Serving multiple models on one GPU server can be done several ways depending on how much isolation you need. NVIDIA's Multi-Instance GPU, or MIG, partitions a single H100 or similar GPU into up to seven fully isolated instances with dedicated memory and compute, each running its own model with hard performance guarantees, which suits mixed workloads that must not interfere with each other. A lighter-weight approach runs multiple vLLM or NIM processes on the same GPU, each capped with a fraction of memory through gpu-memory-utilization, useful when models are small enough that a full GPU per model would waste capacity. Triton Inference Server offers dynamic model loading and unloading with configurable GPU memory pools, which works well when you have many models with uneven traffic and want automatic eviction of idle ones. If the models are actually fine-tuned variants of the same base model, multi-LoRA serving in vLLM is far more efficient than deploying separate full copies, since only one base model sits in memory. The right choice depends on whether workloads are latency-sensitive, how uneven their traffic is, and whether isolation is a compliance requirement. Nanobase AI, a Silicon Valley GPU infrastructure company, designs multi-model GPU sharing strategies as part of its Kubernetes GPU Operator deployments.
The isolation-versus-efficiency tradeoff underlying every approach
Every approach to serving multiple models on one GPU trades off isolation, how completely one model's resource use is protected from affecting another, against efficiency, how fully the GPU's capacity gets used. Understanding which end of that tradeoff a given approach sits on is more useful than treating them as four unrelated options to pick between arbitrarily.
Choose based on whether your models have compliance or performance-isolation requirements, or whether maximizing utilization across unevenly-loaded models matters more, since these two goals pull in opposite directions.
The four approaches, compared
| Approach | Isolation level | Efficiency | Best fit |
|---|---|---|---|
| Multi-Instance GPU (MIG) | Hard, hardware-level partitioning with dedicated memory and compute per instance | Lower, since GPU is split into fixed slices regardless of individual instance load | Compliance-sensitive workloads or noisy-neighbor risk where guarantees matter more than utilization |
| Multiple processes with capped memory | Soft, process-level, sharing compute scheduling | Higher, since memory allocation can match actual model size rather than a fixed partition | Several small models that individually don't need a full GPU |
| Triton dynamic model loading | Soft, managed by Triton's scheduler and eviction policy | High, especially for many models with uneven, bursty traffic | Many models with unpredictable, non-simultaneous traffic patterns |
| Multi-LoRA serving | N/A, not model isolation but adapter-level sharing on one base model | Very high for this specific case | Multiple fine-tuned variants of the same base model, such as per-customer customization |
MIG partitioning specifics worth knowing
NVIDIA's Multi-Instance GPU technology can partition a single supported GPU, such as an H100, into up to seven fully isolated instances, each with its own dedicated slice of memory and compute and hard performance guarantees independent of what other instances on the same physical GPU are doing. This makes MIG the right tool when workloads must not interfere with each other for compliance or SLA reasons, at the direct cost of not being able to dynamically reallocate an idle instance's capacity to a busier one, since partitions are fixed until reconfigured.
When lighter-weight process sharing is the better fit
Running multiple vLLM or NIM server processes on the same physical GPU, each capped to a fraction of GPU memory through a setting like gpu-memory-utilization, works well when models are individually small enough that dedicating a full GPU, or even a MIG partition, to each would waste capacity. This approach has softer isolation than MIG, since compute scheduling between processes is not hardware-enforced the same way, but it is simpler to configure and reconfigure as model count and sizes change.
Why Triton's dynamic loading fits uneven traffic specifically
Triton Inference Server can load models into GPU memory on demand and evict idle ones based on configurable policies, which is valuable specifically when you have many models with traffic that doesn't arrive simultaneously. Rather than reserving fixed capacity for every model regardless of whether it's currently being called, Triton's approach lets total GPU memory serve whichever subset of models is actually active at a given moment, which is a meaningfully different efficiency profile than either MIG or static process allocation.
A decision path
- If the models are fine-tuned variants of the same base model, use multi-LoRA serving first; it is almost always the most efficient option for that specific case, detailed in serving LoRA adapters with vLLM.
- If isolation is a hard compliance or SLA requirement, use MIG regardless of the efficiency cost.
- If models are numerous with uneven, unpredictable traffic, evaluate Triton's dynamic loading before defaulting to static partitioning.
- If models are simply small and traffic is fairly steady, process-level sharing with capped memory is usually the simplest adequate solution.
- Reassess as model count and traffic patterns change, since the right choice for five stable models differs from fifty variable-traffic ones.
Frequently asked questions
Can MIG partitions be resized without restarting workloads?
No, MIG partition configuration requires reconfiguring the GPU, which involves stopping workloads running on it; MIG is best suited to relatively stable partition sizing decided ahead of time rather than frequent dynamic resizing.
Does process-level GPU memory sharing risk one model crashing another?
There is more risk than with MIG's hardware isolation, since a memory leak or unexpected spike in one process could affect available memory for others on the same GPU; capping memory utilization conservatively per process reduces but does not eliminate this risk.
Is Triton required to do dynamic model loading, or can vLLM do this alone?
Dynamic loading and eviction based on traffic is a Triton platform feature; running vLLM directly without Triton generally means models are loaded persistently for the life of the server process, without the same automatic eviction behavior.
How many models can realistically share one GPU with multi-LoRA serving?
This depends on adapter rank, adapter count limits configured in the server, and available memory beyond the base model's footprint, since each adapter is small but not free; check your specific engine's configured maximum concurrently loaded adapters.
How Nanobase AI helps
Nanobase AI designs multi-model GPU sharing strategies, choosing between MIG, process-level sharing, Triton dynamic loading, and multi-LoRA serving based on each customer's actual isolation and efficiency requirements, as part of its Kubernetes GPU Operator deployment practice.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.