Choosing GPUs for Enterprise LLM Deployment: Hourly or Monthly Rental?

You are setting up an internal AI assistant or a company knowledge base. Is one RTX 5090 enough? Should you rent an eight-GPU server instead?

Start with the workload, not the card. Model size, document length and simultaneous requests all change the configuration you need. Get the service running, check how it behaves during busy periods, then decide whether to pay by the hour or keep the resources for a month.

This guide uses Compshare's public configurations and prices to work through that decision. It covers renting cloud GPUs and running your own model. If company policy requires data to stay in your own facility, you will need an on-premises server and network plan instead.

Choose the model before choosing 24GB, 32GB or 80GB

First check that deployable weights are available and the license allows your intended use. Renting a GPU does not turn an API-only model into a self-hosted one.

VRAM is the first constraint. Weights occupy part of it; conversation caches and runtime operations need room too. As a rough calculation, an 8B model stored at two bytes per parameter needs about 16GB for weights alone. That is not its total serving requirement.

For a smaller text model, a 24GB RTX 4090 or 32GB RTX 5090 can go on your test shortlist. Larger models, longer documents or more simultaneous conversations may call for an 80GB or 96GB GPU, or a supported multi-GPU setup. Choose the configuration from the model's deployment requirements and your load test.

Quantization reduces weight memory, but check the resulting answers. Use your actual contracts, product documents, code or support questions before committing to a smaller configuration.

Choose CPU, host RAM and disks alongside the GPU. Document ingestion, indexing and model loading use those resources. A quote listing 48GB of RAM usually means host memory, separate from a GPU's 32GB of VRAM.

A hundred employees do not mean a hundred concurrent requests

Employee count helps determine account access. GPU capacity depends more directly on how many requests are generating answers at the same time, and how much content each request carries.

A short question about an expense policy and a request to review a long contract put different pressure on the same GPU. Long documents and ongoing conversations use more cache. Under load, responses that felt quick in a demo can start queueing. The vLLM optimization guide explains how recomputing requests when cache space runs out can increase latency.

A useful test includes short questions, long-document queries and follow-up conversations—not just “hello.” Increase simultaneous requests gradually and record time to first token, full response time and failures. Starting the model proves the setup works. Meeting your service requirements under load shows whether the configuration is adequate.

For batch image or video generation, measure how many acceptable outputs you can produce per hour. Multiple GPUs can run independent jobs or cooperate on one job through a supported runtime. Those are different setups; adding cards is not a throughput calculation.

Monthly rental buys more than a lower hourly equivalent

Hourly rental is convenient for development: finish a test, shut down and stop paying for compute. A service used by colleagues or customers has another requirement: the resources need to be there when you start it.

Compshare's official FAQ says shutting down a pay-as-you-go instance releases its CPU, GPU and RAM. A monthly instance retains its resources after shutdown and remains billable. Saving compute charges while stopped and keeping that allocation are different choices.

If a service needs to be available every morning or throughout the day, ask about monthly rental. Occasional experiments and project batches leave more room for hourly or daily plans. An hourly instance that remains running is not competing for a new GPU on every request; ordinary hourly rental is also distinct from reclaimable spot capacity.

Monthly retention applies to resources already allocated. It does not guarantee stock for an expansion next week. For production, agree on renewal, additional capacity, hardware replacement and the SLA rather than treating the billing term as the entire availability plan.

Budget for a complete instance

These five configurations were checked on Compshare's public price list on October 14, 2026. Multi-GPU rows show the price of the whole instance. VRAM is listed per card.

← Swipe the table to see hourly and monthly prices →

Source: Compshare's official price list. These are public list prices, not account- or region-specific order quotes or confirmation of stock. Extra storage, networking and paid images may add charges. Check the current quote and service terms before ordering.

Using the single RTX 5090 row, with the same configuration and no additional discount:

This compares instance rent, not continuous GPU utilization. Running hours and the need to retain resources both matter. Discounts, the billing period and add-ons can change the outcome; recalculate against the latest order and billing terms. Check cancellation rules too: ending a monthly plan early is not a simple full refund for unused days.

Why include Compshare in an enterprise shortlist?

Compshare publishes prices for GPU, CPU and RAM combinations, offers hourly, daily and monthly billing, and documents image selection and instance management. A team can test a configuration with hourly rental before asking for a longer-term plan, rather than buying a server at the outset.

Its corporate identity is public: Compshare belongs to UCloud, listed under stock code 688158, as shown in the site's footer. Have finance review the business verification process and service framework agreement for payment and invoicing requirements before starting the instance.

For contracts, customer records and source code, confirm the data location, access controls, backups and deletion process. Running the model yourself lets you manage the application, but a rented cloud machine remains external infrastructure. Projects that cannot leave the corporate network need an approved deployment location first.

Quotes against that brief are easier to compare. Use ComputeUnion's GPU rental prices and provider profiles to narrow the shortlist, then confirm the configuration with your own workload.

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Sources and quote check · October 14, 2026

• Compshare: public instance prices and company details
• Supported GPU types and regions
• FAQ: resource retention after shutdown
• Billing: renewal, shutdown and cancellation
• Business identity verification
• Service framework agreement: payments and invoices
• vLLM: cache pressure, concurrency and parallelism

We checked public pages and documentation without creating paid instances or benchmarking model throughput on these configurations. The public price list does not display a separate price-update date; October 14 is our check date. Confirm account-specific quotes, stock and service terms when placing an order.