高盛研究：token 需求增长18倍，但 GPU 需求未必同步

增量 token 越来越多来自更便宜的小模型、开源模型或路由模型，而非计算量最大的前沿模型。量化、蒸馏、投机解码、缓存优化等技术进一步降低了每个 token 消耗的 GPU 算力。

因此判断 AI 基础设施周期不能只看 token 增长，还要看 token 量、token 价格与每 token 算力需求的共同作用。

AI is entering a volume economy: because token demand now has to outrun the price collapse underneath it.

Per Goldman Sachs research, total token demand is accelerating (by 18x from December-2025 to Sept-2026 ), while frontier-model token demand is growing much more slowly, at roughly 8-9x on the same index.

the bar for sustaining investment spending getting higher as inference becomes commoditized.

That means the marginal AI token is increasingly coming from cheaper, smaller, open-source, or routed models, not necessarily from the most compute-heavy frontier model. This changes the infrastructure math quite a bit.

That gap is so important for hyperscaler's capex-cycle, because frontier models remain an important source of hyperscaler compute demand, while increasingly competitive open-source models are helping push average token prices down.

So token growth alone is no longer enough to read the AI infrastructure cycle. The relevant variable is the interaction between token volume, token pricing, and the amount of compute required to serve each token.

What really matters is:

tokens consumed × compute required per token

If token volume grows 18x but more of those tokens move to models that need far fewer GPU cycles per token, total GPU demand can grow much slower than token demand suggests. Quantization, distillation, speculative decoding, better caching, smaller models, and model routing all push in that direction.

本条情报归属 AITOP「行业」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Goldman Sachs、token、推理成本、开源模型，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

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