GPU calculator
LLM serving capacity planner: GPUs for your throughput
Direct answer
LLM serving capacity planner: GPUs for your throughput helps teams planning a self-hosted LLM deployment for a target request rate. Size a self-hosted LLM deployment: from target requests per second and output tokens per request, find how many GPUs you need and the monthly rental cost, with a labeled throughput band. Use this page to decide how many GPUs a target requests/sec needs and the resulting monthly rental cost, then follow the related calculators and source pages to turn the answer into a budget, comparison, or shareable scenario. The prerendered HTML includes the same H1, direct answer, sections, FAQ, related links, and citation data before JavaScript runs, so crawlers and users can understand the page without waiting for the interactive React app.
Open the capacity planner - GPUs for your target throughput →
What this page does
Size a self-hosted LLM deployment: from target requests per second and output tokens per request, find how many GPUs you need and the monthly rental cost, with a labeled throughput band.
It is designed for teams planning a self-hosted LLM deployment for a target request rate. The goal is to make the cost question explicit before the team commits to a model, platform, plan, or workflow.
Use it for
- Deciding how many GPUs a target requests/sec needs and the resulting monthly rental cost.
- Comparing options with the same workload assumptions instead of vendor examples.
- Turning engineering usage into finance-readable monthly cost, margin, or sourcing notes.
Decision inputs
| Area | What ByteCosts shows |
|---|---|
| Demand | Requests/sec and output tokens per request |
| Throughput | Measured benchmark, or a labeled roofline band |
| Output | GPUs needed and monthly rental cost |
Formula
monthlyCost = usageVolume * unitCost, adjusted for token mix, cache hit rate, retry rate, seat count, batch discount, or runtime cost when those inputs apply.
Assumptions
- Provider and model rates come from committed ByteCosts datasets or visible source-backed rows.
- Calculator outputs are planning estimates, not final invoices.
- Taxes, negotiated discounts, rate limits, and provider-specific billing minimums are excluded unless a page states otherwise.
- Unknown inputs stay unknown until the user enters assumptions or the data pipeline has a source-backed value.
Example scenario
Start with a conservative workload, such as 1,000 active users, a fixed number of requests per user, and a known input/output token mix. Run the calculation once with average usage and once with heavy-user usage before choosing a price or provider.
Rendered example output
| Output | Example input | What to inspect |
|---|---|---|
| Average case | Known volume and unit price | Budget range |
| Stress case | Higher usage or retries | Risk signal |
| Decision | Same assumptions across options | Cheaper path |
Interpretation guide
- Use the result as a budgeting range and compare alternatives with the same assumptions.
- Stress-test output-heavy, retry-heavy, and power-user scenarios because they often change the winner.
- Verify source links and last-checked dates before production billing decisions.
Common mistakes
- Comparing providers with different token mixes.
- Ignoring output tokens, retries, cache misses, or heavy-user behavior.
- Using a planning estimate as a final invoice forecast without checking provider source pages.
Limitations
LLM serving capacity planner: GPUs for your throughput is a planning surface. It does not fetch live provider data at runtime, does not include negotiated discounts unless a source-backed row includes them, and does not guarantee the invoice you will receive.
Use the cited source pages, ByteCosts methodology, and your own logs before making production billing or pricing decisions.
Frequently asked questions
Is LLM serving capacity planner: GPUs for your throughput usable without JavaScript?
Yes. The static HTML includes the page summary, direct answer, sections, related links, and citation block. JavaScript enhances the interactive tool or navigation when it is available.
Where do the numbers and assumptions come from?
ByteCosts links calculator assumptions back to the provider pricing index, source pages, and methodology notes so you can verify the evidence before using it in a budget.
Cite this page
LLM serving capacity planner: GPUs for your throughput. ByteCosts. https://bytecosts.com/tools/llm-capacity-planner/