The 10 Best Fractional GPU and GPU Sharing Tools in 2027

Hardware partitioning versus software fractions

The field of fractional GPU and GPU sharing tools splits into two families, and almost every purchasing mistake comes from confusing them. Hardware partitioning — NVIDIA Multi-Instance GPU (MIG) on A100, H100, H200 and newer data-center parts — carves the physical die into as many as seven instances, each with its own dedicated slice of memory, L2 cache, memory bandwidth, and streaming multiprocessors. The split is enforced below the driver, so a workload in one instance physically cannot read another instance's memory, cannot steal its bandwidth, and cannot degrade its latency. That property is what makes MIG the default answer for multi-tenant inference, for regulated environments, and for anything where a latency SLO is written into a contract. The cost is rigidity: MIG profiles are fixed sizes (a 1g.10gb slice, a 2g.20gb slice, a 3g.40gb slice, and so on), reconfiguring usually requires draining the GPU, and you cannot ask for "37 percent of a card."

Software fractions take the opposite trade. Tools in this family — Run:ai (now part of NVIDIA), HAMi, and the fractional scheduling built into the open-source KAI Scheduler — intercept at the driver, container-runtime, or scheduler layer and enforce limits in software. A pod can request 4 GB of VRAM and 30 percent of compute, and the middleware refuses allocations past that ceiling. The fractions are arbitrary, they can change without draining a node, and they work on GPUs that have no MIG support at all, which matters enormously for fleets built on L4, L40S, RTX-class, or older V100 hardware. The weakness is that isolation is advisory rather than physical: a badly behaved kernel can still cause contention, a driver-level fault can still take down neighbors, and performance is less predictable under load.

A third mechanism sits underneath both. Time-slicing through the NVIDIA GPU Operator's device plugin simply interleaves access — each container gets the whole GPU for a quantum, then yields. There is no memory isolation whatsoever; the sum of all containers' allocations must fit in physical VRAM or someone gets an out-of-memory error. Multi-Process Service (MPS), a free part of the CUDA toolkit, is subtly different: it lets multiple CUDA processes execute *concurrently* on the same device rather than taking turns, which raises throughput for many small kernels that individually cannot fill the GPU. MPS is the right tool when you have dozens of small inference processes; time-slicing is the right tool when you have notebooks and dev pods that are idle 90 percent of the time.

The practical read: hardware partitioning buys you a guarantee, software fractions buy you flexibility, and the free NVIDIA mechanisms buy you utilization at near-zero effort. Most mature platforms run all three simultaneously — MIG for the production inference tier, software fractions for shared research, time-slicing for the notebook fleet — with one scheduler enforcing fair share across the whole thing.

Choosing a mechanism for your workload mix

Start from the workload, never from the tool. Three questions decide almost every case: does this workload have a latency SLO, does it fit in a fraction of VRAM, and do the tenants trust each other?

If the answer to the SLO question is yes and tenants are untrusted, you are on the hardware-partitioning path and the only real question is whether your GPUs support MIG. If they do, MIG plus the GPU Operator is the answer and it costs nothing beyond hardware you already own. If they do not — L4, L40S, consumer cards, older accelerators — you cannot get hardware isolation, and the honest move is to dedicate full GPUs to the latency-sensitive tier rather than pretend a software fraction is equivalent.

If the workload is bursty and internally trusted (notebooks, feature engineering, ad-hoc evaluation, CI jobs that touch a GPU for ninety seconds), time-slicing is almost always the highest-return change available, because those workloads leave the accelerator idle the overwhelming majority of the time. Turning on time-slicing with a replica count of 4 to 8 on a development node typically lets four to eight times as many people work on the same hardware with no user-visible change, because they were never contending in the first place.

If you need arbitrary fraction sizes on non-MIG hardware, or you need per-team quotas, preemption, and gang scheduling for distributed training, you are in software-fraction territory. HAMi, a CNCF sandbox project, gives memory-and-compute slicing across NVIDIA and several non-NVIDIA accelerators for free. KAI Scheduler, open-sourced by NVIDIA out of the Run:ai codebase, adds fair-share quotas, gang scheduling, and workload priority. Run:ai itself is the commercial option when you also want the console, the reporting, and the support contract. nos automates applying MIG and time-slicing profiles dynamically so the cluster reshapes itself around pending pods instead of an operator hand-editing profiles.

There are cases where the correct answer is not to share at all. Large-model training that needs the full HBM and contiguous compute should own the card. Sub-millisecond inference breaks under time-slicing because a scheduling quantum is longer than the SLO. And some compliance regimes forbid shared memory space entirely, hardware isolation or not — in which case the conversation is over and you dedicate.

The numbers that decide the business case

The case for fractional GPU and GPU sharing tools is arithmetic, and the arithmetic is worth doing explicitly before you commit engineering time.

