Running a large language model on your own infrastructure can be cost-effective when demand is steady enough to keep the capacity busy and the benefits of control, data locality, or integration justify the operational work. For low, variable, or bursty demand, a commercial inference API may be easier to scale because you pay for usage rather than planning for peak simultaneous load. There is no universal break-even traffic level: the answer depends on workload, model quality, latency, utilization, and the full cost of each option.
What does “on-premises LLM” mean?
An on-premises deployment runs inference on infrastructure your organization owns or operates in its own facility. That may mean servers in a company data center, not necessarily a server in the same office as the users. It differs from a commercial API, where a provider operates the inference infrastructure and charges according to its service and usage terms. Hosted dedicated capacity sits between those choices: infrastructure is hosted for you, but capacity and commercial terms may differ from a usage-based API.
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These arrangements shift responsibility rather than remove it. With owned infrastructure, your organization plans and maintains capacity. With an API, the provider manages the underlying servers, but your application still depends on the provider’s service, availability, and capacity.
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Owned capacity has a substantial fixed-cost component. The economics can improve when a predictable workload uses that capacity consistently over the period you are evaluating. If demand is sparse or changes sharply, the same servers may sit idle for long stretches, while infrastructure sized for rare peaks raises acquisition and operating costs.
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That does not establish a universal utilization target or break-even number of tokens. Pan and Wang’s August 30, 2025 arXiv preprint presents a cost-benefit framework for on-premises open-model deployment versus commercial services, but the threshold in any real case depends on local prices, utilization, performance, and the assumptions used. Treat the result as a calculation for your workload, not a general rule.
Situations that may favor owned infrastructure
- Demand is sustained and reasonably predictable, so purchased capacity can serve useful work for much of the time.
- Data locality, privacy, control, compliance, or integration requirements carry meaningful value for the organization. These can motivate local deployment, but they are not automatically cash savings.
- You can operate the model and infrastructure reliably, including software, hardware support, monitoring, and failover.
Situations that may favor an API
- Traffic is low, bursty, or difficult to forecast, making fixed capacity likely to be underused.
- You want to avoid procuring and operating accelerators and can meet your service requirements through a provider.
- You need flexibility to scale usage without buying for a maximum simultaneous-load scenario.
These are decision signals, not a verdict. Compare alternatives using the same workload, model quality, latency objective, and service expectations.
What should a total-cost comparison include?
Compare costs over the same time horizon and divide by useful output: responses that meet your quality and latency requirements, not just theoretical peak tokens. The NVIDIA Developer Technical Blog’s June 18, 2025 guidance, updated July 15, describes a total-cost methodology that includes hardware depreciation, hosting fees, and software licensing. A practical budget should also account for relevant facility and operating costs.
| Cost area | On-premises or owned capacity | Commercial API |
|---|---|---|
| Compute | Accelerator servers and associated hardware, including depreciation or financing and support. | Input and output usage charges under the provider’s applicable model and service terms. |
| Facilities and operations | Power, cooling, networking, storage, implementation, staffing, maintenance, and spare or failover capacity where applicable. | Include any service, support, or integration costs that apply to your chosen arrangement; do not assume they are included or absent without checking the terms. |
| Capacity and utilization | Account for idle time, peak-load headroom, resilience, and maintenance windows. | Model expected usage and any available caching or batching effects only when they apply to the service and workload. |
| Change and transition | Include deployment effort and the cost of changing hardware or software configurations. | Include migration costs and the implications of changing model or provider. |
Local prices and provider rates depend on region, date, configuration, and service terms. The available sources do not establish current universal prices or a neutral, comparable cost-per-gigabyte figure for accelerator memory. Avoid treating any single hardware quote or token rate as a general benchmark.
How do you estimate the hardware and model-instance count?
There is no reliable GPU count without a workload and a tested configuration. Model size matters, but so do accelerator memory capacity and bandwidth, software compatibility, precision, prompt and output lengths, concurrency, and the response-time target. Estimate against the peak simultaneous load you must serve, then account for normal utilization and resilience rather than sizing from average requests alone.
- Define the workload. Record model and quality requirements; distributions of input and output token lengths; peak concurrent requests; traffic variability; response-time objectives; availability needs; data-handling constraints; and whether jobs are interactive or batch.
