When Jensen Huang draws a parallel between a new deal and the Mellanox acquisition, the industry pays attention. During NVIDIA's Q4 FY2026 earnings call, the CEO revealed that Groq's technology would extend NVIDIA's architecture as an accelerator — mirroring how Mellanox once transformed the company's data center strategy. Wccftech
The statement was the first concrete hint at how NVIDIA intends to use the technology it secured through a non-exclusive licensing agreement worth up to $20 billion — the largest investment in NVIDIA's history. Wccftech
What the Mellanox Comparison Actually Means
Mellanox laid the foundation for InfiniBand and eventually enabled what NVIDIA calls "extreme co-design," an approach where compute and networking are engineered as a unified system. Wccftech That 2019 acquisition turned NVIDIA from a GPU vendor into a full-stack data center platform provider.
Groq is now expected to do the same for inference decoding — solving a latency problem that NVIDIA has not fully addressed even with its Hopper and Blackwell architectures. igor´sLAB While NVIDIA dominates AI model training, the rise of agentic AI workloads has shifted the bottleneck to decode — the token-generation phase where response time matters most.
Why Low-Latency Decode Matters for Agentic AI
In multi-agent workloads, decode enables AI agents to perform complex reasoning steps in seconds, which is critical as the industry moves toward swarms of interdependent AI agents. Wccftech Training demands raw throughput; inference demands speed and predictability.
Groq's Language Processing Units use deterministic execution with large on-chip SRAM, eliminating the memory bandwidth bottlenecks common in GPU-based inference. ObjectWire Demonstrations have shown LPUs generating 10,000 reasoning tokens in roughly two seconds — a level of decode performance that traditional GPU architectures struggle to match.
SRAM provides tens of terabytes per second of internal bandwidth, while compile-time scheduling eliminates timing variations across kernels, enabling near-perfect pipeline utilization. Wccftech
How NVIDIA Could Integrate Groq's LPU Technology
Two integration paths are being discussed across the industry.
The first is a rack-scale hybrid approach. According to GF Securities, NVIDIA may unveil an "LPX rack" at GTC 2026 featuring up to 256 LPU units in a single rack. Wccftech Under this model, LPU-to-LPU communication would rely on a native plesiosynchronous chip-to-chip protocol, while LPU-to-GPU connections could use NVLink Fusion for offloading KV cache during the prefill stage. This creates a clean functional split: GPUs handle attention and prefill, LPUs handle decode.
The second, more ambitious path involves directly embedding LPU technology into future GPU architectures such as Feynman through hybrid die bonding. igor´sLAB However, this approach introduces significant packaging, yield, and thermal challenges, making rack-level integration the more likely near-term option.
The Bigger Picture: NVIDIA's Modular AI Ecosystem
Huang disclosed during the same earnings call that NVIDIA's compute growth and revenue growth are now tracking 1:1, driven by the accelerating evolution of the AI application layer. Wccftech This signals a fundamental shift from model training to mass deployment — and inference is where the next wave of value lies.
With Rubin CPX, NVIDIA has already addressed the prefill stage through attention-acceleration engines and NVFP4 compute. Wccftech Groq's LPU technology is intended to close the remaining gap on decode.
The result is not a monolithic product but a modular ecosystem: Mellanox for networking, GPUs for training and prefill, LPUs for latency-critical decode. igor´sLAB This is architectural consolidation — and whoever controls decode controls agentic workloads, which increasingly define where AI revenue is generated.
What to Expect at GTC 2026
NVIDIA is expected to formally unveil its plans for LPU integration at this year's GTC conference. WccftechWhether it takes the form of an LPX inference rack, a tighter GPU-LPU coupling, or something entirely new remains to be seen.
What is already clear is the strategic direction. The $20 billion price tag on Groq validates a growing consensus that specialized inference accelerators represent a distinct and rapidly expanding market category EE Times — and NVIDIA intends to own it from the inside.