Anthropic Pursues Custom Silicon to Challenge OpenAI's Broadcom Deal
Anthropic, the AI safety-focused company behind the Claude model family, is in early-stage discussions with Samsung to develop a custom AI inference chip, TechCrunch reported today. The move comes roughly one week after OpenAI announced its own custom AI chip partnership with Broadcom, signaling that the race for specialized silicon among frontier AI labs is accelerating faster than most industry analysts anticipated.
According to sources familiar with the matter, Anthropic and Samsung are exploring a chip designed specifically for running large language model inference workloads—the computationally intensive process of generating responses from models like Claude. The partnership would leverage Samsung's advanced semiconductor manufacturing capabilities, particularly its 3nm and upcoming 2nm process nodes, to create a chip optimized for Anthropic's unique model architectures.
Why Custom Silicon Matters for AI Developers
For developers building on top of Claude or other large language models, the implications are significant. Today, most AI inference runs on general-purpose GPUs like NVIDIA's H100 or AMD's MI300X, which are designed for a broad range of parallel computing tasks. Custom chips tailored to specific model architectures can deliver 3-5x improvements in tokens-per-second throughput while reducing per-inference costs by up to 60%, according to estimates from semiconductor analysts.
"Anthropic's move reflects a growing recognition that the biggest bottleneck to AI deployment isn't model quality—it's inference cost," said Dr. Elena Marchetti, a former Google TPU engineer now consulting for AI infrastructure firms. "If Anthropic can get a Samsung chip that slices their inference costs in half, they can either undercut OpenAI on API pricing or absorb the savings to improve Claude's capabilities."
The Technical Architecture: NPU vs. GPU
The chip under discussion is expected to be a neural processing unit (NPU) rather than a traditional GPU. NPUs employ dataflow architectures that excel at the matrix multiplication and attention mechanism operations that dominate transformer model inference. Samsung already has a track record here: its Exynos mobile processors include NPU cores, and the company has been developing dedicated AI accelerators for data center use since 2024.
Key technical differentiators expected in an Anthropic-Samsung chip include:
- Support for mixed-precision computing at FP8 and FP4, reducing memory bandwidth requirements by up to 75% compared to FP16
- On-chip SRAM optimized for transformer attention layers, potentially reaching 128MB or more per die
- A PCIe 6.0 interface for high-bandwidth connectivity in existing server infrastructure
- Power efficiency targets below 150W per chip for rack-scale deployments
Timeline and Likely Release Window
Industry insiders suggest the chip is unlikely to reach production before late 2027 or early 2028, given the typical 18-24 month design cycle for custom ASICs. However, Anthropic may deploy early samples to select enterprise customers through its Amazon Web Services relationship as early as mid-2027. AWS already offers Anthropic's Claude models through Bedrock and has its own Trainium and Inferentia chips, though those are optimized for general AI workloads rather than Anthropic-specific architectures.
The Competitive Landscape: OpenAI, Google, and Meta
Anthropic's Samsung discussions place it in direct competition with several other AI labs pursuing custom silicon strategies:
- OpenAI: Partnered with Broadcom for a custom inference chip, leveraging Broadcom's networking IP and VLSI experience. Target production: 2027.
- Google DeepMind: Already uses Google's in-house TPU v5p and v6 chips, giving it a multi-year head start in custom silicon integration.
- Meta: Is designing its own AI chip in-house through its Meta Silicon group, with first-generation hardware expected for recommendation systems rather than generative AI.
- Microsoft: Has Azure Maia AI accelerators but primarily relies on NVIDIA GPUs for inference.
For developers, this fragmentation means the AI infrastructure landscape is becoming increasingly Balkanized. A custom Anthropic chip could eventually mean lower API costs for Claude users, but it also raises the risk of vendor lock-in if Anthropic optimizes its model architectures exclusively for its own hardware.
Implications for Businesses Using AI Today
Enterprises currently building applications on Anthropic's Claude API should watch this development closely for three reasons:
First, if the Samsung chip delivers on its projected performance gains, Claude API pricing could drop significantly by 2028—potentially 50% or more from current levels. This would make Anthropic more cost-competitive with both OpenAI and open-source models for high-volume inference use cases like customer support chatbots and content generation.
Second, the custom chip could enable new deployment models. Instead of relying solely on Anthropic's cloud API, enterprises might eventually be able to run Claude inference on dedicated Samsung-powered hardware within their own data centers, addressing data sovereignty and latency concerns that currently push some companies toward open-source alternatives.
Third, the timeline matters. If you're a CTO evaluating AI infrastructure for 2027-2028, this chip could reshape your total cost of ownership calculations. Waiting might yield better pricing, but it also means delaying deployment.
The Broader Industry Signal
What this news really tells us is that the AI industry is entering its hardware arms race phase. The first wave of competition was about model quality—who could build the best LLM. The second wave is about operational efficiency—who can deliver the most inference compute per dollar. Custom chips are the defining technology of this second wave.
Anthropic's choice of Samsung over TSMC is also strategic. Samsung has been aggressively courting AI chip customers with competitive pricing and a willingness to do custom packaging and integration work that TSMC typically reserves for its largest clients. By partnering with Samsung, Anthropic gains a manufacturing partner that is highly motivated to make the relationship work.
For now, developers should continue building on Claude with confidence—the API isn't going anywhere—but keep an eye on the hardware roadmap. If Anthropic's Samsung chip materializes, it could fundamentally change the economics of running state-of-the-art AI inference.
Related: Vercel AI Gateway Restores Claude Fable 5 Access After US Lifts Export Controls
Source: TechCrunch. This article was produced with AI assistance and reviewed for accuracy. Editorial standards.
