GPU providers

Important Developer Trends for 2026 (View Older)

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.	87.8	—	Strategic 85 Adoption 100 · Attention 100 · Momentum 36	61 · 63 · 60 · 66 · 91 · 89 · 91 · 88
#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.	82.9	+1	Strategic 78 Adoption 100 · Attention 100 · Momentum 23	63 · 54 · 60 · 66 · 85 · 86 · 86 · 83
#3	Kubernetes Kubernetes is durable core infrastructure for deploying, scaling, and operating GPU-backed workloads, especially training and inference services.	78.4	-1	Strategic 80 Adoption 100 · Attention 63 · Momentum 20	84 · 70 · 70 · 81 · 64 · 81 · 86 · 78
#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.	78.0	—	Strategic 67 Adoption 100 · Attention 100 · Momentum 55	51 · 52 · 49 · 51 · 56 · 63 · 80 · 78
#5	Serverless Serverless execution is materially relevant to GPU providers because it reduces operational overhead and enables elastic, usage-based AI inference and application deployment.	74.6	—	Strategic 64 Adoption 100 · Attention 100 · Momentum 23	66 · 65 · 68 · 68 · 71 · 72 · 78 · 75

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