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Where are people finding GPU capacity?

3 modelsComplete

70%Consensus Score — 2 models mostly agreed, with some nuance. Read the breakdown.

🔴Semantic Entropy — Highly divergent · 2 distinct meanings across 2 responsesH=100%

70%

consensus

Mostly aligned

How much the models agreed on this answer

High agreement — the answer is well-supported across models.

Models agree on

✓Traditional cloud providers (AWS, Google Cloud, Azure) dominate the market but often require reservations or commitments for high-end GPUs.
✓Specialized AI cloud providers (CoreWeave, Lambda Labs) focus on GPU density and high-performance networking.
✓Decentralized marketplaces (Vast.ai, Akash Network) offer cost-effective but less reliable GPU access.
✓Non-NVIDIA hardware (AMD MI300X, Intel Gaudi) is a viable alternative for training and inference.
✓On-prem and hybrid solutions are essential for regulated or latency-critical workloads.

Points of disagreement

~Oracle Cloud is highlighted as more accessible for H100s in one response but not emphasized in the other.
~Peer-to-peer platforms like Golem and iExec are mentioned in one response but not the other.
~The role of cryptominers-turned-AI-hosts is detailed in one response but omitted in the other.

Finding GPU capacity in today's AI landscape involves navigating a mix of traditional cloud providers, specialized GPU clouds, decentralized marketplaces, and alternative hardware solutions. Here’s a comprehensive guide to where people are sourcing GPU capacity, based on the latest trends and availability.

1. Traditional Cloud Providers (AWS, Google Cloud, Azure, Oracle)

·AWS, Google Cloud, and Azure dominate the market but often require reservations or multi-year commitments for high-end GPUs like H100s. Oracle Cloud (OCI) is emerging as a more accessible option, offering H100 capacity with fewer barriers.
·Google Cloud TPUs provide an alternative for those willing to step outside the CUDA ecosystem, particularly for large-scale training workloads.

2. Specialized AI Cloud Providers

·CoreWeave, Lambda Labs, Crusoe Cloud, and Applied Digital focus exclusively on GPU density and high-performance networking (InfiniBand). These providers are ideal for AI training and offer on-demand or reserved instances for A100s and H100s.

3. Decentralized and Peer-to-Peer Marketplaces

·Vast.ai, Akash Network, RunPod, and TensorDock allow renting idle GPUs from individuals or data centers. These platforms are cost-effective for fault-tolerant workloads but lack the reliability and security of enterprise solutions.

4. Non-NVIDIA Hardware Alternatives

·AMD MI300X, Intel Gaudi 2/3, Groq, and Cerebras provide viable alternatives for those facing NVIDIA shortages. AMD and Intel chips are gaining traction for training, while Groq and Cerebras excel in high-speed inference.

5. On-Prem and Hybrid Solutions

·University labs, enterprise data centers, and co-location providers (Equinix, CoreSite) offer control over hardware and data sovereignty, making them suitable for regulated or latency-critical workloads.
·Cryptominers-turned-AI-hosts (e.g., Hut 8, Core Scientific) repurpose older GPUs (RTX 3090s) for inference and fine-tuning, offering a cost-effective middle ground.

Key Recommendations:

·For Training: Use specialized GPU clouds (CoreWeave, Lambda Labs) or traditional hyperscalers with reservations. Consider alternative hardware (AMD, Intel) if NVIDIA supply is constrained.
·For Inference: Opt for decentralized marketplaces (Vast.ai, RunPod) or edge GPUs (NVIDIA Jetson, T4) for cost efficiency.
·For Regulated Workloads: On-prem or co-location solutions provide the necessary control and compliance.

Strategic Takeaways:

·Separate training from inference to optimize GPU usage.
·Network with hyperscalers or NVIDIA partners to unlock reserved capacity.
·Hybrid approaches (cloud for burst, on-prem for steady workloads) balance cost and performance.