Nvidia’s $20 Billion Bet on Groq: Why The Tech Giants Are Playing the Long Game
January 7, 2026
This summer, AI chip startup Groq secured a substantial $750 million investment at a valuation of $6.9 billion. Fast forward three months, and Nvidia announced a staggering $20 billion expenditure to license Groq’s technology and talent, sparking widespread speculation across the tech community.
What We Know About the Deal
Nvidia's deal involves a non-exclusive license for Groq's intellectual property, notably its language processing units (LPUs) and related software libraries. This licensing arrangement allows Nvidia to leverage Groq's LPUs for high-performance inference services without an outright acquisition. However, the transfer of Groq's CEO Jonathan Ross, President Sunny Madra, and most of its engineering team to Nvidia suggests a deeper strategic move.
While Groq remains technically independent with Simon Edwards as its CEO, the reality is that much of its core talent has shifted to Nvidia. This raises questions about Groq's long-term viability as an independent entity and whether Nvidia's move effectively sidelines potential competitors.
The SRAM Theory: Is Groq's Tech About Memory Speed?
A prevalent theory centers on Groq’s use of static random access memory (SRAM). Unlike the high-bandwidth memory (HBM) prevalent in current GPUs—where a single HBM3e stack offers about 1 TB/s of bandwidth—Groq’s LPUs utilize SRAM that can be 10 to 80 times faster. This instant access memory enables blistering inference speeds, particularly beneficial for large language models (LLMs).
For example, Groq's chips can generate approximately 350 tokens per second on Llama 3.3 70B or up to 465 tokens per second with GPT-oss 120B. Such performance is compelling amid a global memory shortage and skyrocketing demand for HBM.
But there's a catch: SRAM is space-inefficient. A single Groq LPU contains just 230 MB of SRAM—insufficient for large models requiring hundreds of gigabytes. To run large models, hundreds or thousands of LPUs need to be interconnected, an approach that is complex and power-intensive. Larger, SRAM-rich chips like Cerebras' WSE-3 are massive and consume significant power, pushing the boundaries of practicality.
Why Invest $20 Billion? The Power of Data Flow Architecture
The more convincing reason for Nvidia’s investment is Groq’s "assembly line architecture", an innovative data flow design optimized for accelerating linear algebra calculations during inference. Unlike the traditional Von Neumann architecture, which fetches, decodes, and executes instructions sequentially, data flow architectures stream data through a network of conveyor belts and functional units, drastically reducing bottlenecks.
Groq’s architecture allows instructions and data to move continuously across its SIMD (single instruction/multiple data) units, minimizing wait times caused by memory latency. This design is promising for inference tasks, where optimizing throughput and speed is crucial.
While data flow architectures are complex to implement, Groq has demonstrated some success, making its technology attractive for Nvidia. As industry experts note, such architectures offer avenues to push chip performance beyond traditional limits without necessarily relying on massive amounts of SRAM or HBM.
What About Speculative Decoding and Inference Optimization?
Groq’s LPUs are inherently designed for inference, though their limited SRAM may restrict their effectiveness in certain stages like decoding in large models. Still, they could be invaluable for speculative decoding, a technique where a smaller “draft” model preempts the output, reducing the workload on the main model.
This approach could significantly speed up token generation and lower costs. Nvidia’s recent chips, like the Rubin series set for 2026, are already evolving to optimize different parts of the inference pipeline, and Groq’s technology might fit into this future.
The Price Tag: Is $20 Billion Justified?
At face value, $20 billion is a hefty price for licensing IP and acquiring talent—roughly equivalent to the valuation of some large tech companies. Nvidia, however, generated $23 billion in cash flow last quarter alone, making such a deal appear manageable from a financial standpoint.
Skeptics might argue that Nvidia could have simply developed similar SRAM-based data flow accelerators in-house without the costly acquisition. But Nvidia's strategic move likely aims to secure advanced data flow architectures early, gaining an edge in AI inference performance.
Is Groq Opening New Foundry Opportunities?
Some speculate that Groq's licensing might help Nvidia diversify its chip manufacturing sources. Groq currently uses GlobalFoundries and plans to leverage Samsung’s 4nm process for upcoming chips. However, Nvidia predominantly relies on TSMC for its production, and while Samsung or Intel could feasibly manufacture Nvidia’s chips, such shifts are complex, time-consuming, and unlikely to be the primary motivation behind the deal.
Long-Term Play or Strategic Hold?
Ultimately, Nvidia’s acquisition extends beyond immediate hardware concerns. Jensen Huang’s strategy often involves playing the long game—investing in foundational IP and talent that might reshape AI inference for years to come. Whether this deal will ultimately disrupt the competitive landscape or serve as a strategic hedge remains to be seen.
In summary: Nvidia’s massive investment in Groq is driven by its promise of innovative data flow architectures, high-speed SRAM, and inference optimization—technologies that can provide a competitive advantage in the race for AI dominance. While some assumptions about the deal's motivations—like addressing memory shortages or foundry diversification—may be unlikely, the move positions Nvidia to maintain its leadership in AI hardware for years to come.