GPU vs. ASIC，胜负已定？｜GS

第四维的梦想01-21

专注分享财经信息的专业社区，喜欢请点击下方名片关注。昨晚在《整体性估值压缩期？关于CPO？关于PCB？关于 GJD 筹码？》里讲了 GJD 筹码，也重点说了 688，感兴趣可以一读。高盛针对投资者关于 GPU 与 ASIC 路线之争的提问，尝试用量化框架把不同芯片厂商方案的性能与成本动态放到同一张坐标系里比较。他们搭建了一条推理成本曲线，用来衡量不同加速器在单位推理输出上的成本变化，并据此讨论通用...

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688，感兴趣可以一读。高盛针对投资者关于 GPU 与 ASIC 路线之争的提问，尝试用量化框架把不同芯片厂商方案的性能与成本动态放到同一张坐标系里比较。他们搭建了一条推理成本曲线，用来衡量不同加速器在单位推理输出上的成本变化，并据此讨论通用...</p>\n\n<a href=\"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260121104304953da308&s=b\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"IE00B4YYXB79.USD","symbol_name":"PIMCO BALANCED INCOME AND GROWTH \"E\" (USD) ACC","start_time":0,"source_url":"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260121104304953da308&s=b","article_id":"2605121609","we_media_id":null,"thumbnails":[],"rights":{"source":"tencent","url":"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260121104304953da308&s=b","rn_cache_url":null,"customStyle":"body{padding-top:10px;}#news_title{font-weight:bold;#titleStyle#;}#news_description span{font-size:12px;#descriptionStyle#;}.footer-note{#statement#}","selectors":".mod-LoadTzbdNews, body","filters":".relate-stock, 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70% 左右，在绝对成本维度上与 Nvidia GB200 NVL72 处于大致相当甚至略优的水平。与此同时，Nvidia 仍被认为在产品导入节奏上保持领先，time to market 继续构成优势，而 CUDA软件生态仍是企业客户侧的重要壁垒。相较之下，Amazon 的 Trainium 与 AMD 相关方案被估算仅实现约 30% 的成本下降，在绝对成本层面落后于 Nvidia 与 Google。对于 2026 年后期，高盛预计 AMD 的机架级解决方案在算力性能上会更具竞争力，有望在对比维度上更接近 Nvidia Rubin 与 Google TPU。自 AWS re Invent 之后的产业链调研显示，Trainium 3 与 Trainium 4 可能具备更强的性能表现，从而修正 Trainium 2 的部分挑战，这一进展将持续跟踪。进一步的降本路径上，高盛把网络 内存 与封装技术的进步视为关键变量。在计算芯片面积已经接近光罩尺寸极限的背景下，继续压低单位成本更可能主要来自互连与系统级要素的升级，包括更先进的网络方案 更高效的内存体系以及更高密度的封装技术。同时需要注意方法论边界。相关结论基于当前时点的算力表现快照，并未充分纳入软件优化带来的增益，也未完整计入网络 内存 与封装后续迭代对性能与成本的抬升效应。该方法更偏向以加速器算力为核心度量，因此对系统级升级带来的整体性能提升覆盖有限。（长按图片可选择 翻译 选项，查看中文版）核心结论：我们认为 Google 的 TPU 在原始算力表现上已经取得显著进展，使其在推理应用中能够与 Nvidia 的 GPU 方案更为接近地竞争，而 AMD 与 Amazon 的 Trainium 目前仍落后于这些市场领先者。