AI推理芯片是专门为大模型推理场景设计的加速芯片，与追求极致算力与带宽的训练芯片不同，推理芯片更关注单位功耗下的Token吞吐量、延迟确定性与多模型并发能力，核心是以低精度计算与大容量片上存储换取推理效率，能力包括低精度计算单元、专用Transformer加速与多芯粒互联。它解决的是通用GPU跑推理成本高、并发承载有限的问题，并不解决大模型规模持续增长带来的算力需求本身的问题。
AI推理芯片的核心是单位成本下的吞吐，它的作用是：
- 让单位功耗下的产出显著提高。低精度计算与专用加速单元让每瓦性能成倍提升，规模化部署时电力成本明显下降。低精度矩阵运算与专用算子把同等功耗下的有效算力进一步释放，规模化部署时单位推理成本随之下降。
- 让响应延迟可预期。专用加速单元针对自回归生成过程做了专门优化，首字延迟与逐词生成速度的波动被控制在业务可接受的范围内。显存与算力在多租户之间被高效切分，线上服务的整体承载能力随之提升。
- 让多模型并发更容易支撑。显存与算力在多租户之间被高效切分，线上服务的整体承载能力提升。
- 让总体拥有成本下降。专用硬件在高频推理场景的性价比优于通用卡，采购与运维的规模效应显现。
总之，AI 推理芯片可以帮助企业实现在线 AI 服务从算力瓶颈到成本可控的转变，让大规模并发调用与高频轻量推理在单位成本上成立，是推理侧基础设施走向专业化的关键一步。
翻译
AI inference chips are accelerators designed specifically for large model inference, differing from training chips that pursue peak compute and bandwidth in focusing on token throughput per watt, latency determinism and multi-model concurrency. Its core is trading low-precision computation and large on-chip memory for inference efficiency, with capabilities covering low-precision compute units, dedicated transformer acceleration and chiplet interconnect. It solves the problems of high inference cost and limited concurrency on general-purpose GPUs, but it does not solve the compute demand arising from continuously growing model sizes.
At the heart of AI inference chips is throughput per unit cost, which can help companies achieve the following:
- Substantially raise output per watt. Low-precision computation and dedicated acceleration units multiply performance per watt, visibly cutting power cost at deployment scale.
- Make response latency predictable. The inference path is optimized for generation, keeping time-to-first-token and generation speed variation within business-acceptable bounds.
- Make multi-model concurrency easier to support. Memory and compute are partitioned efficiently across tenants, raising overall carrying capacity of online services.
- Bring total cost of ownership down. Specialized hardware beats general GPUs in high-frequency inference, with procurement and operational scale effects emerging.
In short, AI inference chips can help enterprises shift online AI services from compute bottleneck to controllable cost, make large-scale concurrent calls and high-frequency lightweight inference economically viable, and are a key step toward specialization of inference-side infrastructure.
