Nvidia's acquisition of Groq for approximately $20 billion is already showing tangible results, with the company's new Groq 3 LPU (Language Processing Unit) chip delivering record-breaking inference speeds that could reshape how AI agents operate. In independent benchmarking conducted by Artificial Analysis, Nvidia's Groq 3 LPX rack systems generated 3,400 tokens per second (tok/s) when running Google's Gemma 4 31B model with a 100,000-token input sequence, making it 4 times faster than the nearest competitor.

What Makes Groq's Chip Architecture So Different?

Unlike traditional data center graphics processing units (GPUs) that rely on high-speed memory like GDDR7 and HBM4, Groq's LPU chips use a fundamentally different approach. Each Groq 3 LPU features an SRAM-heavy dataflow architecture with approximately 2.75 terabytes per second of memory bandwidth, orders of magnitude faster than conventional memory stacks. The tradeoff is significant: while Nvidia's top-tier Rubin GPU boasts 288 gigabytes of onboard memory, each Groq 3 LPU has just 500 megabytes, or 576 times less.

This design choice reflects a fundamental insight about AI inference: memory bandwidth, not raw computing power, is the bottleneck. When an AI model generates text token by token, it spends most of its time waiting for data to move between memory and processors. Groq's architecture eliminates that wait by putting massive amounts of ultra-fast memory directly on the chip itself.

How Does Groq Handle Models Larger Than a Single Chip?

Because individual LPUs have limited memory, Nvidia's architecture distributes larger models across multiple accelerators using Ethernet connections. Each LPX rack can hold up to 256 LPUs, providing 128 gigabytes of high-bandwidth SRAM total, and multiple racks can be connected together for even larger models. For the Gemma 4 31B model tested in the benchmark, Nvidia likely used pipeline parallelism, a technique that splits the model across chips and processes inference operations in sequence.

The real challenge emerges with massive models like DeepSeek V3, which has 671 billion parameters. Serving such a model would require 1,342 accelerators, or just over 5 LPX racks, creating significant complexity in coordinating inference across so many chips.

Why Speed Matters for AI Agents

The 3,400 tokens-per-second figure might seem abstract, but it directly translates to how quickly AI agents can complete tasks. Faster token generation gives agents more time to inspect files, write and test code, call external tools, verify results, and iterate while maintaining a responsive user experience. In practical terms, agentic AI systems can complete coding tasks in minutes instead of hours when running on Groq 3 LPX infrastructure.

"Inference is the growth engine of AI. Vera Rubin extends that vision with workload-optimized AI factory configurations designed for the era of agentic AI, advancing the performance frontier with LPX for ultrafast token generation," said Jensen Huang, founder and CEO of Nvidia.

Jensen Huang, Founder and CEO at Nvidia

How Nvidia Plans to Combine GPUs and LPUs for Maximum Performance

Nvidia's strategy isn't to replace GPUs with LPUs, but to combine them in a heterogeneous architecture that plays to each chip's strengths. The approach divides inference into two distinct phases:

• Prefill Phase: Nvidia's Rubin GPUs handle the compute-intensive work of processing prompts and generating key-value caches that track model state, where raw computing power matters most
• Decode Phase: Groq 3 LPUs take over the memory-bandwidth-intensive work of generating output tokens one at a time, where speed of memory access is critical
• Combined Result: By splitting the workload this way, Nvidia achieves both high throughput for many concurrent users and extremely low latency for individual users, maximizing what's called the Pareto curve in performance optimization

The benchmark result of 3,400 tokens per second represents performance at the extreme end of the latency spectrum. By combining Groq's LPUs with Rubin GPUs, Nvidia expects to deliver a substantial fraction of that speed across large numbers of concurrent users, balancing responsiveness with throughput.

What Are the Real-World Limitations?

The Gemma 4 31B model used in benchmarking is relatively small and dense, meaning all 31 billion parameters activate for every token generated. This makes it a best-case scenario for Groq's architecture. Larger mixture-of-experts (MoE) models like DeepSeek V3, which have 671 billion total parameters but only 37 billion active parameters per token, suffer from performance overheads that don't appear in dense models.

Additionally, Nvidia's comparison claiming 4x faster performance than Cerebras requires context. While Nvidia's LPX systems are indeed faster, they require at least 64 Groq 3 LPUs to run the Gemma 4 31B model, whereas Cerebras' CS-3 accelerators can run the same model on just one or two chips. Cerebras has also announced its next-generation CS-4 accelerator, which doubles compute, input-output bandwidth, and memory bandwidth compared to existing chips, potentially narrowing the performance gap.

Who's Adopting Groq 3 LPX First?

Nebius, a Netherlands-based AI cloud provider, is the first to bring Groq 3 LPX to production through its Nebius Token Factory inference platform. The company plans to give developers access to extreme token generation speeds for responsive agentic AI applications through the same application programming interfaces (APIs) they already use, eliminating the need to migrate to new software stacks.

"Generation is the phase of inference that determines how responsive an AI system actually is, and that's exactly what Nvidia Groq 3 LPX is built to accelerate," stated Danila Shtan, chief technology officer of Nebius.

Danila Shtan, Chief Technology Officer at Nebius

Following Nebius, Groq itself, the original company behind the LPU technology before being acquired by Nvidia, plans to be among the earliest adopters of the platform.

What This Means for the AI Infrastructure Race

Nvidia's $20 billion acquisition of Groq represents a significant bet that inference, not just training, will be a major revenue driver as AI systems become more agentic and interactive. The company is positioning itself to serve every phase of the AI pipeline: training with Rubin GPUs, and inference with a combination of Rubin GPUs for bulk processing and Groq 3 LPUs for ultra-responsive, latency-sensitive workloads.

The record-breaking benchmark results suggest the bet is paying off, at least in controlled testing conditions. Whether Groq's architecture will scale gracefully to the massive, complex models that define the frontier of AI remains to be seen, but the early results have clearly caught the attention of major AI cloud providers looking to differentiate their inference offerings.