GPU providers

Important Developer Trends for February 2024 (View Older or Newer)

Reconstructed with current taxonomy

Rank	Trend	Score	Change	Components	Recent scores
#1	LLM LLMs are a primary workload for GPU providers, driving demand for training, fine-tuning, inference, model hosting, and high-performance cluster capacity.	81.9	—	Strategic 85 Adoption 60 · Attention 100 · Momentum 77	87 · 88 · 80 · 85 · 76 · 71 · 69 · 82
#2	AI Model Fine-tuning Fine-tuning is a core GPU workload that directly shapes infrastructure selection through accelerator availability, interconnects, storage, orchestration, pricing, and managed training tooling.	73.1	—	Strategic 78 Adoption 60 · Attention 86 · Momentum 29	83 · 75 · 66 · 56 · 70 · 77 · 68 · 73
#3	Kubernetes Kubernetes is durable core infrastructure for deploying, scaling, and operating GPU-backed workloads, especially training and inference services.	67.8	—	Strategic 80 Adoption 20 · Attention 86 · Momentum 58	58 · 48 · 67 · 48 · 62 · 68 · 66 · 68
#4	Serverless Serverless execution is materially relevant to GPU providers because it reduces operational overhead and enables elastic, usage-based AI inference and application deployment.	62.2	—	Strategic 64 Adoption 40 · Attention 86 · Momentum 58	63 · 52 · 66 · 63 · 69 · 63 · 55 · 62
#5	Observability Observability and distributed tracing are durable, broadly relevant capabilities for operating GPU clouds, AI training systems, and inference platforms reliably at scale.	58.4	—	Strategic 68 Adoption 20 · Attention 57 · Momentum 100	53 · 41 · 43 · 43 · 43 · 43 · 43 · 58

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