AI Inference Speed Calculator
Calculate LLM inference throughput, latency and token generation speed to evaluate deployment performance and model selection
Calculator Interface
Interactive calculator will be available soon
Features
- ✓ Computes core inference metrics: tokens/second, request latency and concurrent throughput
- ✓ Built-in profiles for mainstream GPUs (A100, H100, L4 etc.) and inference frameworks (vLLM, TensorRT-LLM etc.)
- ✓ Advanced parameters: input/output token ratio, batch size and quantization level
- ✓ Real-time performance comparison across models and hardware
- ✓ Auto-estimates inference serving cost and cost-performance, with exportable reports
How to Use
- Select AI model and inference framework (multi-select for comparison)
- Choose GPU model and quantization level (FP16/INT8/INT4)
- Enter concurrency, batch size and input/output token ratio
- Click calculate to compare throughput, latency and cost
FAQ
What is AI Inference Speed Calculator?
An online AI inference performance calculator. It calculates LLM inference throughput, latency and token generation speed to evaluate deployment performance and model selection. Input: model, GPU, concurrency and token parameters. Output: throughput, latency and cost estimates with one-click copy and download. Everything runs in your browser — no uploads.
What does the calculation rely on?
It relies on official benchmark data from inference frameworks and real-world measurements on mainstream GPUs, covering vLLM, TensorRT-LLM, llama.cpp and more.
Which GPUs are supported?
NVIDIA A100, H100, H200, L4, L40S, RTX 4090, A10G and AMD MI300X, among other mainstream training and inference GPUs.
Does quantization level affect speed much?
Significantly. INT8 is typically 1.3-1.5x faster than FP16, and INT4 can add another 20-40%, at a slight accuracy cost. The calculator includes empirical speedup ratios per level.
How accurate are the results?
Results are based on official benchmarks and real measurements, usually within ±15%. Actual performance varies with context length, KV cache policy and batch scheduling, so use it for comparison and capacity planning.
Can I save the calculation results?
Yes. Export performance reports as CSV or PDF for hardware selection and cost reviews.