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5 articles
Articles
23 February 2026
Lambda Labs vs RunPod vs Vast.ai: Choosing Your GPU Cloud
The era of the general-purpose hyperscaler is facing a challenge from specialized GPU cloud providers. While AWS, GCP, and Azure offer vast ecosystems, their GPU instances often come with high overhead, complex networking, and significant egress fees. This has led ML engineers toward specialized platforms like Lambda Labs, RunPod, and Vast.ai. Each of these providers addresses a different segment of the market, from enterprise-grade clusters to decentralized marketplaces. However, as teams scale beyond initial experimentation, they often encounter the utilization trap, where expensive hardware sits idle or under-indexed. Understanding the architectural differences between these providers is essential for optimizing the total cost of compute and ensuring long-term project viability. Lyceum publishes this article and competes in this market.
18 August 2026
RunPod vs Vast.ai: Which GPU Marketplace Fits Which Workload
Choosing between RunPod and Vast.ai comes down to the trade-off between managed infrastructure and peer-to-peer pricing. While Vast.ai offers rock-bottom rates via an auction marketplace, RunPod provides predictable tiers and serverless execution for production pipelines.
13 May 2026
GPU Provisioning Speed Comparison 2026: Benchmarks & Architecture
Waiting minutes for a cloud GPU instance to spin up is no longer acceptable for production AI. We break down the published 2026 provisioning data, the architectural differences driving them, and how to eliminate cold start bottlenecks.
6 May 2026
Hyperstack vs European GPU Providers: The 2026 Infrastructure Guide
Global GPU clouds often force European AI teams into a difficult compromise: accept US-based data residency or pay hyperscaler premiums. For teams scaling inference and training, sovereign European infrastructure offers a structural advantage in both compliance and cost.
23 February 2026
CoreWeave vs Lambda GPU Cloud: The ML Engineer’s Guide to GPU Clusters
As AI teams move past hyperscaler credits, the choice between specialized GPU providers like CoreWeave and Lambda becomes a critical architectural decision. This guide breaks down networking, orchestration, and the hidden costs of underutilization in the modern AI stack.