NVIDIA Groq 3 LPX Enters Full Production: 4x Faster AI Inference for Agentic Systems

NVIDIA has announced at Hot Chips 2026 that its Groq 3 LPX interactive AI inference accelerator is now in full production. This marks a significant milestone in the company's efforts to dominate the AI inference market, which is becoming increasingly important as AI agents and real-time applications demand faster response times.

The announcement comes at a time when the AI industry is shifting focus from training large models to deploying them efficiently at scale. Inference speed has become a critical competitive advantage, and NVIDIA's latest hardware is designed to set new standards.

NVIDIA Groq 3 LPX: A New Era for AI Inference

The Groq 3 LPX is NVIDIA's latest generation inference accelerator, specifically designed for agentic AI systems that require ultrafast token generation. Unlike traditional inference chips that prioritize throughput for batch processing, the Groq 3 LPX is optimized for interactive scenarios where response latency matters most.

Key specifications include:

• Part of the NVIDIA Vera Rubin NVL72 platform
• Optimized for real-time, interactive AI inference
• Designed specifically for agentic AI workloads
• Supports models up to 100K token context windows

Breaking Records: 3,400 Tokens Per Second

The Groq 3 LPX has achieved a remarkable benchmark of 3,400 output tokens per second on Gemma 4 31B, an open-source agentic model, at a 100K token context. This is 4 times faster than the nearest alternative platform.

To put this in perspective:

• At 3,400 tokens/second, a typical chatbot response (200-300 tokens) is generated in under 100 milliseconds
• This enables truly real-time conversational AI experiences
• Complex agentic workflows that require multiple model calls can be completed in seconds rather than minutes

These performance gains are particularly significant for agentic AI applications, where AI systems need to make multiple decisions and generate multiple responses in rapid succession.

The Vera Rubin NVL72 Platform

The Groq 3 LPX is a key component of NVIDIA's Vera Rubin NVL72 platform, which represents the company's next-generation infrastructure for AI inference. The platform is designed to support the growing demand for real-time AI applications across industries.

The Vera Rubin platform builds on NVIDIA's previous DGX and HGX architectures but is specifically optimized for inference workloads rather than training. This strategic shift reflects the industry's evolution as more companies move from AI development to AI deployment.

Nebius: First Cloud Deployment

Nebius has been announced as the first cloud provider to deploy the Groq 3 LPX. This partnership gives Nebius customers early access to the fastest inference hardware available, positioning the company as a leader in high-performance AI cloud services.

The deployment is expected to benefit:

• AI startups building real-time applications
• Enterprises deploying customer-facing AI agents
• Research institutions running large-scale inference experiments

The HBM vs DDR5 Memory Challenge

At the same Hot Chips 2026 event, Micron issued a warning about the widening silicon penalty between HBM (High Bandwidth Memory) and DDR5. As each new generation of AI chips demands more memory bandwidth, the cost differential between HBM and standard DDR5 memory continues to grow.

This has significant implications for AI hardware economics:

• HBM provides the bandwidth needed for AI workloads but at a premium cost
• The widening gap could make AI hardware increasingly expensive
• Companies may need to balance performance requirements against memory costs

What This Means for AI Development

The availability of the Groq 3 LPX in full production has several implications for the AI industry:

• Real-time AI becomes standard — Applications that previously suffered from latency issues can now deliver instant responses
• Agentic AI accelerates — Multi-step AI workflows become practical for production use
• Competition intensifies — Other hardware providers will need to match or exceed these performance levels
• Cost considerations — While performance improves, memory costs remain a concern for widespread deployment