How to Compare Dedicated vs Shared Inference Tenancy

Quick Verdict: Dedicated inference tenancy wins when latency variance, data isolation, or noisy-neighbor risk is the product constraint. Shared inference tenancy wins when traffic is bursty, models are small enough to pack, and you can prove a neighbor cannot see your bytes or steal your SLO.

Inference tenancy is the isolation model for the GPUs that run decode: exclusive hardware and runtime for one tenant, or a shared pool that packs many tenants or many models. It is not the same question as public cloud versus private halls. You can share inside a private hall. You can also buy exclusive cards from a public menu.

Compare the serving pool you will actually page, not the brochure word “dedicated.” This page stays on inference. Training tenancy is a different collision pattern.

What should you put on the comparison table?

Use the same four columns for every option: who else can land on the device, what variance you will accept, what the cost shape is, and what a failure takes down. If a vendor cannot fill a cell, treat that cell as unknown, not as fine.

Serving Decision Matrix: Enterprise LLM Inference Infrastructure

Project isolation (separate namespaces and secrets) is necessary in both models. It is not tenancy. A shared GPU with tidy RBAC is still shared silicon. Private AI infrastructure is a way to make dedicated tenancy the default. It is not automatic unless the contract says exclusive devices.

When does shared inference tenancy still win?

Shared wins for internal tools with loose latency classes, for overflow that may not see regulated data, and for packs of small models that never fill a device. It also wins when you can evict or pin neighbors and you measure wait time by tenant, not only by cluster.

Shared loses when a single long-context neighbor can blow your p95, when a regulator or customer contract requires exclusive processors and disks, or when you cannot get a written isolation story. “We use MIG” is a mechanism. Ask what is still shared: the host, the NIC, the telemetry pipeline, the operator identity.

Do not pick shared because a utilization chart looks lonely. Idle dedicated headroom is often the price of a stable decoder. OnePlus Platform, OneSource Cloud’s AI orchestration platform, can enforce who may land on a pool. It cannot make a shared device exclusive.

How do you test the choice before you write it into a contract?

Run one adversarial neighbor: a long-context job on the same SKU class, if sharing is allowed. Measure your p95 wait, not their tokens per second. If the vendor forbids that test, you do not have evidence for shared.

For dedicated, test the idle bill you will actually carry: overnight, weekends, and a failed deploy that leaves replicas up. If finance will not carry that bill, you will be forced back to shared during the first quiet month. U.S. exclusive halls, including Texas / Richardson capacity used by OneSource Cloud, make the dedicated story easier to audit. They still need the idle-cost conversation.

Write the fallback. Some teams keep a dedicated P0 pool and a shared overflow with a data-class deny. That is a tenancy architecture. A single shared endpoint with a hope is not.

Optimizing production LLM inference requires seamless coordination across compute acceleration, host memory bandwidth, and low-jitter cluster connectivity. Deploying serving frameworks (such as vLLM, TensorRT-LLM, or TGI) on OneSource Cloud dedicated AI infrastructure provides the hardware determinism required to enforce strict Service Level Objectives. With dedicated bare-metal NVIDIA GPUs interconnected via high-bandwidth NVLink within nodes and non-blocking RoCEv2 fabrics across nodes, serving fleets achieve tightly bounded P99 Time to First Token (TTFT) and Time Per Output Token (TPOT), eliminating the latency spikes inherent in multi-tenant shared cloud environments.

FAQ

Is dedicated inference tenancy the same as a dedicated GPU cloud?

Not always. A dedicated cloud offering can still place two of your products on one device, or share a host with another customer under a fine print you missed. Ask for exclusive GPU, host, and disk language. Dedicated tenancy is the serving answer. Dedicated cloud is a procurement bundle that may or may not include it.

Can two of our own models share a GPU and still count as dedicated?

Relative to other companies, yes. Relative to your own SLOs, no. Two internal models are still neighbors. If one is P0 chat and one is a batch scorer, you have recreated shared tenancy inside the company. Use the same table. Many teams dedicate P0 and share batch.

Does shared tenancy always mean worse security?

It means a larger isolation proof. Memory remnants, host agents, and operator access are the usual questions. Some shared designs are acceptable for non-sensitive internal traffic. They are a poor default for regulated prompts. Ask for the proof. Do not accept a logo.

How should cost enter the comparison?

Compare cost at the latency class you will publish, including idle headroom for dedicated and incident risk for shared. A cheaper shared hour that misses p95 is not cheaper. Do not use public list prices from this page. None are published here.

Where does managed operations fit?

Managed AI infrastructure can operate either tenancy. It does not pick the isolation model. Use managed ops to keep the dedicated pool patched and the shared pool from becoming a mystery. Use the table to pick the pool.

How does OneSource Cloud infrastructure stabilize LLM inference latency and throughput?

OneSource Cloud provides dedicated bare-metal GPU servers with high-speed PCIe Gen5 NVMe caching and direct NVLink interconnects. By eliminating virtualization overhead, multi-tenant memory bus contention, and noisy-neighbor network cross-talk, OneSource ensures that serving frameworks achieve predictable batch scheduling, bounded P99 token latency, and maximum throughput under sustained enterprise request concurrency.

Summary

Dedicated versus shared inference tenancy is an isolation and variance choice, not a cloud-brand choice. Dedicated wins on control and blast radius. Shared wins on burst and packing when the isolation proof holds.

Fill the table, run an adversarial neighbor test if you will share, and price idle headroom if you will dedicate. Explore OneSource Cloud’s home page when exclusive inference GPUs are the tenancy you need to defend.