NVIDIA plans to introduce a new inference chip integrating Groq's "Language Processing Unit" (LPU) technology at next month's GTC developer conference. This move signals NVIDIA's accelerated shift toward inference computing, addressing growing customer demand for high-performance, cost-effective computing solutions.
According to reports, this new system, described by NVIDIA CEO Jensen Huang as "unlike anything the world has seen," is specifically designed to accelerate query responses for AI models. The product launch is expected to reshape the current AI computing market landscape, directly impacting cloud service providers and enterprise investors seeking more cost-efficient alternatives.
In a significant endorsement of the technology, ChatGPT developer OpenAI has agreed to become one of the primary customers for the new processor, announcing large-scale purchases of "dedicated inference capacity" from NVIDIA. This commitment not only secures NVIDIA's core client base but also signals market recognition that foundational infrastructure for autonomous AI agents is transitioning from large-scale pre-training to efficient inference.
Amid intense competition from Google, Amazon, and numerous startups, NVIDIA is expanding beyond its traditional reliance on graphics processing units (GPUs). By introducing new technical architectures and exploring CPU-only deployment models, the company aims to maintain its market dominance during the next phase of AI industry evolution.
The integration of LPU design directly addresses bottlenecks in large model inference. As the AI industry shifts from model training to practical application deployment, inference computing has become the central focus. AI inference primarily consists of pre-fill and decode stages, with the decoding process for large AI models being particularly slow. To overcome this technical limitation, NVIDIA is pursuing external technology integration to push physical boundaries.
Reports indicate that NVIDIA secured key technology licensing from startup Groq for $20 billion late last year, acquiring executive team members including founder Jonathan Ross through a major hiring initiative. Groq's LPU architecture differs fundamentally from traditional GPUs, demonstrating exceptional efficiency in handling inference functions.
Industry analysts suggest the upcoming product may involve groundbreaking next-generation Feynman architecture. This architecture potentially incorporates broader SRAM integration solutions and might even deeply integrate LPU through 3D stacking technology, specifically optimizing for latency and memory bandwidth—the two primary inference bottlenecks—thereby significantly reducing energy consumption and operational costs for AI agents.
While introducing LPU architecture, NVIDIA is also adapting its traditional processor usage patterns. The company's previous standard approach involved bundling Vera CPUs with powerful Rubin GPUs in data center servers, but this configuration has proven excessively costly and energy-inefficient for certain AI agent workloads.
Some large enterprise clients have found CPU-only environments more efficient for specific AI tasks. Responding to this trend, NVIDIA recently announced expanded collaboration with Meta Platforms, implementing its first large-scale CPU-only deployment to support Meta's advertising targeting AI agents. This partnership is viewed by markets as an early indicator of NVIDIA's strategic adjustment, demonstrating the company's move beyond pure GPU sales toward diversified hardware combinations targeting different AI market segments.
This evolution in underlying hardware design directly responds to exploding demand for AI agent applications across the technology sector. Many companies building and operating AI agents have discovered traditional GPU solutions too expensive and suboptimal for actual model operations.
OpenAI's recent activities highlight this trend. Beyond committing to purchase NVIDIA's new systems for its rapidly growing Codex tool, OpenAI last month entered a multi-billion dollar computing partnership with startup Cerebras. According to Cerebras CEO Andrew Feldman, their inference-focused chips outperform NVIDIA's GPUs in speed. Additionally, OpenAI has signed significant agreements to use Amazon's Trainium chips.
Beyond startups, major cloud providers are accelerating their own chip development efforts. Anthropic Claude Code, widely regarded as the leader in automated coding markets, primarily relies on chips designed by Amazon AWS and Alphabet's Google Cloud rather than NVIDIA products.
Facing competitive pressures, Jensen Huang emphasized in recent interviews that NVIDIA is transforming from a pure chip supplier into a comprehensive AI ecosystem builder spanning semiconductors, data centers, cloud services, and applications. For investors, next month's GTC conference will serve as a critical test of whether NVIDIA can maintain its 90% market share dominance in the emerging inference computing era.