Prevent GPU out-of-memory failures by measuring each workload’s peak memory under real concurrency, tuning model and request settings, and choosing an appropriate sharing or partitioning mechanism. A GPU assigned to a container is not automatically a hard per-process VRAM quota; if measured workloads still do not fit with headroom, reduce concurrency or add compatible GPU capacity.
How to budget GPU memory for concurrent agents
Plan for overlapping peaks, not average utilization or the number of GPU devices alone. For each agent or inference process, measure memory while it handles representative requests, then repeat with the intended agents active together. Include model weights, runtime and context allocations, KV cache, graph capture, and temporary workspaces where relevant. NVIDIA’s MPS memory-limit documentation says its accounting includes CUDA internal device allocations, while vLLM notes that CUDA graphs add GPU memory use by default.
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- Record the workload: use the actual model, runtime, input sizes, cache settings, and request pattern you expect to deploy.
- Measure individual and concurrent peaks: note whether memory rises when requests overlap or agents load and serve at the same time.
- Set a tested concurrency ceiling: leave headroom for variation and validate the ceiling under realistic traffic instead of assuming that an average-memory estimate is safe.
- Re-measure after changes: changing the model, engine settings, input lengths, or partition sizes can change the usable memory budget.
There is no universal safe concurrency ratio: the available official guidance does not establish one. The value you need is the peak for your workload and configuration.
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Which control fits the problem?
| Option | What it controls | Best fit and important boundary |
|---|---|---|
| Application and request tuning | Memory used by each inference workload | Start here when model, input, cache, or concurrency choices can be changed; verify effects on latency and throughput. |
| NVIDIA MPS client memory limits | Limits for CUDA clients sharing a GPU | Useful for cooperative concurrent CUDA work; it is not dedicated hardware isolation. |
| NVIDIA MPS v3 memory partitioning | Soft and hard memory thresholds across cgroups and containers | Consider only if its Linux, cgroup, CUDA, and non-MIG prerequisites fit your deployment. |
| NVIDIA MIG | Dedicated GPU-instance resources, including memory | For supported GPUs and profiles when workload separation and more predictable resource boundaries matter. |
| Kubernetes GPU scheduling | Assignment of GPU device resources to containers | Schedules devices through vendor device plugins; a GPU request alone does not establish a generic per-container VRAM quota. |
These controls operate at different levels. Application tuning reduces demand; MPS manages cooperative CUDA clients; MIG provisions hardware instances. Kubernetes can schedule the exposed GPU resource, but the GPU mechanism determines whether memory is actually constrained.
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When NVIDIA MPS helps—and what to check
NVIDIA describes MPS as useful when each application process does not generate enough work to saturate the GPU. It allows kernels from different CUDA processes to run concurrently, which can avoid leaving capacity idle while work waits behind another process. NVIDIA also documents device-memory limits for MPS clients and a hierarchy of controls. See When to Use MPS and the Multi-Process Service documentation.
MPS is a cooperative sharing mechanism, not equivalent to a dedicated GPU instance. Before deploying it for multiple agents, account for these operating constraints in NVIDIA’s documentation:
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- MPS is supported on Linux and QNX.
- Only one user on a system may have an active MPS server.
- System monitoring and accounting can attribute client behavior to the MPS server process, so ordinary per-process visibility may not tell the whole story.
- Client or context limits can cause context-creation failures; define how the application detects and recovers from them.
When MPS v3 memory partitioning is appropriate
MPS v3 adds fractional device-memory accounting across cgroups and containers, with distinct soft and hard thresholds. Below the soft threshold, a tenant remains within its share. Between soft and hard, it is in a pressure and borrowing zone. Allocations above the hard limit fail with out-of-memory errors. This is a threshold mechanism, not a guarantee that a workload will complete successfully below its hard limit.
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NVIDIA’s current guide lists these prerequisites: Linux, cgroup v2 mounted at /sys/fs/cgroup, CUDA 13.4 or newer, and a non-MIG device. The feature’s memory-partitioning function does not support MIG and has stated limitations involving managed and UVM memory. Review the full MPS v3 Memory Partitioning guide, including its known limitations, against the installed GPU, driver, CUDA version, and container setup.
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Do not generalize this MIG restriction to every MPS feature: it applies to MPS v3 memory partitioning. NVIDIA’s MIG deployment guide separately says CUDA MPS is supported on top of MIG. Check the specific MPS function and deployment path you intend to use.
When MIG is a better fit
Multi-Instance GPU (MIG) partitions supported GPUs into instances with dedicated memory, cache, and compute resources. Instances can run workloads simultaneously, making MIG a consideration when competing jobs need more predictable separation than cooperative sharing provides. It must be supported by the GPU and provisioned using available instance profiles; it is not a feature of every GPU. See NVIDIA’s MIG overview and MIG deployment considerations.
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Profile sizes depend on the hardware. As examples specific to GB200, NVIDIA describes creating two instances with 93 GB each, four with 46 GB each, or seven with 23 GB each; the page does not state a publication year for these examples. NVIDIA also says a GPU may be partitioned into as many as seven instances, subject to supported hardware and profiles. These are not general MIG sizes or a promise that any GPU supports seven instances.
Before choosing profiles, compare each workload’s measured peak and compute demand with the memory and resources available in the intended instance. Confirm the exact GPU generation, profiles, configuration procedure, and how your container or orchestrator exposes those instances.
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What Kubernetes GPU scheduling does—and does not do
Kubernetes documents GPU scheduling through vendor device plugins: containers request GPU resources, and the scheduler places workloads according to the available device resources. That is device-level scheduling, not evidence of a built-in, generic VRAM quota for each container. Consult the Kubernetes GPU scheduling documentation for the mechanism it documents.
If a container needs a hard memory boundary, identify the GPU-vendor feature that supplies it—such as MIG or, where prerequisites fit, MPS v3 memory partitioning—and verify how the selected device plugin exposes and enforces that feature. Test allocation failures and monitoring in the deployed configuration rather than inferring isolation from a Kubernetes GPU request.
Reduce application memory before raising concurrency
Use the inference engine’s documented memory-conservation options and constrain model, input, cache, or concurrency choices where the engine permits. For vLLM, CUDA graphs consume additional GPU memory by default, and its memory-conservation guide documents configuration options. Measure the result with your actual model and request mix: the documentation does not establish one universally safe setting, and memory changes can carry latency or throughput trade-offs.
When to add or rent GPU capacity
If measured workload peaks still do not fit after tuning and choosing a suitable sharing or partitioning approach, lower concurrency or provide more GPU memory. For hosted GPU compute, check the instance’s memory size, GPU type, isolation model, scheduling controls, and compatibility with the model and software stack before moving workloads. Additional capacity addresses a sizing shortfall; it does not replace workload measurement or compatibility checks.
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