我们认为 Nvidia 仍保有更快的产品上市节奏优势，这一点叠加其 CUDA 软件生态壁垒，使其在短期内凭借快速的创新节奏仍有望维持加速器市场的领导地位。我们预计 Google 与 Broadcom 的 TPU 将在 Google 内部工作负载中继续提升份额，同时也会被那些拥有足够内部软件人才、能够在 TPU 与 ASIC 平台上进行开发的客户所采用，例如Anthropic。更长期来看，我们看到定制 ASIC 在内部工作负载中的占比将不断提高，并预计头部云厂商会选择性地将其内部自研方案调整后提供给外部客户使用。随着原始算力表现逐步触及物理极限，并且加速器芯片已经受到光罩尺寸限制，我们预计后续的性能与成本改善将主要由网络、内存与封装领域的创新来驱动。我们认为 Nvidia 与 Broadcom 在这些方向上最具优势，能够更好地利用相关技术进展。Nvidia 在研发投入上显著高于竞争对手，通过 Mellanox 业务在网络领域具备强势布局，同时也开始在内存技术上推出创新，例如其 context memory storage controller 产品。Broadcom 也持续以其业内领先的处理器与加速器方案保持市场领先，并拥有行业领先的以太网网络能力与 SERDES 技术积累。我们认为，围绕 Nvidia 竞争护城河可持续性的结构性疑问仍在压制其估值倍数。推理应用的显著降本被视为推动更广泛采用并支撑 AI 生态长期盈利能力的关键前提。在定制 ASIC 尤其是 TPU 已经实现明显降本并在成本层面逐步向 Nvidia 方案靠拢的背景下，尽管 Nvidia 的毛利率仍显著领先于竞争对手，但头部云厂商资本开支仍处于高位且核心大模型业务的盈利能力仍未清晰兑现，Nvidia 的估值倍数因此持续承压。作为对照，过去两年 NVDA 相对 AI 芯片同业中位数估值水平约有 25% 的折价，而在回看过去五年时其估值通常处于约 20% 的溢价。若 Nvidia 的估值折价要收敛甚至重新回到溢价区间，我们认为需要看到以下因素中的若干组合出现。其一，基于 Blackwell 训练出的模型在大模型性能基准上展现出具有说服力的性能跃升，时间窗口最早可能在 2026 年 3 月。其二，Nvidia 相对于竞争方案的单 token 成本显著更低。其三，企业场景中 AI 的变现路径得到实质性验证与落地。我们正密切关注头部云厂商以及 AMD 在 AI 软件层面的进展。即便竞争对手持续完善其硬件技术栈以提升硬件性能，我们仍认为CUDA 以及成熟丰富的开发者生态是 Nvidia 维持竞争优势的关键因素。原因在于，将工作负载迁移到其他竞品方案上的能力目前仍相对受限，尤其是在企业客户群体中更是如此。从长期来看，我们认为 GPU 与 ASIC 的争论可能沿着四种潜在情景之一展开。不过，无论哪一种情景，ASIC 都会在不同程度上受益。情景一是企业端与消费端 AI 的渗透都相对有限。如果 AI 的大规模采用仍局限于少数应用场景，例如编码、后台流程自动化等，行业整体资本开支水平可能趋于降温。虽然这对 GPU 与 ASIC 都是偏负面的结果，但由于应用形态演进不足，工作负载更容易标准化，ASIC 的采用速度在这种环境下反而可能更快。情景二是消费端 AI持续扩张，但企业端 AI 渗透仍然有限。在这一情景下，从 Nvidia 的视角看最重要的是继续保持在训练市场的主导地位，因为工作负载可能逐渐趋于静态，并且更集中在头部云厂商的使用场景中，这种集中化与稳定化预计会推动更多 ASIC 的采用。情景三是消费端 AI继续扩张，同时企业端 AI 取得中等程度的渗透。与情景一类似，Nvidia 最关键的仍是维持其训练市场的主导地位，但在企业客户侧预计会出现更多增量收入机会，且在高份额基础上贡献更可观的增量。情景四是消费端与企业端 AI 都在更广泛的用例集合中实现强劲扩张，这是最乐观的情景。在这一情景下，随着多模态模型、物理 AI 等应用扩展，训练强度将保持在高位，头部大模型提供商与初创公司最终有望实现盈利。我们认为这对 Nvidia 最为有利，因为其在训练市场的主导地位会进一步强化其产品导入节奏优势与 CUDA 生态壁垒。与此同时，随着更多工作负载达到规模化，ASIC 也将受益，但其份额提升速度可能相对更慢。往期精彩回顾《当逻辑来敲门，新高是其次，更重要的是...》（注意保存）《存储 HBF 将会超越 HBM？》（闪迪存储）《整体性估值压缩期？关于CPO？关于PCB？关于 GJD 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