NVIDIA's Feynman Chip: What It Means for AI Costs

NVIDIA just previewed the chip that will power AI in 2028

At the tail end of GTC 2026, NVIDIA CEO Jensen Huang offered a glimpse of the company’s next-generation AI architecture: Feynman. Targeted for 2028 and built on TSMC’s 1.6nm A16 process, Feynman is the successor to the Vera Rubin platform shipping later this year. It introduces silicon photonics, 3D die-stacking, and an “inference-first” design — all technical terms that translate into one practical outcome: running AI gets cheaper and faster, again.

If you run a small business and already use AI tools for scheduling, customer service, or inventory management, this matters. Not because you will buy a Feynman GPU, but because every tool you pay a monthly subscription for runs on hardware like this. When that hardware gets dramatically more efficient, your costs go down and your tools get better.

What NVIDIA announced at GTC 2026

Feynman was previewed alongside the full Vera Rubin platform launch, which dominates the near-term roadmap. Here are the key details on Feynman:

Process node: TSMC A16 at 1.6nm — one of the most advanced manufacturing processes ever used for a commercial chip. It uses backside power delivery (Super Power Rail), routing power beneath the silicon to improve efficiency and thermal performance.
Silicon photonics: For the first time in an NVIDIA product, Feynman will use optical signals instead of electrical ones to move data between components. This addresses a core bottleneck in today’s AI accelerators — the energy cost of shuttling data around.
3D die-stacking: Rather than a single flat chip, Feynman stacks compute layers vertically, shortening the distance data travels and reducing latency.
Inference-first design: Unlike previous architectures optimized for training massive models, Feynman is built specifically for running those models — the reasoning, multi-step agent workflows, and long-context processing that define today’s AI tools.
New components: A new Rosa CPU, BlueField-5 DPU, and next-generation NVLink 8 interconnects round out the platform.

The system is designed to scale to NVL1152 configurations — racks with over a thousand GPUs working in concert on inference workloads.

Feynman vs. Vera Rubin: what changes

We covered the Vera Rubin platform and its 10x cost reduction when it was announced earlier this year. Here is how the two architectures compare:

Feature	Vera Rubin (2026)	Feynman (2028)
Process node	TSMC 3nm	TSMC 1.6nm (A16)
Interconnects	Electrical (NVLink 6)	Silicon photonics (NVLink 8)
Die design	Multi-chip module, flat	3D die-stacking
Inference vs. Blackwell	~10x cheaper per token	Further reduction (TBD)
Primary focus	Inference + MoE training	Inference-first / agentic AI
Rack scale	NVL72 – NVL576	Up to NVL1152

The shift from electrical to optical interconnects is the headline change. Moving data with light instead of copper uses less power and enables higher bandwidth — two things that directly affect how much it costs to process each AI request.

How each chip generation makes your AI tools cheaper

This is the part that matters for business owners. NVIDIA’s annual architecture cadence creates a compounding cost curve:

Blackwell (2024–2025) established the baseline for today’s AI pricing. When you pay $20/month for ChatGPT or $49/month for an AI scheduling tool, the infrastructure behind those tools runs on Blackwell-era hardware.

Vera Rubin (2026–2027) promises a 10x reduction in inference cost per token compared to Blackwell. That is the single largest efficiency leap in the current roadmap, and it starts shipping this year.

Feynman (2028) extends that trajectory with silicon photonics and 1.6nm manufacturing. While NVIDIA has not published specific cost-per-token projections, the combination of a smaller process node, optical interconnects, and inference-optimized design points to another significant step down.

The pattern is clear: each generation makes running AI models substantially cheaper. That cost reduction flows from NVIDIA to cloud providers like AWS and Azure, then to AI tool companies, and finally to you as lower subscription prices or better capabilities at the same price.

We are already seeing this with inference competition from Cerebras and the broader inference era — hardware companies are racing to make AI cheaper to run, not just more powerful to train.

What small businesses should take from the hardware roadmap

You do not need to track GPU architectures. But understanding the direction gives you confidence in three things:

AI tool prices will keep falling. The tools you cannot afford today at $100/month may cost $30/month in two years. Plan your automation roadmap accordingly — features that are too expensive now are worth revisiting annually.
Agentic AI is the priority. Feynman’s inference-first design confirms that the entire industry is betting on AI agents that take actions, not just chatbots that answer questions. If you run a service business, AI employees that handle scheduling, reviews, and customer intake represent where the technology is heading.
The Appalachian advantage is growing. As AI tools become cheaper, the barriers to adoption shrink. A plumber in Charleston or a restaurant owner in Boone does not need enterprise budgets to use the same AI capabilities as a chain operation in Charlotte. Each hardware generation widens that window.

What to watch

Feynman is two years out, but the roadmap between now and then is packed:

H2 2026: Vera Rubin ships to cloud providers. Expect AI tool pricing to start reflecting the 10x efficiency gain within 6–12 months of deployment.
H2 2027: Rubin Ultra arrives with 14x the performance of current hardware. The agentic AI infrastructure becomes genuinely enterprise-scale.
2028: Feynman production begins. Silicon photonics and 1.6nm manufacturing set the stage for the next cost inflection.

Between now and Feynman’s arrival, the practical step is to start using AI tools while prices are already falling. Waiting for the cheapest possible hardware means missing years of competitive advantage.

Keeping up with how AI hardware affects your business tools? Get in touch — we help Appalachian businesses adopt AI at the right time and the right price.