Nvidia partners with Groq, ramps up Samsung Foundry output to boost inference AI

Nvidia’s partnership with Groq marks its first major foray into a non‑GPU inference accelerator, a move designed to diversify the company’s hardware portfolio for latency‑critical AI workloads. According to a ChosunBiz report, the collaboration will see Groq’s tensor streaming processors integrated into Nvidia’s reference designs, allowing customers to off‑load inference tasks that demand deterministic performance and ultra‑low jitter. The two firms will co‑develop a software stack that translates Nvidia’s CUDA‑based models into Groq’s instruction set, effectively extending the familiar Nvidia development environment to a new class of silicon without requiring developers to rewrite code from scratch. Nvidia’s chief architect, Jensen Huang, is quoted in the ChosunBiz piece as saying the partnership “opens a new frontier for inference at the edge,” underscoring the strategic intent to capture markets such as autonomous robotics and real‑time video analytics where GPU‑centric solutions can be overkill.

In parallel, Nvidia is scaling its Samsung Foundry production to meet the anticipated surge in demand for AI inference chips. The ChosunBiz article notes that Nvidia has secured additional wafer capacity from Samsung’s 5‑nanometer node, a process that promises higher transistor density and lower power consumption compared to the 7‑nanometer platforms currently used for many of Nvidia’s data‑center GPUs. By leveraging Samsung’s advanced packaging technologies, Nvidia aims to ship higher‑bandwidth memory (HBM)‑enabled inference accelerators that can sustain the throughput required for large language model (LLM) serving and multimodal AI pipelines. The increased foundry output is expected to complement the Groq partnership, giving Nvidia a broader supply chain and mitigating the risk of bottlenecks that have plagued the industry’s recent chip shortages.

The hardware shift dovetails with Nvidia’s broader push into physical AI, a theme highlighted by ZDNet’s coverage of the company’s “physical AI models” that target next‑generation robotics. ZDNet reports that Nvidia is using its new inference stack to power robot control loops that must react within milliseconds, a requirement that traditional GPU inference pipelines struggle to meet due to their higher latency and variable execution times. By combining Groq’s deterministic cores with Nvidia’s software ecosystem—particularly the TensorRT inference optimizer—developers can achieve sub‑millisecond response times, a critical metric for autonomous manipulators and mobile platforms. The article also points out that Nvidia’s AI‑driven simulation tools are being used to train these models in virtual environments before deployment, further tightening the hardware‑software loop.

Industry observers see the Nvidia‑Groq alliance as a signal that the AI inference market is fragmenting beyond the GPU monopoly that has dominated the past few years. VentureBeat’s piece on the “new frontier” of compact language models mentions that players such as Hugging Face and Mistral AI are delivering smaller, more efficient models that can run on edge devices, increasing the demand for specialized inference accelerators. While the VentureBeat article does not directly reference Nvidia’s partnership, it contextualizes the strategic need for hardware that can execute these models with minimal power draw and latency. By positioning Groq’s architecture as a complementary solution to its own GPUs, Nvidia is hedging against the risk that a single‑architecture approach could become a bottleneck as model sizes shrink and deployment scenarios diversify.

TechCrunch adds a longer‑term perspective, noting that Nvidia’s ambition to become “the Android of generalist robotics” hinges on an ecosystem that can support a wide array of hardware targets. The article suggests that the Groq collaboration is a step toward that vision, providing a standardized inference layer that can be abstracted across different robot platforms. By offering a unified software stack that works on both Nvidia GPUs and Groq’s ASICs, the company hopes to lower the barrier to entry for robotics developers, much as Android lowered the barrier for mobile app creators. This ecosystem approach could accelerate the adoption of AI‑powered robots in manufacturing, logistics, and consumer applications, where inference speed and power efficiency are paramount.