Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads
Mirrored from NVIDIA Developer Blog for archival readability. Support the source by reading on the original site.
In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition...In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition (ASR) or text-to-speech (TTS) models may require only 10 GB of VRAM, yet occupy an entire GPU in standard Kubernetes deployments. Because the scheduler maps a model to one or more GPUs and can’t easily share across GPUs across models…
More from NVIDIA Developer Blog
-
How to Rewrite Apache Spark UDFs for GPU Acceleration with AI Agents
Oct 13
-
NVIDIA Run:ai Is Now Open Source as Part of NVIDIA DSX AI Factory Platform
Oct 13
-
ReplaySSM: Rethinking State Storage for Faster Hybrid-Model Decoding
Oct 12
-
5 Steps to Create SimReady Assets for Robotics with Frontier AI Models
Oct 8
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.