Dedicated vs shared GPU inference
Every multi-tenant inference API has the same invisible problem: you don't know what the other tenants are running. A spike in their traffic becomes a latency spike in your application. Dedicated GPU inference eliminates that variable at the source — the hardware is yours alone. But single tenancy costs more than a shared endpoint, and the decision depends on whether your workload actually needs it.
Solutions Engineer
What shared GPU inference looks like under load
A shared inference endpoint is a queue. Your requests land in the same scheduling pool as every other tenant on the cluster. The GPU scheduler — vLLM, SGLang, TensorRT-LLM — makes no distinction between your traffic and theirs. When another tenant submits a batch of 50 long-context requests, your p50 latency holds steady. Your p99 spikes by 3-10x. Your throughput collapses because the KV cache space you were using is now occupied by someone else's 128k-token document analysis. None of this is a bug. It's how multi-tenant scheduling works.
The noisy neighbor taxonomy
Shared GPU inference has three failure modes that dedicated hardware eliminates outright:
- Throughput theft. Another tenant's burst consumes HBM bandwidth and SM utilization that your requests were using. Your tokens-per-second drops in real time, not gradually.
- KV cache eviction. A long-context workload from another tenant forces your cached prefixes out of HBM. Every subsequent request from your application pays the full prefill cost again, doubling or tripling time-to-first-token.
- Tail latency amplification. Even if the median request completes on time, the 99th percentile blows out because your request happened to land during another tenant's traffic spike. For real-time applications — chatbots, voice agents, live coding assistants — tail latency is the only latency that matters.
What dedicated hardware gives you
Dedicated GPU inference means the B200 or B100 nodes are yours alone. Your continuous-batching scheduler manages only your requests. Your KV cache serves only your sequences. The HBM bandwidth, the tensor cores, the NVLink fabric — all of it carries your traffic and nothing else. The result is flat, predictable throughput at any concurrency level the hardware can sustain. If an 8xB200 node delivers 300+ tokens per second on DeepSeek-V4 Flash, it delivers that at 9 AM on a quiet Tuesday and at 3 PM during a market event when every other tenant on the shared cluster would be peaking. The hardware doesn't know what day it is.
The privacy dimension
Throughput and latency are the performance arguments for single tenancy. But privacy is the regulatory one. On shared hardware, your prompts and another tenant's prompts coexist in HBM simultaneously. Memory isolation between processes is strong but it's software-enforced, and history has not been kind to software-enforced isolation guarantees. Dedicated GPUs remove the question entirely: there is no other tenant's data in memory because there is no other tenant. For HIPAA, PCI DSS, and defense workloads, that distinction is the difference between a deployment your security team approves and one they kill on the architecture diagram.
The cost tradeoff
Dedicated GPUs cost more than an equivalent shared endpoint because you're paying for reserved capacity whether or not every FLOP is utilized. But the comparison isn't straightforward. A shared endpoint with volatile latency often forces you to over-provision: running more concurrent workers, adding client-side retry logic, and paying the engineering cost of debugging intermittent timeouts that trace back to another tenant's traffic. A dedicated node gives you a known throughput ceiling and a known latency floor. You provision to your actual requirements, not to a worst-case scenario you can't control. For steady, high-volume regulated workloads, dedicated hardware is often cheaper when you account for the operational overhead of managing multi-tenant unpredictability.
When shared is fine, and when it isn't
Shared inference works for experimentation, non-critical batch processing, and workloads where tail latency doesn't affect user experience. If you're classifying a backlog of support tickets overnight, a 10x p99 spike at 3 AM doesn't matter. Dedicated hardware is the right call when latency is user-facing — chatbots, code completion, real-time document processing — and when the data is sensitive enough that co-location in GPU memory is an audit finding. The deciding question is simpler than most teams make it: if another tenant's traffic spike made your application visibly slow right now, would that be acceptable? If the answer is no, you need dedicated GPUs. See HyperInfer's benchmarks for per-model throughput and latency on dedicated B200 nodes.
Related reading
- SecurityWhy single-tenant GPUs matter for privacyMulti-tenant GPUs share HBM, schedulers and side-channel surface with unknown code. Single-tenant architecture is the difference between logical isolation and genuine hardware dedication.
- Performance300+ tokens/sec from open models on B200sContinuous batching, speculative decoding and custom kernels — the techniques behind tier-1 throughput on dedicated single-tenant hardware.
- GuideVPC vs on-prem vs air-gapped inferenceVPC, on-prem, private cloud or air-gapped — break down the four private LLM deployment models across data boundaries, operational burden, and compliance readiness.
- PerformanceThe real cost of LLM inference at scalePer-token API pricing looks simple until volume climbs. The real cost lives in GPU-hours, utilization, ops, and egress. Here's how to model break-even and when dedicated wins.
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