Nvidia last week disclosed a non-exclusive licensing agreement with AI chip startup Groq, a deal that also brings Groq’s founder, president and key engineers into the AI chip giant while leaving the company operating independently.

Under the agreement, Nvidia will license Groq’s inference technology, which is designed for ultra-low latency AI workloads.

According to media reports, Nvidia had agreed to buy Groq for $20 billion in cash, but neither company confirmed this yet.

Citi analyst Atif Malik sees the deal as “a clear positive on two fronts.”

With the deal, Nvidia “is indirectly acknowledging that specialized inference architectures are important for real-time and cost-efficient AI deployments to address competition from TPUs and perhaps to a lesser extent upcoming startups.”

Moreover, while a licensing approach allows Nvidia to avoid regulatory scrutiny tied to a full takeover.

Malik added that licensing Groq’s IP lets Nvidia “swiftly add more inference-optimized compute stacks to its road map without building the technology from the ground up.”

Stifel Ruben Roy highlighted that Groq’s language processing units (LPUs) are optimized for fast decode inference and ultra-low latency.

Roy said Groq’s SRAM-based architecture could complement Nvidia’s upcoming Vera Rubin platform, allowing systems to be tuned for different workloads.

“We believe that the addition of an SRAM-based LPUs will add another option where token/$ and token/Watt metrics are optimized depending on workload,” Roy said.

Truist analyst William Stein described the tie-up as a competitive response to growing pressure from alternative inference architectures, particularly TPUs.

The analyst said the move is designed “to fortify NVDA’s competitive positioning, specifically vs. the TPU,” adding that while the reported $20 billion price tag is large in absolute terms, it is modest relative to Nvidia’s cash position and near-term free cash flow generation.

In a separate note, UBS analyst Timothy Arcuri said the deal “could bolster NVDA’s ability to service high-speed inference applications,” an area where traditional GPUs are less efficient due to reliance on off-chip high-bandwidth memory.

He added that the move is consistent with Nvidia’s push toward offering “ASIC-like architectures in addition to its mainstream GPU roadmap,” alongside initiatives such as Rubin CPX.