Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesPrevent GPU out-of-memory failures by measuring each workload’s peak memory, tuning inference settings, and setting concurrency from the combined peak—not from average use or the number of GPU devices assigned. Then choose the right control: cooperative sharing with NVIDIA MPS, hard separation with MIG where supported, or more GPU capacity. A Kubernetes GPU request schedules a device; it does not, by itself, establish a per-container VRAM quota.
1. Budget memory for overlapping peaks
Start with the workload you actually run. For each agent or inference process, measure representative peak device memory under realistic prompts, input sizes, request overlap, and cache behavior. Include model weights, runtime and CUDA context allocations, key-value (KV) cache, graph capture, and temporary workspaces where applicable. NVIDIA’s MPS memory-limit documentation says its accounting includes CUDA internal device allocations; vLLM separately notes that CUDA graphs use additional GPU memory by default. See NVIDIA’s MPS documentation and the vLLM memory-conservation guide.
Use peak measurements for the concurrent combination you intend to run, not a sum of isolated averages. Simultaneous requests can change cache and temporary-allocation demand. Leave headroom for those peaks, and validate the proposed concurrency under the real workload before relying on it. The official sources do not establish a universal safe concurrency ratio.
- Record peak memory for each model and serving configuration, including the largest expected inputs.
- Test the combination of agents and requests that can overlap in production.
- Set a concurrency limit from the measured combined peak plus reserved headroom; revise it if model, input, or request patterns change.
2. Reduce per-request demand before adding concurrency
Check the inference engine’s documented memory-conservation options and the settings that govern model inputs, caches, and parallel requests. For vLLM, CUDA graphs consume extra GPU memory by default, and its guide describes memory-conservation configuration. Apply settings against the installed version and verify their effects: lower memory use may come with performance or throughput trade-offs, and the guide does not define one universally optimal configuration.
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After changing a setting, repeat the same representative peak and overlap tests used for the original budget. Do not infer safety from a process starting successfully or from a low-memory test prompt.
3. Choose a sharing or isolation control
The right mechanism depends on whether the priority is flexible utilization or stronger workload separation. NVIDIA’s mechanisms below apply to compatible NVIDIA setups; support and behavior depend on the GPU, software stack, and configuration.
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| Approach | Memory behavior | Best fit and main constraint |
|---|---|---|
| Application tuning and concurrency limits | Reduces or bounds demand through workload settings and the number of overlapping requests; it is not a hardware partition. | Use first when settings or request volume can be adjusted. Validate measured peaks because configuration alone does not prove a safe fit. |
| NVIDIA MPS client memory limits | Provides device-memory limit mechanisms for CUDA clients. It is cooperative sharing, not dedicated hardware isolation. | Useful when applications underuse the GPU and can share it. Requires operational planning for the MPS server, monitoring, and client limits. |
| NVIDIA MPS v3 memory partitioning | Uses soft and hard thresholds across cgroups and containers: soft marks pressure and borrowing; allocations beyond hard fail with out-of-memory errors. | Consider only when its Linux, cgroup, CUDA, and non-MIG prerequisites fit. Check documented limitations, including those involving managed and UVM memory. |
| NVIDIA MIG | Partitions a supported GPU into instances with dedicated memory, cache, and compute resources. | Useful when workload separation and predictability matter and a supported instance profile fits the measured demand. It must be provisioned on supported hardware. |
Use standard MPS for cooperative CUDA sharing
NVIDIA says MPS is useful when each application process does not generate enough work to saturate the GPU. It allows kernels from different processes to run concurrently, potentially avoiding unnecessary serialization. MPS also documents device-memory limits, including client-level controls and a hierarchy of controls; choose and validate the relevant limit for the deployment rather than treating MPS as a generic quota for every GPU process. See When to Use MPS and the Multi-Process Service documentation.
Plan for these operational constraints in NVIDIA’s documentation: 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; and client or context limits can cause context-creation failures. Make sure your monitoring can interpret that attribution and your service can handle failed client startup or allocations.
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Use MPS v3 memory partitioning only if its prerequisites match
NVIDIA’s MPS v3 feature accounts for fractional device-memory shares across cgroups and containers. The soft threshold marks the point at which a tenant enters a pressure and borrowing zone; it is not the hard ceiling. When allocations exceed the hard threshold, they return out-of-memory errors. This distinction matters when deciding how much memory to assign and how an agent should recover from an allocation failure. Read the current MPS v3 memory-partitioning guide, including its known limitations, before designing around it.
- Requirements documented by NVIDIA: Linux, cgroup v2 mounted at
/sys/fs/cgroup, CUDA 13.4 or newer, and a non-MIG device. - The feature’s documented limitations include managed and UVM memory behavior; confirm whether they affect your application.
Use MIG when dedicated instances fit the workload
On supported NVIDIA GPUs, Multi-Instance GPU (MIG) creates instances with dedicated memory, cache, and compute resources, allowing workloads to run simultaneously with more predictable resource separation than competing on one unpartitioned GPU. The available profiles and instance count depend on the GPU and its configuration; check the MIG technology overview and deployment considerations for the exact hardware and deployment path.
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NVIDIA’s technology page gives GB200-specific examples: two instances with 93 GB each, four with 46 GB each, or seven with 23 GB each. Those are GB200 examples, not general MIG sizes. The page also says a GPU may be partitioned into as many as seven instances, subject to hardware and profile support.
Do not generalize the MPS v3 restriction into a blanket claim that MPS and MIG cannot be used together. NVIDIA’s MPS v3 memory-partitioning guide says that feature does not support MIG, while its MIG deployment guide says CUDA MPS is supported on top of MIG. These refer to different capabilities: verify the precise MPS function, GPU, driver, and deployment path you intend to use.
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4. Treat Kubernetes scheduling and VRAM enforcement separately
Kubernetes documents GPU scheduling through vendor device plugins and GPU resources requested by containers. That is device-level scheduling; Kubernetes documentation does not establish a generic native per-container VRAM quota. See Schedule GPUs.
If a container needs a hard memory boundary, identify the NVIDIA mechanism that provides it and confirm how the selected device plugin exposes and enforces that mechanism. Depending on the design, this may mean provisioning MIG instances or evaluating MPS v3 with its prerequisites. Test the result in the actual cluster, including allocation failures, monitoring, and container recovery; a GPU resource request alone is not proof of a VRAM cap.
5. Respond to out-of-memory failures and reassess capacity
When an allocation fails, first identify which workload and allocation pattern crossed the available budget. Check whether input size, request overlap, cache demand, graph capture, or a recent configuration change differs from the tested case. Then reduce concurrency or the memory demand, or adjust the applicable partition or limit within its supported configuration. For MPS v3, an allocation beyond the hard threshold fails rather than borrowing indefinitely; application and orchestration behavior should account for that outcome.
If measured workload peaks still do not fit after tuning and appropriate sharing or partitioning, the remaining choice is capacity: use a GPU with more device memory or suitable hosted GPU capacity. Compare actual memory size, instance type, isolation, scheduling, and compatibility with your models and runtime before moving workloads. More capacity does not remove the need to budget overlapping peaks.
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