Anthropic co-designing custom AI inference chips to bypass costly Nvidia GPUs — Samsung reported as manufacturing partner for Claude maker
Anthropic has announced it’s building an in-house chip development team to co-design its own custom ASIC processors for handling AI inferencing workloads. As described to Business Insider, Anthropic is starting to hire engineers to design the chips with an unspecified partner, and it looks set to do it at pace, with the job listing saying that any potential hiree would need to be ready to work to a schedule and get the chip design over the line.
This is just the latest major AI company to announce it’s developing its own custom hardware. As the global economic shortages squeeze chip supply and models increasingly lean on optimizations to make workloads more efficient and potentially profitable, making custom silicon for your own data centers makes a lot of sense. Anthropic now joins the likes of Google, Meta, Microsoft, Amazon, and OpenAI in building their own chips for the job.
If you want something done right…
It’s no secret that if you want to train an advanced AI model, you need Nvidia GPUs. Even Chinese AI developers, who have the ruling party leaning on them and limited access to Nvidia hardware, still use Nvidia GPUs — even if they have to smuggle them first.
But if you’re looking to run AI to perform inferencing workloads for agentic and generative AI models, you can use a much wider array of hardware. Nvidia GPUs are good, but they’re expensive — custom silicon can have a lower total cost of ownership of up to 65% — and power-hungry, and there are much more efficient options available. Chinese labs are using domestic Chinese hardware, and many Western AI developers have their own solutions; those that don’t are making them.
Google has been building its Tensor Processing Unit (TPU) chips for 12 years, working with Broadcom to develop each generation. Amazon has its Trainium and Inferentia chips, and Meta recently announced several new MTIA designs for deployment through 2027. Microsoft has its Maia line, and Tesla recently pivoted to its AI5 and AI6 chip designs after years of developing Dojo.
And now Anthropic is getting in on the act, and for much the same reasons. Anthropic told Business Insider that it was co-designing the chips so that they would allow Claude to run faster and more efficiently at the scale its customers need. Indeed, Anthropic has seen explosive growth in the past year, seeing huge expansion in the consumer space and taking on significant government contracts — not to mention agentic AI requiring far more tokens than traditional single-prompt interactions.
Anthropic hasn’t revealed which firm it’s working with on the design and development. Although Broadcom and Marvell are the two largest companies in the ASIC co-design market, representing some 95% of it, The Information reported last month that Anthropic was in talks with Samsung for manufacturing.
The shovel sellers always benefit
Designing, packaging, and manufacturing your own custom ASIC for AI inferencing isn’t cheap, and it isn’t easy. Alongside developing the hardware, you need the software stack to utilize it, and you want models that are optimized to run on it to make the most of its potential advantages. That makes it more worthwhile for most companies to simply use other firms’ hardware and more general-purpose GPUs. But for major AI companies with enough money to burn and the ability to scale up to maximize efficiency gains, it’s well worth the investment.
But the AI developers aren’t the only ones who benefit. Nvidia has been one of the few companies to make enormous profits from the AI boom, while the likes of Meta, Google, Microsoft, and OpenAI are all losing enormous sums of money on their AI efforts. There are very real winners from the custom ASIC design and build market, too: TSMC, Broadcom, and Marvell.
Broadcom has been Google’s co-design partner for years, and was also recently tapped to help build OpenAI’s inferencing chips. It also works with Meta on its MTIA design, as well as holding contracts for other custom ASIC designs with ByteDance and Fujitsu. It also produces strong interconnect and networking hardware, which allows it to offer customers a more complete solution. It claims to have a $73 billion backlog of orders to work through, and expects to generate over $100 billion in annual AI chip revenue by the end of 2027.
Marvell holds massive contracts with Amazon for its Trainium chips and Microsoft for Maia, and is expected to make upwards of $11 billion for these co-design jobs in 2026.
If Samsung ends up as Anthropic’s partner, it would be a relatively small player in this particular space, but it would bring enormous manufacturing and chip design expertise to the table, as well as access to the all-important memory that is in such short global supply.
The biggest winner of all these initiatives, though, is arguably TSMC. The Taiwanese company produces the majority of the world’s cutting-edge silicon and is involved in the production of almost all the chips discussed here. They need TSMC’s CoWoS advanced packaging technologies for integration with HBM. It also handles much of the packaging of Nvidia’s and AMD GPUs, as well as producing much of the underlying wafers.
Multi-polar chip world
Anthropic joining the custom ASIC race is hardly surprising and further cements the future we seem to be barrelling towards, which is each of the hyperscaler AI companies looking to handle as much of their inferencing with custom hardware as possible. It’s more efficient, easier to control for features and specifications, and easier to scale up when optimized for internal models. From a Chinese perspective, it’s also easier to avoid problems caused by international trade blockades and tariffs.
They’ll likely never become 100% reliant on their own chips — there is just too much AI demand to scale into for that to happen, and Nvidia has been ruthlessly dominating access to the supply chain. But every new chip installed is an Nvidia GPU that won’t be used for the same purpose, which may help reduce the stranglehold Nvidia has on the industry. Not for training, though. That’s likely to remain Nvidia’s biggest appeal for some time to come.
