FinOps-Estimator: sizing GPUs for a voice AI pipeline
An open-source tool that turns concurrency and model choices for STT → LLM → TTS into VRAM, RAM, CPU, TFLOPS and cloud or on-prem cost.
Sattaya Singkul · 2 min read
Voice AI projects tend to hit the same question early: how much hardware do we need, and what will it cost? A speech-to-text model, an LLM and a text-to-speech model all compete for the same GPUs, and the answer changes with every concurrency target. I built FinOps-Estimator to answer that question with numbers instead of guesses. It is open source, and there is a live demo.
What it estimates
Most calculators stop at GPU memory. A production voice pipeline also needs host memory and CPU, so the tool tracks all three:
- GPU VRAM — model weights, the KV cache (following vLLM PagedAttention) and per-component overhead.
- System RAM — host memory for model runtimes and audio buffering.
- CPU cores — a vCPU recommendation that depends on the architecture.
It also estimates the compute you need:
TFLOPS = 2 × Params × Total_ThroughputIt then compares that figure with reference GPUs such as the NVIDIA H100, A100 and L4, so you don’t buy more than you need.
Monolithic or distributed
The same models can be deployed in two ways, and the cost profile is very different:
| Pattern | What it means | When it fits |
|---|---|---|
| Monolithic (all-in-one) | STT, LLM and TTS in a single pod | Small scale, simplest operations |
| Distributed | Separate pods for STT, LLM and TTS | Components scale independently |
The estimator simulates both, so you can see where the crossover happens for your own concurrency.
Cloud or on-premise
The final step is a hardware matchmaker. It finds cost-effective GPU instances on GCP, AWS and Azure, including spot and preemptible pricing. It also calculates on-premise ROI, comparing CapEx with OpEx to show when owning the cluster beats renting it. That trade-off matters most for on-premise deployments, where data cannot leave the building.
From the results you can export Terraform and Kubernetes HPA configurations as a starting point for provisioning.
How the numbers are grounded
The formulas come from vLLM PagedAttention, TensorRT-LLM and Pipecat/LiveKit benchmark data. Hardware prices and model presets were last synchronised with cloud instance data in March 2026.
What’s next
The roadmap has two open items:
- p95 latency simulation
- Carbon-footprint estimation per region
Contributions of new models and pricing are welcome — see CONTRIBUTOR.md in the repo.
Source
- FinOps-Estimator on GitHub: README, formulas, roadmap and contributor guide. Every feature described in this post comes from the project’s README.