NVIDIA CEO Jensen Huang may have just delivered one of the most unexpected year-end moves in the AI hardware world: a reported high-value deal with Groq, the company known for building specialized processors designed to make AI inference faster and more predictable. At first glance, it looked like a blockbuster acquisition. In reality, it appears to be something far more strategic—and potentially more disruptive for the future of AI inference.
A deal that looks like an acquisition, but isn’t one on paper
Early reports sparked immediate speculation that NVIDIA was “buying” Groq in a massive deal said to be valued around $20 billion. That rumor quickly lit up the industry, with debates about regulatory roadblocks, what it would mean for Groq’s product direction, and whether the agreement would trigger lengthy scrutiny.
Then came Groq’s own clarification: the arrangement is described as a non-exclusive licensing agreement that gives NVIDIA access to Groq’s inference-related technology. NVIDIA’s internal messaging reportedly reinforces the framing: NVIDIA plans to integrate Groq’s low-latency processors into its AI factory approach, will bring in talent, and will license IP, but is not acquiring Groq as a company.
That distinction matters. Structuring the relationship as licensing plus talent onboarding can potentially reduce the regulatory friction that comes with a traditional acquisition. The result, at least as the situation has been described, resembles a modern “reverse acqui-hire” play: secure key people and critical intellectual property, keep the startup operating in a reduced form, and avoid the formal merger pathway that typically draws heavy oversight.
Why this matters now: inference is the next major battleground
The bigger story is not the business structure—it’s what NVIDIA gains on the hardware side.
AI compute demand is shifting. Training frontier models still matters, but inference is where many companies expect ongoing usage and revenue to concentrate, especially for hyperscalers serving real-time AI products. And inference isn’t one single workload. It’s multiple phases with very different bottlenecks.
Training generally favors throughput and high arithmetic intensity, which is why today’s accelerators lean on massive parallel compute and high-bandwidth memory. Inference, however, increasingly rewards low latency, stable performance, and predictable response times—especially as real-time AI applications scale.
A key detail: the “decode” phase is becoming crucial
In transformer-based AI models, inference is often discussed in two broad parts:
Prefill (often tied to processing prompts and large context)
Decode (token-by-token generation)
Decode is where responses are generated, and it tends to be extremely sensitive to latency and jitter. Predictable per-token timing matters, especially when serving many users simultaneously, meeting strict response targets, or trying to keep infrastructure utilization high without overprovisioning.
This is where Groq’s approach stands out.
Groq’s LPU approach: built for deterministic, low-latency inference
Groq’s solution is the LPU, or Language Processing Unit—hardware designed specifically to run inference workloads with determinism and speed. The architecture emphasizes two ideas that align closely with decode requirements:
1) Deterministic execution via compile-time scheduling
Instead of relying heavily on dynamic scheduling that can introduce timing variability, Groq focuses on compile-time orchestration. The goal is to reduce pipeline stalls and eliminate unpredictable slowdowns, improving consistency and utilization.
2) On-die SRAM as a primary performance weapon
Groq’s chips have been described as including around 230 MB of on-die SRAM and delivering up to 80 TB/s of on-die memory bandwidth. SRAM, compared to HBM/DRAM-based approaches, can drastically reduce access latency and improve predictability—two traits that matter heavily in decode workloads where memory behavior can dominate.
SRAM can also improve energy efficiency per token, since it avoids part of the overhead associated with moving data in and out of external high-bandwidth memory subsystems. For large-scale inference, power and efficiency are not side benefits—they’re core buying criteria.
Why NVIDIA would want this: a “missing piece” for inference dominance
NVIDIA already has enormous strength in GPU compute, software, and networking—an ecosystem that defined the training era. But inference is evolving into a more specialized, workload-segmented world. One promising strategy is a split stack:
GPUs handle prefill, long-context processing, and broader general-purpose inference
Specialized low-latency processors focus on decode, where deterministic token generation is the bottleneck
If NVIDIA integrates Groq-style low-latency processors into rack-scale inference systems alongside its networking and platform software, it could offer a more complete end-to-end inference solution. That would make it easier for hyperscalers to deploy a unified inference “factory” rather than stitching together different vendors and architectures.
Just as importantly, NVIDIA’s reach could turn highly specialized inference hardware from a niche option into a default part of modern AI infrastructure—especially if it becomes tightly packaged with NVIDIA’s existing deployment model.
The takeaway
Whether you view this as clever deal-making, a talent-and-IP land grab, or a calculated move to stay ahead of shifting AI economics, the direction is clear: inference is becoming the next headline battleground, and low-latency decode is a key front within it.
If NVIDIA can successfully fold Groq’s deterministic, SRAM-driven inference technology into its broader AI platform strategy, it won’t just protect NVIDIA’s position—it could reshape how large-scale AI inference systems are built, optimized, and sold.
