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DEEP RESEARCH · NVIDIA/GROQ

NVIDIA's Strategic Groq Deal and the Reshaping of AI Inference

A structured analysis of the reported $20 billion deal, LPU determinism, and the AI chip landscape for 2026~2028.

Date: 2025-12-26 · AI semiconductor/inference infrastructure lens · Original Naver Blog post and references

Investment decisions are your own responsibility. This material is research and is not a buy or sell recommendation.

0. Bottom line first

My read is that NVIDIA is trying to extend its training-GPU dominance into inference efficiency. Groq's LPU offered ultra-low latency and deterministic execution, and NVIDIA can use that capability to address inference cost and delay, the weaker side of the GPU stack.

  • The source presents the December 24, 2025 NVIDIA-Groq strategic deal as a $20 billion, roughly KRW 28 trillion event.
  • Groq was founded in 2016 by Jonathan Ross, a key Google TPU designer, and built the LPU for real-time inference.
  • For the next three years, the source frames NVIDIA, Broadcom, and Rebellions as the platform consolidator, custom ASIC partner, and independent inference-chip alternative.

1. Why inference now matters

The first phase of generative AI centered on securing GPUs such as H100 and Blackwell for training. Once models moved into production services, the bottleneck shifted toward inference cost, latency, and power efficiency.

Interpretation: Conversational AI, agentic AI, real-time search, coding, and support services are latency-sensitive and often run at small batch sizes. GPUs are powerful, but not always the most efficient answer for that workload.

AI infrastructure bottleneck shiftFrom training to inference
2023~2024GPU training race
2025Production deployment
2026~2028Inference and power cost
Winning conditionSpeed·cost·ecosystem
Groq's LPU was built directly for this inference bottleneck.

2. Groq LPU: deterministic execution and SRAM

Groq LPU deterministic architecture image

The key idea is to reduce hardware scheduling, cache-control, and branch-prediction logic, while the Groq compiler pre-plans data movement and operation timing at the clock-cycle level.

Official fact: The source says Groq places about 230MB of SRAM on chip and cites more than 80TB/s of internal bandwidth.

Interpretation: SRAM is fast but small. Groq addresses capacity by linking hundreds of chips through RealScale interconnect so the compiler can treat the rack like one large processor.

3. GPU vs TPU vs LPU

GPU, TPU, and LPU comparison image

CategoryGPUTPULPU
PhilosophyMassive parallelism and generalitySystolic arrays for matrix mathDeterministic execution for low latency
MemoryHBM, source cites 80GB for H100HBM + ICIAbout 230MB on-chip SRAM
StrengthCUDA ecosystem and trainingGoogle-scale training efficiencyUltra-low-latency predictable inference
WeaknessSource cites 30~40% utilization at Batch Size=1Limited accessibilityRequires rack-scale design for large models

4. Strategic meaning and macro backdrop

The source frames the deal as a strategic acquisition or technology-licensing combination and as NVIDIA's answer to pressure from Google TPU and independent inference chips. It also says Groq partnered with Aramco in February 2025 for a Middle East deployment of more than 19,000 LPUs, a project worth about $1.5 billion.

Policy

Genesis Mission

The source describes Groq as one of 24 key partners in the DOE/OSTP-led AI-for-science infrastructure effort.

Demand

Project Prometheus

The $6.2 billion Jeff Bezos-backed physical AI project could become a low-latency chip demand source, according to the source.

Market

Middle East data centers

The Aramco partnership and 19,000+ LPU deployment are presented as major revenue drivers.

5. 2026~2028 picks and risks

NVIDIA

  • Groq's SRAM and compiler capabilities could help NVIDIA cover both training and inference.
  • Risks include antitrust pressure and faster in-house chip development by large customers.

Broadcom

  • Broadcom can benefit from demand by Google, Meta, OpenAI, and others seeking custom AI chips to reduce NVIDIA dependence.

Rebellions

  • If Groq moves into NVIDIA's orbit, the market may need another independent high-performance inference-chip vendor.
  • The source mentions Samsung Foundry 4nm/2nm, HBM integration, a 2026 Rebel-Quad production plan, and Saudi Aramco investment.

My conclusion is simple: the AI chip war is shifting from “who can build the biggest chip” to “who can control computation most efficiently.” That means CUDA, compilers, memory hierarchy, and rack-scale interconnect all matter together.

Sources

Naver original