#Features

#vGPU (Based on Opensource GPU-Manager)

• Fine-Grained Resource Slicing
  Splits physical GPUs core from 1-100 quotas. Supports dynamic allocation for multi-tenant environments like AI inference and virtual desktops.

• Topology-Aware Scheduling
  Automatically prioritizes NVLink/C2C-connected GPUs to minimize cross-socket data transfer latency. Ensures optimal GPU pairing for distributed training workloads.

#pGPU (NVIDIA Device Plugin)

• NUMA-Optimized Allocation
  Enforces 1:1 GPU-to-Pod mapping with NUMA node binding, reducing PCIe bus contention for high-performance computing (HPC) tasks like LLM training.

• Exclusive Hardware Access
  Provides full physical GPU isolation through PCIe passthrough, ideal for mission-critical applications requiring deterministic performance (e.g., medical imaging processing).

#MPS (NVIDIA Multi-Process Service Plugin)

• Latency-Optimized Execution
  Enables CUDA kernel fusion across processes, reducing inference latency by 30-50% for real-time applications like video analytics.

• Resource Sharing with Caps
  Allows concurrent GPU context execution while enforcing per-process compute (0-100%) and memory limits via environment variables.