NVIDIA MIG 與 NUMA 加速資料處理

• NVIDIA MIG creates hardware-isolated GPU instances with dedicated memory-system paths and compute engines, preventing cross-tenant interference and enabling fault isolation[2] • MIG supports multiple allocation modes: strict isolation with dedicated resources and GPU time-slicing that multiplexes workloads over the entire device, both accessible through NVIDIA GPU Operator and device-plugin stacks[1] • Kubernetes integration with MIG enables distributed inference layers like DAS to schedule heterogeneous workloads across partitioned GPU slices with policy-extensible fairness mechanisms[1] • Peer GPU memory caching via MIG reserves dedicated cache instances and leverages NVLink for high-speed KV block transfers, achieving speedups of 3–5.68× depending on cache entry count[2] • MIG slice allocation patterns (e.g., two 3g.20gb profiles on A100) may leave residual GPU capacity unallocable, resulting in idle streaming multiprocessors and lower overall utilization averages[1] • Hardware contention tracking and MIG-based execution time determinism (ETD) improvements are being developed for safety-critical cyber-physical systems requiring time predictability guarantees[4]

MIG's integration with Kubernetes and distributed inference frameworks positions it as a critical enabler for cost-efficient multi-tenant AI inference at scale. As GPU bandwidth continues to increase in newer architectures (Hopper, Blackwell), NUMA-aware workload localization via MIG becomes increasingly important for unlocking performance and power efficiency gains. The technology's adoption in high-criticality systems suggests growing demand for deterministic GPU execution in safety-sensitive applications. Peer-memory caching patterns enabled by MIG may drive architectural innovations in GPU interconnect design and memory hierarchy optimization for large language model inference.