How fast will a model run on your hardware?
Pick a model and your GPUs to get speed and memory-fit projections — computed from physics, checked against real community measurements.
Free · runs entirely in your browser · nothing to install
Popular models on popular hardware
Click a row to open it in the planner.Plan a deployment
Make three choices, then see the prediction and what limits it.
Performance, fit, and the reason why
ExecutionHow this runs on your hardware
Why: the model execution map
How the model maps onto your devices — what each one stores, where they communicate, and where the milliseconds go.
Physical topology Inspect links and utilization after understanding the logical deployment
Per-device utilization chart Memory, compute, local bandwidth, and network
Published benchmarks for reference click to expand
Real-world throughput plus official model-card task scores from vendor, community, and research sources. Throughput rows are used for configuration alignment; task-score rows are for comparison only.
| Model | Quantization / Mode | Runtime / Benchmark | Hardware / Task | Batch / Eval | Seq / Setting | Batch Rate / Score | Single Rate / Score | Source |
|---|
Find a model, then plan the hardware.
Compare base models, fine-tunes, quantizations, active parameters, and measured performance without digging through a giant select menu.
Compare the estimate with real machines.
The gold set keeps only community-measured runs that map cleanly to a known model, device, quantization, runtime, and command.
Measured versus predicted
Reproducible reference runs
| Model / setup | Observed | Ideal | Calibrated | Error |
|---|
Compare a measured run with the plan.
Paste a result or enter its speed to see implied efficiency, the ideal ceiling, and the changes most likely to help.
Your active plan supplies the model and hardware.
We will compare the entered result with both its physical ceiling and the peer-calibrated expectation.
View the normalized result JSON
One payload, with its assumptions made explicit.
Copy this normalized result into a report or a future API call.
{
"observedTokS": 32.2,
"configuration": "active-plan",
"return": ["idealTokS", "expectedTokS", "headroom", "recommendations"]
}