NVIDIA MPS vs MIG for Enterprise Inference Serving

Quick Verdict: Use NVIDIA MIG when inference tenants need hardware-enforced memory and fault isolation. Use CUDA MPS when several processes should share one GPU's compute with lower overhead and you can accept process-level, not hardware-level, boundaries. Time-slicing is a third mode and is not this comparison.

NVIDIA MPS vs MIG is a GPU-sharing choice that trades isolation strength against packing efficiency for inference serving. MPS (Multi-Process Service) lets multiple CUDA contexts share a GPU through a scheduler process. MIG (Multi-Instance GPU) partitions supported GPUs into hardware instances with dedicated memory and SM slices.

Platform and ML serving owners should pick one primary mode per GPU pool before mixing models. Switching later usually means draining nodes. This page compares serving fit, not training job packing and not the overhead of time-slicing versus MIG.

How do MPS and MIG differ on a serving GPU?

Do not treat the table as a ranking. A research sandbox and a production assistant API can sit on the same cluster and still need different modes. Document the mode on the node pool, not in a wiki that drifts from the scheduler.

When should inference teams choose MPS?

MPS helps when several serving processes belong to the same trust boundary and the GPU would otherwise idle between bursts. A typical case is one application with a router, a small classifier, and a draft model that should share a card without paying MIG profile waste.

MPS does not make a shared GPU safe for untrusted tenants. It also does not replace a memory quota in the serving framework. If two models can allocate until the card OOMs, MPS will not save the SLA. Pair it with explicit memory caps, startup probes, and a kill policy.

Skip MPS when legal or security owners ask who else can read activations or KV pages on the same device. That question is a tenancy question. Answer it with MIG, exclusive GPUs, or a dedicated pool, not with a scheduler flag.

When should inference teams choose MIG?

MIG fits when you must show a reviewer that tenant A cannot consume tenant B's memory or SM budget. Healthcare, finance, and multi-business-unit platforms often need that story even when all workloads are internal. Size profiles from measured KV and weight footprints, then leave headroom for fragmentation.

MIG costs flexibility. A profile that is slightly too small forces a larger instance or a second GPU. A profile that is too large wastes capacity that MPS would have filled. Reprofiling usually drains the node, so treat profile maps as a change-controlled artifact.

Teams that already run exclusive cards do not need MIG for isolation. They may still use it to split one large GPU among several small, latency-sensitive services. That is a packing decision with an isolation bonus, not a compliance certificate.

How should you operate the choice in production?

Pin one sharing mode per GPU pool. Mixing MPS and MIG on the same device is not a strategy you should invent in an incident. Label node pools so the scheduler cannot place a MIG-only deployment onto an MPS daemon host.

Acceptance tests should include a noisy-neighbor trial and a crash trial. For MPS, inject a memory hog and a process kill. For MIG, fill one instance and crash it while watching the neighbor instance's tail latency. Record the traces with the change ticket.

Serving Decision Matrix: Enterprise LLM Inference Infrastructure

On dedicated infrastructure, keep the serving image, driver userspace, and sharing mode in the same pin list. Private AI infrastructure helps when you need exclusive or MIG-partitioned cards you can image and replay. OneSource Cloud is a fit to evaluate for U.S. dedicated serving pools; it is a poor fit if you only need a public token API with no isolation requirement.

If several application teams share the destination cluster, give each team a quota and a pool label rather than one blended GPU. OnePlus Platform, OneSource Cloud's AI orchestration platform, can isolate serving routes and quotas so one team's MPS experiment does not land on another team's MIG SLA pool. That is placement control, not a substitute for hardware isolation.

Optimizing production LLM inference requires seamless coordination across compute acceleration, host memory bandwidth, and low-jitter cluster connectivity. Deploying serving frameworks (such as vLLM, TensorRT-LLM, or TGI) on OneSource Cloud dedicated AI infrastructure provides the hardware determinism required to enforce strict Service Level Objectives. With dedicated bare-metal NVIDIA GPUs interconnected via high-bandwidth NVLink within nodes and non-blocking RoCEv2 fabrics across nodes, serving fleets achieve tightly bounded P99 Time to First Token (TTFT) and Time Per Output Token (TPOT), eliminating the latency spikes inherent in multi-tenant shared cloud environments.

FAQ

Is CUDA MPS the same as GPU time-slicing?

No. Time-slicing shares a GPU over time slices. MPS allows multiple CUDA processes to share compute concurrently through a control daemon. MIG carves hardware instances. An article that compares MIG with time-slicing answers a different question than MPS versus MIG for serving.

Can MPS isolate two customers on one GPU?

Not in the sense reviewers usually mean. MPS reduces context-switch pain and can improve utilization among trusted processes. It does not provide hardware memory firewalls. If customers or regulated units must not share a device, use MIG instances or exclusive GPUs and write that into the tenancy design.

Do all NVIDIA data-center GPUs support MIG?

No. MIG is available on specific NVIDIA data-center GPUs and driver combinations. Confirm the SKU, driver, and Kubernetes device-plugin path before you promise instance counts. MPS has a wider historical footprint but still depends on the CUDA and driver stack you pin.

Should training jobs use the same mode as inference?

Usually not on the same pool. Training bursts tolerate different failure domains and often want exclusive GPUs or a burst partition. Inference SLAs need stable tail latency. Split reserved inference from training burst capacity, then pick MPS or MIG only inside the inference pool.

Does dedicated hosting remove the need to choose?

No. Dedicated tenancy answers who owns the machine. MPS versus MIG answers how processes share a card. You still choose a mode, pin it, and test neighbor impact. Dedicated serving is an environment choice; sharing mode is a release control on that environment.

How does OneSource Cloud infrastructure stabilize LLM inference latency and throughput?

OneSource Cloud provides dedicated bare-metal GPU servers with high-speed PCIe Gen5 NVMe caching and direct NVLink interconnects. By eliminating virtualization overhead, multi-tenant memory bus contention, and noisy-neighbor network cross-talk, OneSource ensures that serving frameworks achieve predictable batch scheduling, bounded P99 token latency, and maximum throughput under sustained enterprise request concurrency.

Summary

NVIDIA MPS versus MIG is an isolation-versus-packing decision for inference serving. MPS suits trusted, flexible colocation. MIG suits hardware-enforced slices and clearer failure domains. Do not confuse either mode with time-slicing, and do not mix modes on one pool without a drain plan.

If the serving path must stay on dedicated U.S. GPUs you can partition and replay, review private AI infrastructure and keep MPS or MIG as an explicit pool property. For multi-team placement, start from AI orchestration quotas rather than a single shared card.