MIG benchmarks show better GPU slicing

GPU slicing sounds like a cloud billing trick. But MIG — NVIDIA’s Multi-Instance GPU feature — is really a hardware partitioning system, and that difference is the whole story. A shared GPU usually means several jobs fighting over the same memory paths, cache, and schedulers. MIG changes that by carving one accelerator into smaller instances with dedicated resources, which is why the current wave of benchmarks and operator guides matters for anyone buying or renting expensive AI compute. (nvidia.com) ### What is MIG, exactly? MIG lets supported NVIDIA data center GPUs be split into multiple GPU instances, each with its own chunk of memory and compute. On supported profiles, one card can be divided into as many as seven instances. The point is not just sharing — it is sharing with boundaries the hardware actually enforces. (docs.nvidia.com) The usual alternative is time-slicing or software multiplexing. That can improve utilization, but workloads still take turns on the same underlying hardware. If one tenant gets bursty or memory-hungry, neighbors can feel it. NVIDIA pitches MIG as parallel execution with deterministic latency and throughput, and recent third-par(docs.nvidia.com)sure. (nvidia.com) ### Did the newer benchmarks actually show that? Broadly, yes — with an important caveat. A March 30, 2026 A100 benchmark using seven vLLM tenants found the protected tenant’s p95 latency was about 2,499 ms in shared mode versus about 1,319 ms when each tenant was pinned to a 1g.5gb MIG slice, and the protected tenant stayed much flatter when noisy neighbors ramped up. That is a usef(nvidia.com)a universal law. (medium.com) ### So is MIG always faster? No — and this is where the story gets more interesting. A February 17, 2026 Journal of Supercomputing paper comparing MPS and MIG found a real tradeoff: MPS can improve performance by up to 30% and cut energy about 20% in favorable cases, but it can also degrade by around 30% under memory (medium.com)gid partitioning scheme. (link.springer.com) ### Why are people talking about orchestration now? Because slicing the card is only half the job. You also need the cluster to request, bind, and place the right slice at the right time. Kubernetes Dynamic Resource Allocation has now matured — stable in v1.35 and GA for core APIs in v1.34 — and cloud operators are wiring MIG into that flow so a workload can ask for a right-sized partition instead of an entire GPU. (kubernetes.io) ### Why does that change the economics? A lot of AI jobs do not saturate a whole A100 or H100. If Kubernetes still treats the device as indivisible, the unused portion just sits there burning money. Microsoft’s March 3, 2026 AKS post makes that explicit: whole-GPU scheduling leaves capacity stranded, while MIG plus DRA lets multiple workloads consume right-s(kubernetes.io)ectly pack its jobs by hand. (blog.aks.azure.com) ### What’s the catch for buyers? The catch is fragmentation. MIG gives cleaner isolation, but fixed profiles can strand capacity in a different way if your workload sizes do not match the available slice shapes. It also depends on supported GPUs, driver versions, and orchestration maturity. So the winning setup is not “always slice everything.” It is matching tenan(blog.aks.azure.com)lexibility matters more. (docs.nvidia.com) ### Bottom line? The real news is not that one benchmark looked good. It is that MIG is becoming operational infrastructure instead of a niche feature — with hardware isolation on one side and modern schedulers on the other. That makes fractional GPUs feel less like a workaround and more like a procurement choice. (docs.nvidia.com)