The Register: Nvidia won the AI training race, but inference is still anyone’s game

Mar 12, 2025

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Source URL: https://www.theregister.com/2025/03/12/training_inference_shift/
Source: The Register
Title: Nvidia won the AI training race, but inference is still anyone’s game

Feedly Summary: When it’s all abstracted by an API endpoint, do you even care what’s behind the curtain?
Comment With the exception of custom cloud silicon, like Google’s TPUs or Amazon’s Trainium ASICs, the vast majority of AI training clusters being built today are powered by Nvidia GPUs. But while Nvidia may have won the AI training battle, the inference fight is far from decided.…

AI Summary and Description: Yes

Summary: The text provides an in-depth analysis of the current landscape of AI inference versus training, highlighting the dominance of Nvidia GPUs while noting the emergence of challengers in the AI hardware space. The discussion focuses on the importance of memory capacity, bandwidth, and compute power in inference performance and hints at a significant shift as AI models evolve.

Detailed Description:
The text discusses the ongoing evolution of AI hardware, particularly focusing on the distinction between training and inference workloads. Here are the major points:

– **Dominance of Nvidia GPUs**:
– Currently, Nvidia leads the market for AI training hardware.
– Despite its dominance in training, inference is highlighted as an area where competition is heating up, with the possibility of new entrants challenging Nvidia’s supremacy.

– **Shifting Focus from Training to Inference**:
– There has been a historical emphasis on developing more capable training models, but now there is a growing need for efficient inference solutions.
– Inference workloads are becoming increasingly sophisticated, pushing the need for high-performance hardware.

– **Factors Affecting Inference Performance**:
– There are three core factors that predominantly influence performance in inference:
– **Memory Capacity**: Determines the size and complexity of models that can be processed.
– **Memory Bandwidth**: Affects the speed at which responses are generated.
– **Compute Power**: Influences the duration it takes to receive a response and the number of requests handled simultaneously.

– **Diversity of Inference Workloads**:
– The text underscores that inference workloads vary significantly, depending on the model architecture, hosting location, and target audience.
– Examples include low-latency models that may run on NPUs or CPUs versus large language models (LLMs) that necessitate datacenter-class hardware with extensive memory capabilities.

– **Emerging Competitors**:
– Companies like AMD, Cerebras, SambaNova, and Groq are highlighted as key challengers to Nvidia, each with unique architectures aimed at enhancing inference speeds.
– Upcoming products like Corsair accelerators aim to minimize latency to enhance user experience with large AI models.

– **The Impact of Fast Inference**:
– As AI models deploy more complex reasoning (e.g., chain-of-thought), the need for fast inference becomes critical.
– Startups are emerging to provide fast inference solutions, indicating a burgeoning market.

– **Market Dynamics and Future Outlook**:
– Established chipmakers are integrating NPUs into their systems, creating a race to offer powerful AI-optimized hardware.
– Despite emerging competition, Nvidia remains a key player, preparing for future inference deployments with its new NVL72 GPUs.

– **Economic Considerations**:
– The economics of AI inference services focus primarily on delivering a high ratio of tokens processed per dollar spent.
– Developers will likely prioritize performance and cost-effectiveness when selecting AI services, regardless of underlying hardware differences.

This analysis presents several insights for professionals in the fields of AI, cloud, and infrastructure security by highlighting the rapid advancements in inference technology and the competitive landscape. Understanding these trends is essential for organizations planning to invest in or scale AI capabilities while ensuring efficient resource allocation and planning for compliance with any relevant hardware and data management regulations.

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