Samsung Teams with Nvidia to Produce Next‑Gen LPU AI Chips as Groq Starts Production

Samsung’s foundry line is now the production hub for Groq’s LP30 “LPU” AI chip, a move that signals a deeper alignment between the Korean semiconductor giant and Nvidia’s inference strategy, according to Korea JoongAng Daily. The partnership gives Groq, a startup known for its low‑latency tensor processing architecture, access to Samsung’s advanced 5‑nanometer process, while Nvidia leverages the same fab capacity to accelerate its own next‑generation inference silicon, Businesskorea reports.

Nvidia CEO Jensen Huang highlighted the collaboration in a recent interview, thanking Samsung for “manufacturing the Groq LP30 chip for us and they’re cranking as hard as they can,” a comment that was echoed in a Reuters brief on Samsung’s share price rally after the announcement. The remark underscores Nvidia’s intent to diversify beyond its traditional “one GPU does everything” model, a theme explored in a Wccftech analysis of Nvidia’s upcoming GTC 2026 roadmap. By tapping Samsung’s high‑volume manufacturing, Nvidia can push specialized LPU designs into data‑center and edge markets faster than it could through its own fabs alone.

The timing of the tie‑up arrives amid a broader memory‑chip shortage that Bloomberg attributes to a supply crunch persisting through 2030. Samsung’s ability to allocate wafer capacity for Groq and Nvidia chips may alleviate some pressure on high‑bandwidth memory (HBM) stacks that power inference workloads, though Bloomberg’s coverage stops short of quantifying the impact. Nonetheless, the collaboration illustrates how major foundries are becoming pivotal enablers for AI‑focused silicon, especially as vendors seek to sidestep the bottlenecks that have hampered earlier generations of GPU‑centric AI hardware.

Analysts note that the Groq‑Samsung production line could serve as a testbed for Nvidia’s own inference‑only accelerators, potentially blurring the line between third‑party and in‑house designs. While the Reuters piece focuses on the immediate market reaction—Samsung shares rose on the news—the longer‑term implication is a more modular AI ecosystem where specialized chips, fabricated by a common partner, can be swapped into heterogeneous compute stacks. This modularity may accelerate adoption of low‑latency AI services in sectors ranging from autonomous vehicles to real‑time video analytics, where inference speed is paramount.

In sum, the Samsung‑Groq‑Nvidia triad represents a rare convergence of design, manufacturing, and market execution in the high‑performance AI chip arena. By aligning Groq’s LPU architecture with Samsung’s leading‑edge process and Nvidia’s inference roadmap, the partnership could reshape how AI workloads are off‑loaded from general‑purpose GPUs, a shift that industry observers will watch closely as the AI hardware race intensifies.