A Beginner's Guide to GPU Virtualization for ML Engineers
Simply put, GPU virtualization lets multiple jobs share one physical GPU safely. Instead of giving the whole card to a single VM or container, you can carve it into isolated slices or time slots and hand those to different workloads.
As a result, utilization goes up, idle gaps shrink and I spend less time waiting for my turn on the “Big GPU.” Training, fine-tuning, inference, data prep and visualization all have different resource shapes.
Sometimes I need a full card for a few hours. Other times I only need a fraction of VRAM for a lightweight notebook or an inference microservice. With virtualization, you can match the slice to the job. This way, my cluster runs more experiments, my teammates are happier and my manager sees better cost efficiency.
Note: Some of the critical tips mentioned here are from AceCloud’s super-intelligent and friendly cloud experts. Shoutout to their friendly team for the free consultation!
Main Ways to Achieve GPU Virtualization
Full pass-through. I attach an entire GPU to one VM or pod. Performance is near native, which is perfect for long training runs or licensed software that wants direct hardware access. However, only one consumer uses the card at a time.
Mediated vGPU or SR-IOV. Here the driver or hypervisor presents several virtual GPUs that split memory and compute among guests. I use this for VDI, model serving, small training jobs and mixed workloads that benefit from quotas.
MIG, Multi-Instance GPU, available on NVIDIA A100 and H100 families. MIG creates hard slices that each get dedicated SMs, L2 cache and a portion of HBM. Isolation is stronger than soft vGPU partitioning. Therefore, noisy neighbors are less likely to disturb latency.
Time-slicing and MPS. The scheduler shares the GPU over time rather than space. This improves throughput for many small jobs. Nevertheless, latency can be less predictable, so I avoid it for tight real-time inference.
API remote for graphics. It forwards graphics calls for 3D desktops or apps. That is handy for visualization, yet it is not my choice for CUDA-heavy training.
How Do I Choose Common ML Scenarios?
For long supervised training that needs deterministic throughput, I prefer pass-through or a large MIG slice. I want consistent memory bandwidth, stable clocks and no jitter.
For fine-tuning or LoRA with moderate memory needs, I use vGPU or medium MIG slices. I can run several experiments in parallel and compare runs sooner.
For batch and real-time inference, I favor MIG or vGPU with clear VRAM limits. I size the slice to the model, keep replicas small and scale horizontally. Consequently, a single physical card can serve many tenants safely.
For interactive notebooks, I reach for the smallest vGPU or a shared time-slice. My experiments start quickly without hogging the whole device.
What Performance Tradeoffs to Expect?
There is always some overhead when multiple guests share a card.
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