Full GPU vs Fractional Allocation: Isolation vs Utilization Trade offs

The GPU allocation decision happens at scheduling time and fundamentally determines performance isolation, utilization efficiency, and cost. Full device allocation gives one workload exclusive access to an entire GPU (like a Tesla V100 with 16GB memory). Fractional allocation uses NVIDIA MIG (Multi Instance GPU on A100 or newer) or vGPU to slice a physical GPU into smaller isolated partitions, allowing multiple workloads to share the same device.

Full GPU allocation provides strong isolation and predictable performance. A large language model requiring 14GB of memory and high memory bandwidth gets the entire device without contention. The downside is capacity waste: a small embedding model using only 2GB leaves 14GB idle on a 16GB GPU, raising costs significantly. For latency critical inference with strict SLOs, full allocation prevents noisy neighbor interference from other workloads competing for shared resources like memory bandwidth, PCIe lanes, or power/thermal budgets.

Fractional GPU allocation improves bin packing and utilization. MIG on A100 can create up to seven instances (like 1g.5gb partitions), each with dedicated memory and SM slices. This allows seven small models to share one A100 instead of requiring seven separate GPUs. The cost savings are substantial: $3 per hour for one A100 versus $21 per hour for seven separate V100s. The catch is scheduling fragmentation and potential interference. If you have three 1g.5gb instances allocated and need a 3g.20gb instance, the remaining capacity is stranded until existing workloads complete.

Production systems use hybrid approaches. Latency critical inference serving gets full GPUs with on demand capacity for reliability. Batch workloads, fine tuning jobs, and development workloads use fractional GPUs on spot instances for cost efficiency. Small models under 4GB memory footprint are good fractional GPU candidates. Large models over 10GB or those requiring near peak memory bandwidth get full devices to avoid performance degradation.

💡 Key Takeaways

•Full GPU allocation wastes capacity when small models use 2GB out of 16GB available, but provides strong isolation and predictable latency for Service Level Objective (SLO) critical inference

•MIG on A100 creates up to seven 1g.5gb instances allowing seven small models on one GPU at $3/hour instead of seven V100s at $21/hour total, saving 86% on compute cost

•Fractional allocation causes scheduling fragmentation: three 1g.5gb instances allocated leaves capacity stranded when you need one 3g.20gb instance until existing workloads drain

•Noisy neighbor interference on shared GPUs affects memory bandwidth, PCIe contention, and power/thermal throttling, degrading p99 latency by 30 to 50% in production multi tenant scenarios

•Hybrid strategy allocates full GPUs for latency critical inference (on demand capacity) and fractional GPUs for batch jobs, fine tuning, and development (spot capacity) based on workload tolerance

📌 Examples

Large language model requiring 14GB memory and high memory bandwidth gets exclusive V100 16GB to avoid interference; embedding model using 2GB gets MIG 1g.5gb partition

A100 40GB configured with seven 1g.5gb MIG instances for development and experimentation workloads, reducing per developer GPU cost from $2.50/hour to $0.36/hour

Production failure: fractional GPU allocation caused out of memory despite 8GB free reported because memory fragmentation from frequent model swaps prevented contiguous allocation

← Back to Autoscaling & GPU Resource Management Overview