GPU calculator
GPU VRAM fit calculator for open LLMs
GPU VRAM fit calculator for open LLMs is built for teams deciding whether to self-host an open model and on which GPU. Use it to decide whether a model's weights plus KV cache fit a GPU at the chosen context, concurrency, and precision. Keep the workload assumptions consistent across options, then inspect the cited prices and last-checked dates before committing budget.
Open VRAM fit calculator - Check model-to-GPU memory fit →
The decision this page helps you make
Check whether an open model fits a GPU: weights plus KV cache at your context length, concurrency, and precision, from each model's real architecture and the GPU's VRAM.
The practical question is whether a model's weights plus KV cache fit a GPU at the chosen context, concurrency, and precision. Use the same workload assumptions for every option so the comparison reflects billing differences instead of different inputs.
Start with these inputs
- Model: Real architecture from Hugging Face config.
- GPU: VRAM from the NVIDIA datasheet.
- Output: Weights + KV cache vs VRAM, fit verdict, tensor-parallel need.
What the result includes
| Area | What ByteCosts shows |
|---|---|
| Model | Real architecture from Hugging Face config |
| GPU | VRAM from the NVIDIA datasheet |
| Output | Weights + KV cache vs VRAM, fit verdict, tensor-parallel need |
How to use the result
- Run a realistic base case and a heavier-usage case before choosing a provider or plan.
- Compare alternatives with identical traffic, token, seat, runtime, and retry assumptions.
- Open the cited provider source before a purchase or production billing decision.
Formula
monthlyCost = usageVolume * unitCost, adjusted only for the billing units and optional inputs that this calculator exposes.
Assumptions
- Published rates come from committed ByteCosts datasets or visible source-backed rows.
- Calculator outputs are planning estimates, not final invoices.
- Taxes, negotiated discounts, billing minimums, and undocumented limits are excluded unless the page states otherwise.
- Unknown inputs stay unknown until the user supplies them or a source-backed value is available.
Example scenario
Enter a conservative base case, then duplicate it and change one important driver such as usage, retries, utilization, or output volume. Comparing controlled scenarios makes the result easier to explain and audit.
Interpretation guide
- Compare alternatives with identical workload assumptions.
- Stress-test the input that is most likely to grow in production.
- Verify source links and last-checked dates before making a purchase decision.
Limitations
GPU VRAM fit calculator for open LLMs is a planning tool, not a billing guarantee. It uses the visible assumptions and committed source-backed data available at the page's last update.
Check the cited provider page and your own production logs before signing a contract, changing price, or committing infrastructure spend.
Frequently asked questions
What should I enter first in GPU VRAM fit calculator for open LLMs?
Start with model: real architecture from hugging face config. Add optional adjustments only after the base case is understandable.
Is the result a guaranteed invoice forecast?
No. It is a planning estimate based on the visible workload assumptions and source-backed public prices. Taxes, negotiated discounts, undocumented limits, and production behavior can change the final invoice.
Where do the prices and assumptions come from?
ByteCosts keeps provider source links, confidence information, and last-checked dates attached to pricing records. User-entered workload assumptions remain separate from published vendor facts.
GPU VRAM fit calculator for open LLMs. ByteCosts. https://bytecosts.com/tools/gpu-vram-fit/