GPU Memory Pooling and Sharing: Maximizing Utilization in Multi-Tenant Clusters

December 2025 Update: 75%+ of organizations reporting GPU utilization below 70% at peak load. GPT-4 trained on 25,000 A100s with only 32-36% average utilization. NVIDIA MIG enabling up to 7 isolated instances per A100/H100. Time-slicing delivering up to 90% cost savings by running 10 inference jobs on single GPU. MIG providing hardware-level memory isolation for multi-tenant security.

NVIDIA Multi-Instance GPU (MIG) technology partitions a single A100 or H100 GPU into up to seven isolated instances, each with dedicated high-bandwidth memory, cache, and compute cores.¹ The capability transforms expensive accelerators from monolithic resources into flexible pools serving multiple workloads simultaneously. Consider a common scenario: an ML team running 10 inference jobs, each requiring only a fraction of a powerful A100 GPU. Without efficient sharing, they might provision 10 separate A100 GPUs, leading to massive overspending. GPU time-slicing can run these 10 jobs on a single A100 GPU, delivering up to 90% cost savings on GPU infrastructure.²

Despite unprecedented investments in GPUs, most enterprises fail to use them effectively. According to the State of AI Infrastructure at Scale 2024 report, over 75% of organizations report GPU utilization below 70% at peak load, meaning the majority of one of the most valuable corporate resources sits idle.³ When GPT-4 trained on 25,000 A100s, average utilization hovered at just 32-36%, and academic audits report GPU usage swinging anywhere from 20% to 80%.⁴ Memory pooling and sharing technologies address the utilization gap by enabling multiple workloads to share GPU resources efficiently.

Understanding GPU sharing strategies

GPU sharing encompasses multiple technologies with different tradeoffs between isolation, overhead, and flexibility.

Multi-Instance GPU (MIG)

MIG provides hardware-backed partitioning creating isolated GPU instances with guaranteed resources.⁵ Each partition receives dedicated memory and compute capacity that other partitions cannot access. The isolation ensures quality of service (QoS) while extending accelerated computing resources to all users.

An NVIDIA A100 GPU contains 7 compute slices and 8 memory slices that MIG partitions allocate.⁶ The partitioning process determines how to divide these resources among instances. Common configurations include 7 instances of 1g.5gb (1 compute slice, 5GB memory) or fewer larger instances for memory-intensive workloads.

MIG mixed strategy provides the greatest flexibility and efficiency in resource partitioning. Cluster administrators can leverage every compute and memory slice to match actual workload requirements.⁷ The mixed strategy represents the most popular MIG use case in production environments where workloads vary in resource needs.

Time-slicing

Time-slicing shares a GPU among multiple processes by rapidly switching between them, similar to how CPUs share time across processes.⁸ Each process perceives exclusive GPU access while actually sharing cycles with other workloads. The approach works on older GPU generations that lack MIG support.

Time-slicing trades memory and fault isolation for broader sharing capability.⁸ A memory error or crash in one time-sliced process can affect others sharing the same GPU. The reduced isolation suits development environments and non-critical workloads better than production inference serving.

Organizations can combine MIG and time-slicing, applying time-slicing within MIG partitions for even finer-grained sharing.⁸ The combination enables scenarios where MIG provides isolation between tenants while time-slicing maximizes utilization within each tenant's partition.

Virtual GPU (vGPU)

vGPU technology provides virtualized GPU access with software-enforced isolation.⁹ The virtualization enables sharing across virtual machines rather than just containers, supporting traditional enterprise virtualization infrastructure. vGPU requires licensing and driver support that container-native approaches avoid.

GPU virtualization and pooling technologies have become effective means to enhance resource utilization, reduce costs, and meet multi-tenant demands.⁹ vGPU, MIG, and time-slicing each suit different scenarios based on isolation requirements, hardware capabilities, and infrastructure architecture.

