3 docs tagged with "gpu"

This guide describes how to configure Multi-Instance GPU (MIG) on a Compute Pool. MIG partitions a supported NVIDIA GPU into as many as seven isolated instances so that several workloads can share a single physical GPU.

PaletteAI is hardware-agnostic. You can run the PaletteAI control plane on commodity hardware and register a mix of CPU-only and GPU-equipped machines as the compute capacity that serves your AI/ML workloads. At the same time, PaletteAI supports silicon-optimized software so that you can take full advantage of accelerators from different vendors, including NVIDIA and AMD GPUs.

Multi-Instance GPU (MIG) is an NVIDIA feature that partitions a supported GPU into as many as seven isolated GPU instances. Each instance has dedicated compute, memory, and memory bandwidth, so several workloads can share one physical GPU without competing for the same resources. MIG helps you increase GPU utilization when individual workloads do not need a full GPU.