As demand for GPU-accelerated workloads soars across industries, cloud providers are under increasing pressure to offer flexible, cost-efficient, and isolated access to GPUs. While full GPU allocation remains the norm, it often leads to resource waste—especially for lightweight or intermittent workloads.
In the previous blog, we described the three primary technical approaches for fractional GPUs. In this blog, we’ll explore the most viable approaches to offering fractional GPUs in a GPU-as-a-Service (GPUaaS) model, and evaluate their suitability for cloud providers serving end customers.
Best Choice: MIG (Multi-Instance GPU)
MIG is Ideal for Cloud Providers because of the following reasons.
Runner-up: Time Slicing
Time Slicing can be an effective approach for the following reasons:
- Cost-effective shared plans
- R&D and dev/test environments
- Elastic compute for batch or exploratory ML
⚠️ Limitations
- Weaker isolation: No resource partitioning at hardware level
- Inconsistent performance under load
- Harder to bill precisely per tenant
Experimental: Custom Schedulers (e.g., KAI)
This approach is not ideal for cloud providers because of the following reasons:
- No hardware isolation or enforcement
- Requires complex scheduler extensions
- Difficult to align with metering and billing
- Resource usage enforcement is manual or cooperative
Conclusion: MIG is the Most Production-Ready
The table below provides a scoring matrix comparing the three approaches.
For GPU cloud providers delivering fractional access to external customers, MIG (Multi-Instance GPU) offers the best combination of:
- ✅ Security and isolation
- ✅ Performance predictability
- ✅ Quota and billing integration
- ✅ Multi-tenant scalability
If your hardware supports it (A100, L40, H100), MIG should be your default fractional strategy. Use time slicing for budget-friendly plans and custom schedulers only in internal or cooperative environments.