- Choose candidate stacks. For each candidate model, accelerator, serving framework, and precision configuration, establish what can run within available memory and identify software compatibility requirements.
- Benchmark at representative concurrency. Measure time to first token, inter-token latency, token throughput, requests per second, memory use, and energy. Test representative prompts and outputs rather than relying on a peak-throughput figure from a different workload.
- Plot latency against throughput. Reject configurations that miss the application’s quality-of-service target. Interactive response constraints can reduce available throughput; offline jobs may instead benefit from larger batches and higher utilization.
- Size for peak load and resilience. Estimate the capacity needed for peak demand, then model headroom, failure capacity, and maintenance windows. Check what happens to latency and throughput when a component is unavailable or demand rises.
- Recheck cost using measured output. Apply the tested performance and planned utilization to the cost model. Do not turn a benchmark’s best-case throughput into an assumed production average.
NVIDIA’s June 18, 2025 Developer Technical Blog, updated July 15, describes inference metrics and a total-cost methodology, and identifies GenAI-Perf for measurement. It discusses applying the tool to OpenAI-compatible endpoints, including vLLM and SGLang; this is vendor tooling guidance, so validate results on the specific stack and workload you plan to use.
Why do latency and workload shape change the calculation?
Two deployments serving the same model can have different economics if one must answer interactively and the other can process jobs in batches. An interactive product may require low time to first token and consistent inter-token latency. Those constraints can limit batching or the throughput a configuration can deliver. An offline workload can often wait, allowing larger batches and a different balance between latency and utilization.
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The NVIDIA Developers’ August 2024 sizing presentation explains this latency-throughput distinction and the fixed-capacity planning problem for on-premises infrastructure. Use the service level your application actually needs: a configuration that is inexpensive per token at high batch sizes is not a good comparison if it cannot meet interactive response targets.
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Workload design can change the required deployment as well. Retrieval-augmented generation, fine-tuning, or a hybrid architecture may affect compute, storage, data movement, and operational needs. Dong and Xie’s May 27, 2024 survey discusses deployment strategies, computational requirements, tokenomics, and sustainability, but does not determine which architecture is right for a particular organization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you account for energy use?
Do not assign a universal energy-per-token value to an LLM. Energy use can vary with the model, prompt attributes, serving framework, and workload. Measure the candidate configuration under representative conditions and include power and cooling in the owned-infrastructure budget. The 2024 study “The Price of Prompting: Profiling Energy Use in Large Language Models Inference” describes the MELODI energy-monitoring framework and dataset; its existence is not a substitute for measuring your own workload.
How do the deployment options compare?
| Option | Capacity responsibility | Potential fit | Main cost question |
|---|---|---|---|
| On-premises owned infrastructure | Your organization procures, sizes, operates, and maintains capacity. | Steady demand or requirements for control and data locality that justify the operating burden. | Can useful workload keep fixed capacity productive enough to cover total ownership and operating costs? |
| Hosted dedicated capacity | A hosting arrangement supplies dedicated capacity; the precise division of operational responsibility depends on the contract. | Cases where dedicated resources are needed but hosting infrastructure may be preferable to operating an in-house facility. | What do the contract, support, capacity, and hosting terms cost over the same evaluation period? |
| Commercial inference API | The provider operates serving infrastructure; your application consumes the service. | Low, variable, or bursty demand, or a preference to avoid operating inference hardware. | What is the actual usage cost for the selected model, traffic, and service terms, including relevant transition costs? |
For each option, compare workload-matched quality, total cost over the same term, demand variability and utilization, latency and throughput, accelerator memory and software compatibility where relevant, energy and facility constraints, privacy and control, portability, and procurement and capacity risk. No one axis settles the decision by itself.
How should you interpret vendor performance and cost claims?
NVIDIA Perspectives, updated June 17, 2026, reports up to 35x lower cost per million tokens and up to 50x higher throughput per megawatt for GB300 NVL72 versus NVIDIA Hopper in vendor-specified inference scenarios. It also reports a fivefold cost-per-token reduction within two months from software optimization on GPT-OSS-120B using SemiAnalysis InferenceX. These are vendor-published, workload-specific benchmark claims, not independent cross-vendor total-cost results or expected improvements for every deployment. NVIDIA’s page advises verification; assess the benchmark setup and test your own service workload before using such figures in a business case.
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