---
type: "Topics"
locale: "en"
url: "https://longbridge.com/en/topics/40244845.md"
description: "Recently, the various architectures of AI chips have been widely discussed again, and different technical paths like CPU/GPU/TPU/NPU/LPU have been brought up for extensive discussion.First, the big picture: AI has now shifted from "large-scale training" to "large-scale inference + agentic AI + edge computing (including terminals like phones, cars, factory sensors, IoT devices)".The training phase is still dominated by GPU/TPU, but inference scenarios are more sensitive to low latency, low power consumption, and cost; therefore, edge devices are driving the development of single-watt NPUs, while cloud inference requires extreme throughput and deterministic latency, giving CPU, NPU, and LPU renewed advantages in their respective niches.CPU is versatile but slow; GPU has thousands of parallel cores; TPU uses systolic arrays for specialization; NPU is for low-power edge (mobile-level); LPU uses all on-chip SRAM with zero cache latency (but requires multi-chip interconnect).The overall market is still experiencing explosive growth: the AI hardware market is close to $60 billion in 2024 and is still expanding rapidly in 2026.The core is everyone is looking for "more efficient than GPU" alternative paths.So as long as AI infrastructure is still being built on a large scale, the leaders in various technical paths like CPU/GPU/TPU/NPU/LPU will likely still have significant room for growth.#Google #Intel #Nvdia are all worth buying some."
datetime: "2026-04-28T04:47:06.000Z"
locales:
  - [en](https://longbridge.com/en/topics/40244845.md)
  - [zh-CN](https://longbridge.com/zh-CN/topics/40244845.md)
  - [zh-HK](https://longbridge.com/zh-HK/topics/40244845.md)
author: "[美股金虫哥](https://longbridge.com/en/profiles/14300122.md)"
---

# Recently, the various architectures of AI chips ha…


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