GPU providers

Important Developer Trends for 2017 (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.	61.0	+1	Strategic 85 Adoption 20 · Attention 7 · Momentum 100	54 · 54 · 54 · 54 · 54 · 54 · 54 · 61
#2	Kubernetes Kubernetes is durable core infrastructure for deploying, scaling, and operating GPU-backed workloads, especially training and inference services.	59.9	-1	Strategic 80 Adoption 20 · Attention 20 · Momentum 100	51 · 51 · 51 · 51 · 51 · 58 · 55 · 60
#3	Developer Experience Developer experience directly influences adoption, retention, and workload expansion for GPU providers by reducing friction in provisioning, deployment, observability, and cost management.	59.4	—	Strategic 74 Adoption 40 · Attention 13 · Momentum 100	47 · 47 · 55 · 44 · 54 · 52 · 44 · 59
#4	AI Agents AI agents increase demand for scalable, low-latency inference and may influence GPU provider product design around deployment, orchestration, and cost efficiency.	55.1	—	Strategic 67 Adoption 20 · Attention 39 · Momentum 100	40 · 51 · 53 · 45 · 51 · 50 · 50 · 55
#5	Observability Observability and distributed tracing are durable, broadly relevant capabilities for operating GPU clouds, AI training systems, and inference platforms reliably at scale.	51.8	—	Strategic 68 Adoption 20 · Attention 13 · Momentum 100	43 · 43 · 43 · 43 · 43 · 43 · 43 · 52

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