Who Controls AI's Future? The Battle for GPU Access | CoreWeave SVP

2026-01-06 · Source: Weights & Biases · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, extended

Summary

CoreWeave SVP of Product, Corey Sanders, discusses the company's unique position in the AI cloud market, emphasizing its specialized infrastructure and customer-centric approach. CoreWeave differentiates itself from major public clouds like Azure, AWS, and GCP by focusing exclusively on AI workloads, particularly training. This specialization allows for optimized solutions such as custom object storage with a "Lotta Cache" caching solution and liquid cooling, which are critical for maximizing GPU throughput and efficiency. Sanders highlights that CoreWeave's infrastructure is designed to feed GPUs with as much data as possible, addressing the high cost and business-critical nature of AI workloads. The company's deep customer engagement, including direct involvement from its CTO, further contributes to its perceived value and "customer love," enabling it to deliver best-in-class services for AI despite being in a supply-constrained market.

Key takeaway

For CTOs and VPs of Engineering evaluating cloud infrastructure for AI, CoreWeave's specialized approach to GPU-intensive workloads offers distinct advantages over general-purpose public clouds. Your teams should consider dedicated AI cloud providers for training and inference to achieve higher performance, better cost efficiency, and tailored support, especially when dealing with large-scale, business-critical AI projects. This focus can translate into superior throughput and optimized GPU utilization, which are crucial in a supply-constrained market.

Key insights

Specialized AI cloud providers differentiate through purpose-built infrastructure and deep customer engagement.

Principles

• Focus on best-in-class for business-critical workloads.
• Specialization enables optimized infrastructure design.
• Customer engagement drives product innovation and satisfaction.

Method

Iterate frequently, ship early, and learn from customers by presenting concrete ideas or prototypes for actionable feedback, rather than open-ended questions.

In practice

• Implement custom object storage for AI data throughput.
• Utilize liquid cooling for high-power GPUs.
• Engage customers with specific product concepts.

Topics

• AI Cloud Computing
• GPU Infrastructure
• CoreWeave
• Object Storage
• Liquid Cooling

Best for: CTO, VP of Engineering/Data, Director of AI/ML, MLOps Engineer, AI Architect, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Weights & Biases.