Begin with measured utilization, not assumed utilization. Export DCGM_FI_DEV_GPU_UTIL and DCGM_FI_DEV_FB_USED from the DCGM exporter that ships with the GPU Operator and look at the p50 and p95 over two full weeks. The distribution matters more than the average: a fleet averaging 30 percent because every card runs at a steady 30 percent is a superb candidate for sharing, while a fleet averaging 30 percent because a third of cards are pinned at 95 percent and the rest sit at 2 percent needs *placement* changes before it needs slicing. Memory tells you the ceiling on density directly — if your typical serving process holds 6 GB resident on an 80 GB H100, you have room for many tenants; if it holds 55 GB, you have room for one.

Then compute density against a MIG profile. An 80 GB H100 split into seven 1g.10gb instances gives seven isolated tenants of roughly 10 GB each. Split into three 2g.20gb instances plus spare, you get three tenants with meaningfully more compute apiece. The seven-way split only works if every model actually fits in 10 GB — a 13B model in FP16 needs roughly 26 GB of weights alone and will not, while the same model quantized to 4-bit lands near 7 GB and will. Quantization and slicing are the same conversation; deciding them separately produces a profile that nothing fits into.

Cost per served workload is the number to put in front of finance. If a card costs $2 per hour on-demand and one model owns it, that model costs $1,440 per month in compute. Put six models on the same card via MIG or software fractions and the same $1,440 covers six services — a per-service cost of $240. The savings are real but they are bounded by density, so resist quoting a percentage: report the actual before-and-after cost per workload from your own measurements, because the density you can achieve depends entirely on your model sizes and traffic shape. Publishing a made-up percentage to leadership is how a sharing program loses credibility the first time someone audits it.

Guardrails have numbers too. Reserve 10 to 15 percent of VRAM per GPU for driver, CUDA context, and framework overhead — each additional CUDA context on a shared device costs real memory, and eight contexts on one card is not free. Keep aggregate allocation at or below roughly 85 percent of physical VRAM in production. Never enable memory oversubscription that spills to system RAM for memory-bound workloads; the throughput collapse when a training step starts paging over PCIe is severe enough that the job effectively stops making progress. And set time-slice replica counts deliberately: a replica count of 4 on a dev node is a reasonable starting point, 16 is a way to make sixteen people simultaneously unhappy.

Finally, model the operational cost honestly against the revenue or budget line the program is meant to protect. Commercial platforms carry a license; open-source tools carry the engineering time to run them. The tools are Best judged not by their feature matrix but by cost per served workload after six months of real traffic, and that number only exists if you instrument before you migrate.

Rolling it out without breaking production

Sequencing matters more than tool choice, because the failure mode of a sharing rollout is not a bad tool — it is a good tool applied to a tier that could not tolerate it.

Instrument first. Deploy the NVIDIA GPU Operator with the DCGM exporter and collect two weeks of per-GPU utilization and framebuffer usage before changing anything. You need the baseline to prove the outcome and to size profiles.

Start on the tier with the least to lose. Development, notebooks, and CI. Enable time-slicing through the device plugin config with a modest replica count, run it for a week, and watch for OOM kills — the signature failure of time-slicing is not slowness, it is a pod dying because the sum of resident allocations exceeded physical VRAM. If OOMs appear, lower the replica count or move that tier to memory-enforced software fractions instead.

Move the inference tier next, and use hardware isolation if you can. Pick MIG profiles from measured memory footprints plus headroom, apply them to a canary node, and shift a fraction of traffic. Watch p99 latency, not p50 — sharing problems show up in the tail long before they show up in the median. Keep a full-GPU node in the pool as an escape hatch for any model that turns out not to fit.

Add quota enforcement once two or more teams share the pool. This is where fair-share scheduling stops being optional: without it, whichever team submits the most jobs takes the cluster. KAI Scheduler or Run:ai gives you per-team quotas, preemption of over-quota work, and gang scheduling so a distributed training job either gets all its workers or none — a half-scheduled multi-worker job holds GPUs while making zero progress, which is the single most expensive scheduling bug in shared clusters.

Automate profile management last. Once the shape of demand is understood, nos can apply MIG and time-slicing profiles dynamically based on pending pods, so the cluster reconfigures itself instead of an operator editing profiles by hand. Automating this before you understand the demand shape means automating the wrong thing.

Two operational details deserve their own mention. First, MIG reconfiguration is disruptive — plan it as a drain-and-cordon maintenance operation, not a live change. Second, observability must be per-slice, not per-card, or you lose the ability to attribute a problem to a tenant; DCGM reports per-MIG-instance metrics and your dashboards should use them.

Managed options shorten all of this. GKE exposes native GPU time-sharing and MIG as node-pool configuration, so a team already on GKE can enable sharing without operating the partitioning layer. Amazon SageMaker multi-model endpoints take a different angle entirely: many models share one GPU-backed endpoint with the platform handling load-on-demand and routing, which is often the fastest path to density for inference-only teams who do not want a cluster at all.