Kubernetes integration

Kubernetes has become the dominant platform for GPU workload orchestration, with native GPU sharing support maturing rapidly.

NVIDIA GPU Operator

The NVIDIA GPU Operator automates GPU driver installation, device plugin deployment, and monitoring across Kubernetes clusters.¹⁰ The operator simplifies GPU lifecycle management, ensuring consistent GPU availability without manual configuration on each node.

MIG configuration through the GPU Operator enables declarative partition management. Administrators specify desired MIG configurations, and the operator creates and maintains partitions automatically. The automation prevents configuration drift and simplifies cluster operations.

Device plugin configuration

Kubernetes device plugins expose GPU resources to the scheduler. Standard configuration presents each GPU as a discrete resource. MIG-aware device plugins expose individual MIG instances as schedulable resources, enabling pod placement on specific partitions.¹¹

Strategy selection determines how the device plugin presents MIG devices. Single strategy exposes one device per GPU regardless of partitioning. Mixed strategy exposes all MIG instances independently, enabling maximum flexibility.⁷ Production deployments typically use mixed strategy for its resource efficiency.

Resource quotas and limits

Kubernetes ResourceQuotas limit GPU consumption per namespace, enabling fair sharing across teams.¹² Organizations set quotas based on team budgets, project priorities, or capacity planning models. The quota enforcement prevents any single team from monopolizing cluster GPU resources.

LimitRanges set default and maximum GPU requests per pod. The defaults ensure pods without explicit GPU requests still receive appropriate resources. Maximums prevent individual pods from requesting excessive GPU allocations that prevent other workloads from scheduling.

Memory pooling architectures

Beyond single-GPU sharing, memory pooling extends resources across multiple GPUs and nodes.

Unified memory and NVLink

NVIDIA Unified Memory provides a single address space spanning CPU and GPU memory.¹³ Applications access memory without explicitly managing transfers between devices. The runtime handles data movement automatically based on access patterns.

NVLink interconnects enable high-bandwidth memory access across multiple GPUs. Memory pooling across NVLink-connected GPUs extends effective memory capacity beyond single-GPU limits. Large models that exceed single-GPU memory capacity can execute using pooled memory from multiple GPUs.

CXL memory pooling

Compute Express Link (CXL) enables memory pooling across the PCIe fabric.¹⁴ CXL memory appears as additional memory tiers accessible to both CPUs and accelerators. The technology enables memory capacity expansion without GPU upgrades.

CXL memory pooling for AI workloads remains emerging but offers promising capacity expansion paths. Organizations planning GPU infrastructure should consider CXL compatibility for future memory pooling options.

Software memory management

Frameworks like DeepSpeed and Megatron-LM implement software-based memory optimization through techniques including offloading, activation checkpointing, and memory-efficient attention.¹⁵ These approaches reduce memory requirements, enabling larger models on given hardware or better sharing of available memory.

vLLM and similar inference frameworks implement PagedAttention and continuous batching to improve memory utilization during inference.¹⁶ The memory optimizations enable serving more concurrent requests on the same GPU hardware, improving effective utilization.

Multi-tenant considerations

Multi-tenant GPU sharing introduces challenges beyond single-tenant resource management.

Isolation requirements

Different tenants require varying isolation levels. Development environments may tolerate shared resources with minimal isolation. Production inference requires stronger guarantees that neighbor workloads cannot affect performance or reliability.

MIG provides hardware-backed isolation suitable for multi-tenant production workloads.¹ Memory isolation prevents one tenant from accessing another's data. Compute isolation ensures dedicated processing capacity regardless of neighbor activity.

Quality of service

Multi-tenant clusters require QoS mechanisms ensuring fair resource allocation under contention.¹⁷ Without QoS enforcement, aggressive workloads can starve neighbors of GPU cycles. Admission control and scheduling policies maintain fairness across tenants.

Priority classes enable differentiation between workloads with different service level requirements. Batch training jobs may accept preemption while inference workloads require guaranteed resources. The priority system enables efficient resource use while protecting critical workloads.

