Nvidia’s GTC 2026 Keynote Unveils World’s First 1.6nm Chips, Featuring Feynman on Stage

Nvidia’s GTC 2026 keynote will feature the debut of its next‑generation “Feynman” silicon, a 1.6‑nanometer (nm) process node that, if realized, would be the smallest commercial chip ever produced. Wccftech reports that the company plans to showcase the chips alongside Groq’s newly integrated LPU (Learning Processing Unit) blocks, marking the first time Groq’s architecture appears inside Nvidia silicon. The announcement follows Jensen Huang’s promise that the event will present “technology never unveiled before,” a claim that aligns with the company’s historic cadence of pushing process‑shrinking limits at each GTC (Wccftech).

According to Reuters, the 2025 GTC already revealed a suite of AI‑focused products, including the H100 Tensor Core GPU and the Blackwell architecture, but the 2026 agenda shifts toward wafer‑scale integration and ultra‑dense compute. The 1.6 nm Feynman chips are expected to combine Nvidia’s Tensor Core matrix engines with Groq’s LPU, which is designed for low‑latency, inference‑heavy workloads. By embedding LPUs directly into the GPU die, Nvidia aims to reduce data movement overhead and improve throughput for large language model (LLM) serving, a bottleneck that has plagued many cloud providers (Wccftech). If the integration succeeds, the hybrid architecture could deliver up to 30 % higher inference efficiency compared with the current 4 nm Blackwell GPUs, according to internal benchmarks cited by the Korean outlet Chosun Biz, which Wccftech references.

TechCrunch’s coverage of the previous year’s GTC highlighted a bullish tone among developers, but also underscored mounting challenges for Nvidia, including supply‑chain constraints and intensifying competition from custom ASICs such as Google’s TPU v5 and Amazon’s Trainium. The 1.6 nm node pushes the limits of extreme ultraviolet (EUV) lithography, a process that only a handful of foundries—most notably TSMC—currently support. Analysts have warned that yield rates at sub‑2 nm scales could be volatile, potentially inflating production costs and limiting early‑stage availability (TechCrunch). Nvidia’s decision to partner with Groq suggests a strategic move to diversify its AI compute stack, mitigating reliance on pure GPU performance and addressing the “software‑hardware mismatch” that many enterprises cite when scaling LLM workloads.

The broader industry impact of the Feynman launch could be significant. If Nvidia can mass‑produce 1.6 nm chips with integrated LPUs, it would set a new benchmark for heterogeneous AI accelerators, compelling rivals to accelerate their own sub‑2 nm roadmaps. Moreover, the move may influence cloud providers’ pricing models; higher inference efficiency could translate into lower per‑token costs for services built on Nvidia’s platform. However, the announcement also raises questions about power consumption. While smaller nodes traditionally reduce voltage requirements, the added LPU circuitry may offset gains, prompting data‑center operators to reassess cooling and power budgets (Reuters). Nvidia has not disclosed the target TDP for the Feynman family, leaving the trade‑off between density and energy use an open question.

Finally, the timing of the reveal dovetails with Nvidia’s broader strategic narrative of “AI‑first” hardware. By positioning the Feynman chip as the centerpiece of GTC 2026, the company signals its intent to dominate the next decade of AI infrastructure, from training to inference. The integration of Groq’s LPU also hints at a more modular approach to accelerator design, one that could enable Nvidia to tailor compute blocks for specific workloads without redesigning the entire GPU architecture. As the industry watches, the success of the 1.6 nm Feynman chips will likely become a litmus test for how quickly leading silicon vendors can translate cutting‑edge process technology into real‑world AI performance gains.