Google’s Ironwood TPUs represent a bigger threat than Nvidia would have you believe

Steven

Google’s Ironwood TPUs represent a bigger threat than Nvidia would have you believe

Summary

Google’s newest TPU v7 “Ironwood” accelerators narrow the hardware gap with Nvidia’s Blackwell GPUs by delivering near-comparable dense FP8 performance and memory specs while leaning on Google’s long-standing advantage: extreme scale. Ironwood offers 4.6 petaFLOPS FP8 compute, 192 GB HBM3e with ~7.4 TB/s bandwidth, and four ICI links for chip-to-chip comms. Where it truly stands out is pod-scale: Google ships pods from 256 up to 9,216 chips and can, in theory, stitch dozens of pods together across its Jupiter network — providing multi-hundred-thousand-accelerator domains that few rivals match in that form factor.

The platform uses a 3D torus mesh plus optical circuit switching (OCS) to reduce switch-related latency and provide flexible partitioning and fault tolerance. That approach contrasts with Nvidia and AMD rack-scale, switch-based designs and makes trade-offs that suit different workloads. Large model builders such as Anthropic are already committing massive TPU deployments, underlining the practical threat to GPU dominance — especially when software and scale are considered alongside raw chip specs.

Key Points

  • Ironwood (TPU v7) delivers ~4.6 petaFLOPS dense FP8, comparable to Nvidia’s B200 (4.5 PF) and close to higher-end GB-series GPUs.
  • Each TPU has 192 GB HBM3e and ~7.4 TB/s bandwidth, putting it in the same class as recent GPU offerings.
  • Interconnect: four ICI Links provide ~9.6 Tbps aggregate; less than some Blackwell chips but designed for Google’s mesh topology.
  • Google’s advantage is scale — pods from 256 to 9,216 chips, and networking (Jupiter + OCS) that can theoretically link many pods into massive clusters.
  • Topology trade-offs: Google’s 3D torus + OCS reduces switch latency and enables flexible pod slicing, while GPU vendors use flatter switch fabrics with lower hop counts for some workloads.
  • Major customers (eg. Anthropic) are deploying large TPU fleets, showing real-world demand beyond Google’s internal use.
  • Performance is now close enough that software, tooling and system-level scale will often decide which accelerator ecosystem wins for a given customer.

Context and Relevance

This matters because the AI-infrastructure race is no longer just about single-chip peak FLOPS. Google has combined competitive per-chip performance with datacentre-scale networking and switching choices that let it deliver very large, tightly coupled TPU clusters. For organisations planning model training or inference at scale, that changes the calculus: you must weigh raw GPU features against the practicality and economics of massive TPU pods, the networking topology that fits your workload, and the software ecosystem.

Why should I read this?

Short version: if you care about AI compute — whether you pick GPUs, TPUs or both — this is a wake-up call. Google’s Ironwood isn’t just another chip; it’s a reminder that scale+networking+software can beat a marginal FLOPS lead. Read it to know how procurement and architecture choices might shift next year.

Author style

Punchy: the piece makes a clear, forceful case — Ironwood closes the gap to Blackwell and leverages Google’s pod-scale strengths. If you’re responsible for AI infrastructure, it’s worth digging into the details rather than shrugging it off as vendor hype.

Source

Source: https://go.theregister.com/feed/www.theregister.com/2025/11/06/googles_ironwood_tpus_ai/