Where each tool actually fits

MIG is the production inference default on supported data-center GPUs, free with the hardware, and the only option that survives a security review for untrusted tenants. Time-slicing via the GPU Operator is the cheapest utilization win in existence for dev and notebook fleets and should be enabled on those nodes essentially by default. MPS is underrated for throughput on many small concurrent CUDA processes and costs nothing. HAMi is the free path to memory-and-compute quotas on Kubernetes across mixed accelerator vendors. KAI Scheduler brings Run:ai-derived fractional sharing, gang scheduling, and fair-share quotas into open source. nos automates the partitioning NVIDIA gives you natively. Run:ai is the commercial platform for organizations that want pooling, self-service, and reporting with a support contract. GKE GPU sharing and SageMaker multi-model endpoints are the managed shortcuts for teams already committed to those clouds.

The wrong question is "which of these is the best fractional GPU tool." The right question is "which mechanism does each of my tiers need, and what one scheduler enforces fairness across all of them." Fractional capacity without quota enforcement just relocates the contention; quota enforcement without fractional capacity leaves the utilization on the floor. Sharing tools and scheduling policy are a single system, and teams that buy only half of it are the ones who report that GPU sharing did not work.

Related questions

Does MIG work on every NVIDIA GPU?

No. MIG requires supported data-center GPUs — A100, H100, H200, and newer architectures. L4, L40S, consumer RTX cards, and older V100s cannot do hardware partitioning at all, so those fleets must rely on time-slicing, MPS, or software-enforced fractions instead.

Can I combine MIG and time-slicing on the same cluster?

Yes, and most mature clusters do. Run MIG on the production inference node pool for isolation and time-slicing on the development pool for density. Label the node pools distinctly so schedulers place workloads on the mechanism appropriate to their tier.

What happens if a shared workload requests more memory than its fraction?

With MIG or memory-enforced software fractions, the allocation fails and the process typically dies with an out-of-memory error — contained to that tenant. With plain time-slicing there is no enforcement, so an over-allocating pod can exhaust physical VRAM and cause OOM kills for its neighbors.

Is GPU sharing worth it if utilization is already high?

Usually not. If p50 utilization sits above roughly 70 percent with a tight distribution, there is little idle capacity to reclaim and sharing mainly adds contention risk. Measure first — sharing pays off on fleets with genuinely idle accelerators, not on saturated ones.

Does gang scheduling matter for fractional GPUs?

It matters for distributed training on any shared cluster. Without it, a multi-worker job can get some workers scheduled and wait indefinitely for the rest while holding GPUs idle. Gang scheduling makes the job all-or-nothing, which prevents that deadlock.

FAQ

What exactly is fractional GPU sharing?

Fractional GPU sharing lets multiple workloads run on one physical accelerator by dividing its memory and compute rather than dedicating the whole card to a single job. Depending on the mechanism, the division is enforced in hardware (MIG), in software at the driver or scheduler layer (HAMi, KAI Scheduler, Run:ai), or by interleaving access over time (time-slicing through the GPU Operator).

Do I need special hardware?

For hardware-level partitioning, yes — MIG only works on supported NVIDIA data-center GPUs such as A100, H100, and H200. Every other mechanism works more broadly: time-slicing, MPS, and software fraction middleware run on essentially any modern CUDA-capable GPU, which is why fleets built on L4, L40S, or consumer cards default to those approaches.

Will sharing slow my workloads down?

MIG slices deliver predictable performance because the isolation is physical, so a well-sized workload sees essentially no interference. Time-slicing and MPS can introduce tail-latency variance when several tenants contend simultaneously. Watch p99 rather than median latency during a canary — contention shows up in the tail long before it shows up in the average.

How do I choose a MIG profile size?

Measure the resident framebuffer usage of the workload with DCGM, add 10 to 15 percent headroom for CUDA context and framework overhead, then pick the smallest profile that comfortably fits. Quantization changes the answer materially, so decide precision and profile size together rather than sequentially.

Is fractional sharing safe for untrusted tenants?

Hardware partitioning with MIG gives each instance dedicated memory and compute paths and is the mechanism to reach for with untrusted tenants. Software fractions and time-slicing share a memory space and a driver context, so treat them as suitable for trusted workloads inside one organization rather than as a security boundary. Some compliance regimes forbid sharing regardless of mechanism.

Which tools should a small team start with?

Start free. Enable time-slicing through the NVIDIA GPU Operator on development nodes, add MPS if you run many small concurrent inference processes, and adopt MIG on any data-center GPUs serving production traffic. Reach for HAMi or KAI Scheduler when you need enforced quotas, and evaluate a commercial platform only once multiple teams contend for one pool.

Sources

• https://docs.nvidia.com/datacenter/tesla/mig-user-guide/
• https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/index.html
• https://docs.nvidia.com/deploy/mps/index.html
• https://github.com/NVIDIA/k8s-device-plugin
• https://github.com/NVIDIA/KAI-Scheduler
• https://github.com/Project-HAMi/HAMi
• https://github.com/nebuly-ai/nos
• https://cloud.google.com/kubernetes-engine/docs/concepts/gpu-sharing
• https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html
• https://github.com/NVIDIA/dcgm-exporter

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