# Nvidia's Groq 3 LPX claims massive speed wins but the math is > Source: > Published: 2026-08-26 07:23:12+00:00 # Nvidia's Groq 3 LPX claims massive speed wins but the math is The massive gap in throughput isn't just a matter of raw clock speeds or architecture efficiency; it's a matter of scale. To hit that 3,400 tokens per second milestone, Nvidia requires a cluster of at least 64 accelerators working in tandem. Cerebras, on the other hand, can reportedly achieve its performance levels using only one or two units. This creates a massive discrepancy in terms of physical footprint, power consumption, and total cost of ownership for anyone trying to deploy an LLM agent or a high-throughput production environment. When we talk about a real-world deployment, the math shifts from "tokens per second" to "tokens per second per dollar" or "tokens per second per rack unit." If you need 64 chips to match a single Cerebras wafer-scale engine, the complexity of your AI workflow increases exponentially. You aren't just managing a chip; you're managing a massive, interconnected network of high-speed communication links, dealing with increased latency between nodes, and trying to keep a small army of accelerators synchronized. ## The scaling bottleneck for MoE models A major question mark remains regarding how these architectures handle Mixture-of-Experts (MoE) models as they grow. MoE models rely on routing specific tokens to specific "experts" within the neural network. In a highly distributed setup like Nvidia's 64-chip configuration, that routing becomes a massive networking headache. Every time a token needs to jump from one chip to another to find its expert, you introduce communication overhead. If the communication latency between those 64 accelerators becomes a bottleneck, that 3,400 tokens per second figure might only be achievable under very specific, perhaps even unrealistic, network conditions. We need to see more data on how the Groq 3 LPX handles: **Inter-chip communication latency:** How much of that speed is lost when the model exceeds the memory of a single chip?**Memory bandwidth vs. Compute:** Is the speed coming from raw compute power or the ability to move weights through the system?**Power efficiency:** Does the energy cost of running 64 chips negate the speed benefits for a service provider? While Nvidia's brute-force approach is undeniably impressive in terms of raw throughput, the industry is moving toward efficiency. For a beginner-friendly deployment or a startup looking for a practical tutorial on scaling, a single-chip solution is infinitely more attractive than a massive cluster requirement. We are essentially watching a battle between "distributed massive scale" and "monolithic architectural efficiency," and the winner won't be decided by token counts alone. [Nvidia Jetson Orin is being used in combat drones in Ukraine 1h ago](/en/news/7726/) [Is AI actually burning the planet down or is that just hype? 15h ago](/en/news/7673/) [Nvidia is basically acting as a venture capitalist for the AI era 15h ago](/en/news/7671/) [Why the US immigration bottleneck is creating a massive talent 19h ago](/en/news/7645/) [The massive AI hype might be hitting a wall of reality 19h ago](/en/news/7643/) [Data centers are quietly becoming the new backbone of American 19h ago](/en/news/7641/) [Next Students are ditching ChatGPT for specialized LLMs when it comes →](/en/news/7728/) [a guide to making money with AI](https://tanyan888.com/), with plenty of directly applicable cases.