Google TPU v7 launch and Marvell inference chip partnership

The headline reaction to the Marvell talks has been to read them as Google walking away from Broadcom, but the underlying structure tells a more deliberate story. Google is not replacing Broadcom; it is adding a third design partner to a supply chain that already includes Broadcom for high-performance chip variants, MediaTek for cost-optimized 'e' variants at 20 to 30 percent lower cost, and TSMC for fabrication. The Marvell scope, as reported, is narrow: a memory processing unit to work alongside existing TPUs and a new TPU built specifically for AI inference.

That structure looks much more like an automotive-style tiered supplier model than a binary vendor swap. Each partner competes for a defined slice — high-end accelerators, cost-optimized variants, memory-side silicon — rather than for the entire program. The result is leverage: Google gets cross-checks on pricing, schedule, and roadmap from multiple ASIC houses while keeping a single fabrication partner. JPMorgan's pushback that 'Broadcom remains the clear incumbent in TPU-related design work' fits this picture, as does Broadcom's separately announced long-term TPU and networking agreement with Google through 2031, which would be incompatible with an actual divorce.

Ironwood is the first TPU Google has explicitly designed for inference, and the specs explain why an inference-only chip suddenly makes sense. Each chip delivers 4,614 TFLOPs and ships with 192 GB of HBM at 7.37 TB/s of bandwidth, while a full pod links 9,216 liquid-cooled chips for 42.5 Exaflops across nearly 10 MW of power. Google says Ironwood is roughly 10x peak performance versus TPU v5p, more than 4x per-chip versus Trillium, and delivers 2x performance per watt versus Trillium and roughly 30x more power efficient than the original Cloud TPU.

Those numbers make Anthropic's commitment to use up to one million TPUs — backed by tens of billions of dollars in spend, well over 1 GW online in 2026 and around 3.5 GW of next-generation TPU capacity via Broadcom starting in 2027 — economically rational rather than aspirational. SemiAnalysis estimates that custom silicon can shave on the order of 30 percent off compute fleet costs versus merchant GPUs, and Google Cloud CEO Thomas Kurian explicitly framed Anthropic's expansion as a vote for TPU price-performance. For a frontier lab whose marginal cost is dominated by inference at scale, a chip with 192 GB of HBM and 2x perf-per-watt against the prior generation is the difference between defending margins and not.

The market is doing two things at once. The custom ASIC pie is growing fast — projected at 45 percent year-on-year in 2026 versus 16 percent for GPU shipments — and within that pie Broadcom currently holds more than 70 percent share, with Mizuho estimating $21 billion of AI revenue from Google and Anthropic alone in 2026 rising to $42 billion in 2027. Hock Tan has projected Broadcom's overall AI chip business will exceed $100 billion in 2027. So even if Marvell genuinely wins the memory processing unit and an inference-TPU socket, Broadcom's absolute revenue can still grow.

That is why the market reaction was bifurcated rather than zero-sum on absolute terms: Marvell shares jumped about 6 percent to record highs on April 20 and Barclays upgraded the stock to overweight, raising its price target from $105 to $150, while Broadcom traded down on fears of share loss. Bloomberg projects Marvell could capture 20 to 25 percent of a $118 billion custom ASIC market by the early 2030s, which is consistent with both the bull case for Marvell and JPMorgan's note that Broadcom remains the clear incumbent. Community discussion on Broadcom-focused investor forums echoed the JPMorgan framing, emphasizing the 2031 Broadcom deal, the 3.5 GW Anthropic capacity from 2027, and the narrower role being negotiated for Marvell.

For Nvidia the structural risk is not that any single hyperscaler abandons GPUs — Meta's reported multibillion-dollar TPU procurement deal explicitly does not preclude continued Nvidia spend, and SemiAnalysis still credits Nvidia's flagships with leadership in training and prototyping. The risk is that inference, the fastest-growing slice of compute, increasingly runs on bespoke silicon co-designed by hyperscalers and merchant ASIC vendors. SemiAnalysis frames Ironwood as the iteration where Google 'nearly completely closes the gap to the corresponding Nvidia flagship GPU,' and the 45 percent projected growth in custom ASIC sales versus 16 percent for GPUs is the quantitative version of that statement.

Sentiment on X and Reddit captures the same tension: technical commentary praised Ironwood's order-of-magnitude generational gains and large optical-switched superpods, while skeptics argued Google still depends on Marvell and Broadcom to actually build the chips and therefore cannot fully wean off the Nvidia ecosystem. The honest read is somewhere in the middle. Google has demonstrated that hyperscaler-led custom silicon with multiple ASIC partners and TSMC can produce inference economics that are good enough to attract Anthropic, Meta, Citadel, and G42 — and that fact, more than any single chip, is what compresses Nvidia's long-run pricing power on the inference side of the workload mix.