GPU utilization increased from ~20% to 43% on a reservation of 96 NVIDIA H100 GPUs,…
This is a dev post classified by Jev as Hosting & infra (a tool drop), kept by the Dev Radar because it carries real work, not commentary.
GPU utilization increased from ~20% to 43% on a reservation of 96 NVIDIA H100 GPUs, while cutting queue starvation by 74%, with blocked jobs falling from roughly 10 per day to around 4. That’s the concrete result @SpreeAI saw after fixing their orchestration. When a unified diffusion model requires 80–100 GB of memory, you can’t simply throw workloads at a cluster and expect to use those GPUs efficiently. SPREEAI was dealing with workload fragmentation, ad-hoc submissions, and storage I/O blocking that left expensive GPUs idle. Working with Lambda’s ML engineering team, they implemented MLf
Posted by Lambda (20.9k followers) 1 days ago · 13 likes · 1.5k views · view the original post on X. Kept by the Dev Radar as Hosting & infra.
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