Google brings custom silicon to GA on GCP: what product teams should do next
Google announced general availability on Google Cloud Platform of new custom silicon for inference and agentic workloads. The headline pieces: Ironwood TPUs (Gen 7) arriving in the coming weeks, plus new Arm-based Axion instances with N4A now in preview and C4A metal coming soon in preview.
What's new
Ironwood TPUs (Gen 7) target large-scale model training, complex reinforcement learning, and high-volume, low-latency inference. Google reports up to 10x peak performance over TPU v5p and more than 4x better performance per chip for both training and inference versus TPU v6e (Trillium). Google describes Ironwood as their highest-performance and most energy-efficient custom silicon so far.
Scale is notable: up to 9,216 chips in a superpod with 9.6 Tb/s Inter-Chip Interconnect and access to 1.77 PB of shared HBM. That level of bandwidth and memory aims to reduce data bottlenecks for very large models and agentic systems with heavy tool-use and context switching.
Axion (Arm-based) compute brings a second general-purpose Axion VM to preview: N4A. Google cites up to 2x better price-performance than comparable current-gen x86 VMs. N4A targets microservices, containerized apps, open-source databases, batch, analytics, dev environments, experimentation, data prep, and web-serving for AI apps. C4A metal-Google's first Arm bare metal instance-will follow in preview.
Ecosystem signals: Anthropic plans to access up to 1 million TPUs for Claude training. As their head of compute put it, Ironwood's gains in inference and training scalability help them meet growing demand while maintaining speed and reliability for customers.
Why this matters for product development
- Lower latency, higher throughput: Agentic and tool-using features drive unpredictable traffic patterns. Ironwood's interconnect and HBM footprint help keep tail latency in check when your agents chain multiple steps.
- Cost-pressure relief: If N4A delivers the claimed price-performance, shifting microservices and data prep to Arm can free budget for model training and inference.
- Scale without re-architecture: Superpod scale and shared HBM can reduce expensive sharding or cross-node chatter for large context windows and retrieval-heavy systems.
How to evaluate (fast path)
- Define target SLOs: Set concrete latency and throughput targets for your highest-traffic inference paths (P95/P99). Use them to choose between GPUs, existing TPUs, and Ironwood.
- Run a bake-off: Replicate a real workload-prompt + RAG + tools-and test on current infra vs Ironwood when available. Track QPS, tail latency, and dollars per 1,000 requests.
- Model lifecycle: If you fine-tune or do RL (RLAIF/RLHF), size training windows on Ironwood. If you only serve models, weigh Ironwood for peak events and agent chains.
- Arm readiness: Pilot N4A for 2-3 services with clear CPU bottlenecks. Validate container base images (aarch64), CI/CD multi-arch builds, and library compatibility.
Practical notes for engineering managers
- Software stack: Confirm XLA/JAX or PyTorch XLA support, frozen container images, and profiling tools for TPUs. Plan for observability at the pod/superpod level.
- Data path: Keep your feature stores, embeddings, and caches close to the TPU pods. Test end-to-end throughput, not just model FLOPs.
- Quotas and regions: Lock in quotas ahead of launches. Validate region availability for Ironwood and Axion before committing roadmaps.
- Arm porting: Audit native extensions (Python wheels, Node addons, database drivers). Ensure multi-arch images in your registry and performance parity tests.
- Cost accounting: Track cost per successful action (not per token). For agents, include tool calls, function execution, and retries.
Where this fits in your architecture
- Use Ironwood for large-scale training, RL, and high-traffic inference where tail latency or context size is a blocker. Consider it for agent frameworks that chain multiple tools.
- Use N4A to shift general compute-microservices, ETL, feature engineering, dev/test-off expensive x86. Reinvest savings into model serving capacity.
- Use C4A metal (preview soon) when you need direct hardware control for specialized runtimes, low-level tuning, or compliance constraints.
Business case highlights
Google positions TPUs as the backbone of its AI Hypercomputer-integrating compute, networking, storage, and software. Referencing an IDC report, they cite a 353% three-year ROI, 28% lower IT costs, and 55% more efficient IT teams on average for customers using this stack.
If those numbers hold for your workload profile, expect budgets to shift: Arm for baseline compute efficiency; Ironwood for peak training and inference density; and a thinner x86 footprint.
30-day action plan
- Pick one production inference path and schedule a controlled load test on Ironwood at GA.
- Migrate two CPU-bound services to N4A preview. Measure cost per request and P95 latency.
- Set infra SLOs for agentic features (latency budgets per tool call, max chain depth).
- Update your CI to build multi-arch images and run Arm-specific integration tests.
- Pre-negotiate quotas and region placements for both TPUs and Axion instances.
Useful resources
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