Chargeback and accounting

Multi-tenant clusters need usage accounting for cost allocation across teams or customers. GPU utilization metrics enable consumption-based chargeback models. The accounting ensures teams bear costs proportional to their actual resource consumption.

Metering granularity affects chargeback accuracy. GPU-level metering undercharges when time-slicing multiplexes many workloads. MIG-aware metering attributes consumption to specific instances, improving accuracy for shared GPUs.

Implementation guidance

Organizations implementing GPU sharing should follow structured approaches balancing utilization gains against operational complexity.

Assessment and planning

Workload characterization identifies sharing opportunities. Memory-bound workloads benefit from MIG partitioning matching their requirements. Compute-bound workloads may achieve better utilization through time-slicing. The analysis guides technology selection.

Utilization baseline measurement establishes improvement potential. Organizations with high baseline utilization see smaller gains from sharing than those with substantial idle capacity. The measurement justifies investment in sharing infrastructure.

Gradual rollout

Start sharing in development environments where isolation requirements are lowest. Teams gain familiarity with sharing mechanisms without risking production workloads. The experience informs production deployment decisions.

Expand to batch training workloads next. Training jobs typically tolerate variable performance better than latency-sensitive inference. The batch workload expansion builds operational confidence.

Deploy inference sharing last, with careful attention to latency monitoring. Inference workloads have strictest performance requirements. Production validation should confirm sharing doesn't violate latency SLAs before widespread deployment.

Professional support

GPU sharing implementation requires expertise spanning Kubernetes, NVIDIA software, and workload optimization. Most organizations benefit from professional support accelerating deployment and avoiding common pitfalls.

Introl's network of 550 field engineers support organizations implementing GPU sharing and resource pooling infrastructure.¹⁸ The company ranked #14 on the 2025 Inc. 5000 with 9,594% three-year growth, reflecting demand for professional infrastructure services.¹⁹

Multi-tenant clusters across 257 global locations require consistent sharing practices regardless of geography.²⁰ Introl manages deployments reaching 100,000 GPUs with over 40,000 miles of fiber optic network infrastructure, providing operational scale for organizations implementing sharing at enterprise scale.²¹

Decision framework: GPU sharing strategy

Sharing Technology Selection:

MIG Configuration Guide (A100 80GB):

Rollout Strategy by Environment:

Cost Savings Potential:

Key takeaways

For platform teams: - MIG provides hardware-backed isolation—use for production multi-tenant deployments - 75% of organizations report GPU utilization below 70%—sharing addresses massive waste - Time-slicing works on older GPUs—V100 and earlier without MIG support - Combined MIG + time-slicing enables finest-grained sharing—but adds complexity

For cluster administrators: - NVIDIA GPU Operator automates MIG configuration—declarative partition management - Mixed strategy device plugin exposes all MIG instances—maximum scheduling flexibility - ResourceQuotas per namespace enforce fair sharing—prevent monopolization - Metering at MIG level enables accurate chargeback—GPU-level undercharges

For finance teams: - GPT-4 training achieved only 32-36% utilization on 25,000 A100s—sharing critical for ROI - Time-slicing can deliver 90% cost savings—10 jobs on 1 GPU vs. 10 GPUs - MIG enables consumption-based billing—charge by partition, not whole GPU - Sharing infrastructure pays back in months—not years

The utilization imperative

Intelligent GPU allocation means that rather than assigning GPUs as monolithic units, systems can automatically match workloads to slices or classes of GPU resources.²² The evolution from monolithic allocation to intelligent sharing represents fundamental progress in GPU resource management.

Organizations investing in GPU infrastructure cannot afford the 30-50% utilization rates common in enterprise deployments. Memory pooling and sharing technologies provide paths to dramatically improved utilization without proportional cost increases. The organizations that master GPU sharing extract more value from their accelerator investments than competitors leaving expensive resources idle.

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