The AI accelerator chip space has three major mainstream architectures: GPU, NPU, and TPU. Add the recently emerging LPU (Language Processing Unit), and many developers find it hard to tell them apart.

This article compares them across four dimensions: architectural design philosophy, ecosystem maturity, real-world performance, and deployment cost.

In 2025, the AI accelerator market has become unprecedentedly rich. From NVIDIA's Blackwell to Huawei Ascend 910B, from Google TPU v6 to Groq LPU, developers face more choices than ever before.

But this is both a blessing and a challenge — picking the wrong card means either wasting money or falling short on performance.

This article helps you sort out the selection logic starting from actual workloads.

What's new in this update:

MirrorFrog is officially live! An open-source AI accelerator driver and documentation directory.

Currently cataloged content includes:

• GPU: 13 models, covering NVIDIA CUDA, AMD ROCm, Intel GPU, Apple Silicon, Moore Threads, Biren, and more
• NPU: 9 models, covering Huawei Ascend, AMD Ryzen AI, Qualcomm Hexagon, Apple Neural Engine, etc.
• TPU: 2 models (Google Cloud TPU, Coral Edge TPU)
• LPU: 1 model (Groq LPU)
• IPU: 1 model (Graphcore IPU)
• DPU: 3 models (NVIDIA BlueField, Intel IPU, AMD Pensando)
• FPGA: 3 models (AMD Alveo, Intel FPGA AI, Achronix Speedster)
• ASIC: 16 models, covering Intel Gaudi, Cerebras WSE, Cambricon, Hygon, Enflame, and more