NPU vs GPU for AI: what can Snapdragon, Lunar Lake, Strix, and Apple chips actually run?

NPU comparison table pulled live from our sourced hardware database, plus an honest assessment of what NPU TOPS buy you.

No current laptop NPU runs large local LLMs faster than a desktop GPU. As of August 2026, mainstream local-LLM stacks (llama.cpp, Ollama, LM Studio) execute on the integrated GPU or CPU, not the NPU, and token generation is bounded by memory bandwidth: laptop SoCs offer 120-273 GB/s where a GeForce RTX 5090 offers 1792 GB/s. What NPUs do well is small, ONNX-optimized models — transcription, background effects, Copilot+ features — at low power.

Which AI-PC NPUs are available, and how do they compare?

The table covers the chips that define the Copilot+ era: Qualcomm Snapdragon X, Intel Lunar Lake, AMD Strix Point, and Apple's M3/M4 Neural Engines. Values come from vendor datasheets recorded in our database; every row links to a product page with its source.

Apple Neural Engine figures cover M3/M4 families in our database; M-series chips run local LLMs through the GPU on unified memory via MLX or Metal, not through the Neural Engine. For AMD's bigger Strix Halo class (Ryzen AI Max+ 395), see our best AI laptops guide.

What is the difference between an NPU and a GPU for AI workloads?

An NPU is a fixed-function accelerator tuned for low-precision (INT8/INT4) matrix math on small models at low power. A GPU is a general-purpose parallel processor with high-bandwidth dedicated memory. For AI PCs this has a practical consequence: NPUs accelerate the small models behind OS features (audio transcription, background blur, frame interpolation, Copilot features), while the heavy lifting in llama.cpp-style LLM inference happens on the GPU or CPU because porting a model to the NPU requires per-model ONNX optimization with DirectML/Windows ML. On Apple Silicon the same logic applies — MLX and llama.cpp use the Metal GPU over unified memory, and the 16-core Neural Engine mostly serves Apple's own frameworks.

Can a Snapdragon X Elite or Lunar Lake laptop run local LLMs?

Yes — within memory and bandwidth limits. A Snapdragon X Elite with 64 GB of LPDDR5x runs 8B-class models at Q4_K_M comfortably (Llama 3.1 8B needs about 5.8 GB) and 14B models in constrained setups, at speeds capped by its 135 GB/s of bandwidth. Intel Lunar Lake tops out at 32 GB of on-package memory, which fits 8B-class models but not 27B+ models. AMD Strix Point with up to 96 GB and Apple M4 Pro with 64 GB unified memory go further; Apple's 273 GB/s (M4 Pro) is the highest laptop bandwidth in this table. Detailed fit lists are on every model page — each one lists edge and NPU devices that meet its memory requirement.

Which laptop should I buy for local AI: NPU or GPU?

Buy by memory capacity and bandwidth first, NPU TOPS last. If local LLMs are the priority, choose 64 GB+ of unified memory (Apple M4 Pro, Snapdragon X Elite 64 GB) or a laptop with a discrete NVIDIA GPU — see our best laptops for LLMs comparison. For image generation, a discrete GPU with 16 GB+ VRAM wins decisively; our laptops for Stable Diffusion guide covers those. NPU TOPS matter if you specifically want long-battery on-device features, not raw local-AI throughput.

Where do these NPU numbers come from?

Every row in the table is stored in our hardware database with a link to the vendor datasheet it came from — Qualcomm product briefs, Intel and AMD product pages, and Apple's platform specification document. TOPS claims are marketing-peak values; we treat them as marketing figures, not measured performance, per our methodology.