Long-form notes distilled from GPUForge's deployment research — written for platform engineers and CTOs evaluating GPU orchestration, scheduling, and deployment surfaces.
Annualized total cost of ownership per GPU generation for a DIY bare-metal H100/H200/B200 cluster — utilization assumptions, hidden costs (noisy-neighbor tickets, scheduler engineering, DCGM stack), and a framework for when DIY wins, when hybrid wins, and when GPUForge is the cheaper fleet.
Read insights →Side-by-side comparison of KServe v0.15, vLLM with prefix caching, and LMCache as a production LLM serving stack — architecture, scaling granularity, GPU placement, routing fanout, cache-aware routing, and K8s fit. Includes a recommendation framework for teams running on top of GPUForge V1's Ray / vLLM baseline.
Read insights →Side-by-side comparison of GKE, EKS, and on-prem across pricing tiers, GPU availability, quota lead times, SLURM+K8s fit, air-gapped fit, and egress — with a recommendation framework for GPU operators.
Read insights →Architecture, scaling granularity, GPU placement, router model, cache-aware routing, and K8s fit — with a recommendation framework for teams running production LLM and embedding serving.
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