereignty: AI and data never physically leave the company.
Conclusion
From the Pocket Lab's 300 grams to an AI200 rack's 160 kW, NPUs have covered the entire spectrum between pocket and datacenter in two years. For business the message is clear: sovereign AISovereign AIStrategic priority for countries to have their own AI infrastructures is no longer a luxury. To understand the term in depth, see the NPU entry in the NAiOS Wiki; and if you want to explore which mode fits your organisation, let's talk.
