Anthropic IPO 路演披露多芯片战略：Claude 已跑通四大芯片平台

视频中最具体的例子是 AMD：过去接入一个新芯片平台需要数十名工程师投入数月，而今年 Anthropic 首次接入 AMD GPU 时，由内部智能体 Mythos 完成，仅用 3 天就让 Claude 跑起来，移植或编写的内核几乎达到 100%，基本无需人工干预。

Tom Brown 还提出“算力乘数”概念：总有效算力等于物理算力乘以乘数，例如 4x 乘数意味着原本需要 100 单位算力训练的模型只需 25 单位。乘数之间会相互叠加，Anthropic 认为随着接近递归自我改进，Claude 将能自主发现新的算力乘数，使增速进一步加快。去年 9 月他们的故障复盘曾提到仅影响 TPU 部署的 bug，是这一策略的早期线索。

Anthropic's IPO roadshow video Anthropic kicked off their IPO roadshow yesterday, and their roadshow video has some previously unreleased information about how their business works. The section on "Approach to compute" with co-founder Tom Brown is particularly interesting, describing how Anthropic train and serve Claude across AWS Trainium, Google TPUs, NVIDIA GPUs, and AMD GPUs. I'm not sure why there's no official video transcript, but here's one that I extracted using MacWhisper. Highlights below: We train and serve Claude now across all four major chip families. That's only possible because early on we made an unconventional bet on a multi-chip strategy. At the time, many people thought that running AI models on different chips wasn't worth the effort. Each chip needs its own tailored software stack and inference stack, which is expensive and difficult to build. We've now been doing it for years, and we've gotten incredibly good at it. [...] As an example, this year, we started working with AMD GPUs for the first time. In the past, bringing on a new chip platform took dozens of engineers months of work This time, we had Mythos try it out. It got Claude running on AMD in just three days, with basically no human intervention . Claude ported or wrote almost 100% of the kernels. [...] The second driver of our compute advantage is our rate of discovery of compute multipliers. We focus on total effective compute, which is the multiplicative product of physical capacity times the compute multipliers that we discover. Each one we discover makes our pool of total effective compute bigger. For example, say you need 100 units of compute to train a model of a certain intelligence. A 4x compute multiplier means you can use 25 units of compute to train the exact same model. Or you can use those hundred physical units to train an even more intelligent model that would have previously required 400 units. Compute multipliers create a competitive advantage for us that's hard to replicate. They build on each other. Each new multiplier we discover has a positive effect on the multipliers before it. They stack. We believe that this can accelerate even further as we approach recursive self-improvement. Claude will be able to discover compute multipliers entirely by itself , allowing the number of compute multipliers to grow faster and faster. We got a hint at Anthropic's multi-chip strategy last September in their postmortem of three recent issues , where they described bugs that affected just their TPU deployments. Tags: ai , generative-ai , llms , anthropic , gpus , claude-mythos-fable , recursive-self-improvement

This report is categorized under AITOP's "Industry" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Anthropic, Claude, AWS Trainium, Google TPU.

Source attribution: curated from Simon Willison’s Weblog. Cross-source corroboration: 11 independent sources have covered this development (including TestingCatalog, The Rundown AI, The Information), confirming industry-wide signal strength.