At this year's NVIDIA GTC, NVIDIA unveiled an entirely new architectural combination designed for next-generation AI inference scenarios: Vera Rubin NVL72 GPUs paired with the Groq 3 LPX Inference System. The core objective of this pairing is to address a critical contradiction increasingly prominent in current AI applications—how to achieve low-latency, predictable interactive experiences while maintaining massive throughput. The LPX is a rack-level deployment inference acceleration system. Each rack consists of 32 liquid-cooled compute trays, each integrating eight LPU (Language Processing Unit) accelerators along with host processors and communication expansion modules. Through cable-free design and high-bandwidth interconnects, this system enables efficient data transmission across different trays and even between racks, thereby reducing communication overhead and latency jitter in distributed inference. Architecturally, the core of LPX lies in the brand-new Groq 3 LPU chip. Unlike traditional GPUs that prioritize peak computing power, the LPU emphasizes "deterministic execution" and data flow control: computation, memory, and communications are uniformly scheduled by compilers, avoiding latency fluctuations caused by runtime uncertainties. On-chip large-capacity SRAM serves as the primary working storage, and explicit data scheduling minimizes performance losses due to cache misses. This architecture is particularly well-suited for decoding-dominated inference stages—the current bottleneck affecting user interaction experience with large models. As AI applications transition from offline processing to real-time interaction, inference workloads are undergoing structural changes. For instance, coding assistants, conversational bots, and multi-step agent systems are highly sensitive to Time-to-First-Token (TTFT) and per-token latency. Meanwhile, longer contexts and reasoning chains have made data transfer and memory bandwidth emerging constraints. Against this backdrop, single hardware architectures struggle to balance both throughput and responsiveness simultaneously. NVIDIA's answer is "heterogeneous inference." Under this paradigm, Vera Rubin GPUs handle high-throughput tasks such as managing extensive contexts and attention computations, while LPX focuses on latency-sensitive decoding stage calculations, including feedforward network (FFN) operations and Mixture-of-Experts (MoE) expert module executions. Working collaboratively via high-speed interconnects, they significantly enhance interactive performance without compromising overall throughput capabilities. This architecture also applies to currently rising agent-based applications. In multi-turn reasoning, tool invocation, and feedback loops, latencies accumulate at every step, directly impacting end-user experience. LPX's capability for low-jitter, deterministic execution makes it a vital complement for these scenarios. Overall, the combination of Vera Rubin and LPX represents more than just a hardware upgrade; it signifies a shift in AI inference system design philosophy—from optimizing singular performance metrics toward achieving multidimensional balances tailored to real-world application scenarios. As AI evolves from "generating content" to "executing tasks," this architecture may become a defining form of next-generation AI infrastructure.