Nvidia's $20B Groq Deal Signals AI Inference Shift

Nvidia's recent agreement to acquire Groq for $20 billion marks a significant pivot in the artificial intelligence hardware landscape. For years, Nvidia's Graphics Processing Units (GPUs) have been the industry standard for training large language models. However, this deal highlights a growing industry focus on inference—the phase where trained models are deployed to answer questions and generate content in real-time.

Groq's Language Processing Units (LPUs) are engineered specifically for this purpose, prioritizing speed and efficiency over the flexibility required for training. This acquisition suggests that the next phase of AI dominance will require specialized hardware tailored to specific tasks, rather than a one-size-fits-all approach.

The artificial intelligence industry is undergoing a fundamental transformation. For the past several years, the primary challenge was building powerful models, a process known as training. This required massive computing power and flexibility, capabilities that Nvidia's GPUs excelled at. However, the industry is now pivoting toward inference, which involves running those trained models in the real world.

Inference is the operational phase of AI where models answer user queries, generate images, and carry on conversations. According to estimates from RBC Capital analysts, the inference market is expected to grow significantly, potentially dwarfing the training market. This shift changes the technical requirements for hardware.

While training is described as building a brain and requires raw power, inference is like using that brain in real-time. Consequently, metrics like speed, consistency, power efficiency, and cost per answer become far more critical than brute force computing power.

Groq, founded by former Google engineers, built its business around inference-only chips called Language Processing Units (LPUs). These chips differ fundamentally from Nvidia's GPUs. Groq's LPUs are designed to function like a precision assembly line rather than a general-purpose factory.

Key characteristics of Groq's LPUs include:

• Operations are planned in advance and executed in a fixed order.
• The rigid structure ensures every operation is repeated perfectly.
• This predictability translates into lower latency and less wasted energy.

In contrast, Nvidia's GPUs rely on schedulers and large pools of external memory to juggle various workloads. While this flexibility made GPUs the winner of the training market, it creates overhead that slows down inference. As AI products mature, the trade-off of using flexible hardware for rigid tasks becomes harder to justify.

Nvidia's decision to acquire Groq rather than develop similar technology internally is viewed by industry analysts as a 'humble move' by CEO Jensen Huang. The deal is seen as a preemptive strategy to secure dominance in the inference market before competitors chip away at it. Various rivals, including Google with its TPUs and Amazon with Inferentia, have been developing specialized inference chips.

Tony Fadell, creator of the iPod and an investor in Groq, noted that GPUs won the first wave of AI data centers, but inference was always destined to be the 'real volume game.' By licensing Groq's technology, Nvidia ensures it can offer customers both the shovels and the assembly lines of AI.

Nvidia is not abandoning GPUs; rather, it is building a hybrid ecosystem. The company's NVLink Fusion technology allows other custom chips to connect directly to its GPUs. This approach reinforces a future where data centers utilize a mix of hardware, with GPUs handling flexible training workloads and specialized chips like Groq's LPUs handling high-speed inference.

The driving force behind this shift is economic. Inference is where AI products actually generate revenue. It is the phase that determines whether the hundreds of billions of dollars spent on data centers will pay off. As AWS CEO Matt Garman stated in 2024, if inference does not dominate, the massive investments in big models will not yield returns.

Chris Lattner, an industry visionary who helped develop software for Google's TPU chips, identifies two trends driving the move beyond GPUs:

1. AI is not a single workload; there are many different workloads for inference and training.
2. Hardware specialization leads to huge efficiency gains.

The market is responding with an explosion of different chip types. The old adage that 'today's training chips are tomorrow's inference engines' is no longer valid. Instead, the future belongs to hybrid environments where GPUs and custom Application-Specific Integrated Circuits (ASICs) operate side-by-side, each optimized for specific workload types.