# GPU Cloud for Whisper Transcription (2026): RunPod vs Thunder Compute and the Real Cost per Hour of Audio

Source: https://srvscripts.com/guides/gpu-cloud-whisper-transcription/
Updated: 2026-10-09
Publisher: srvScripts (https://srvscripts.com/)

**Short answer:** for self-hosted Whisper or faster-whisper, a 24–48 GB GPU rented from a GPU cloud by the second or minute is enough. [RunPod](/runpod/) has the cheapest small cards (RTX 4090 from $0.34 an hour on Community Cloud, $0.74 on Secure Cloud, billed per second), and [Thunder Compute](/thundercompute/) has a 48 GB RTX A6000 for $0.35 an hour, billed per minute. Using the faster-whisper project’s own benchmark, one hour of audio needs only a few minutes of GPU time, so the GPU cost is a few cents per hour of audio.

This page contains partner links. See our [affiliate disclosure](/affiliate-disclosure/).

Prices come from the providers’ own pricing pages and docs, checked on 7 October 2026. The cost per hour of audio is our estimate from the published benchmark, not a measurement on these exact GPUs.

## GPU prices that matter for Whisper

| Provider | Cheapest suitable GPUs (on demand) | Billing | Storage | Notes |
| --- | --- | --- | --- | --- |
| RunPod | RTX A5000 $0.27/h · L4 $0.49/h · RTX 3090 $0.50/h · RTX 4090 $0.74/h (Secure) or $0.34/h (Community) | Per second; needs at least 1 hour of credit to deploy | Volume $0.10/GB/month running, $0.20 stopped; network volume $0.07 | Serverless workers too (24 GB tier $0.69/h, billed per second) |
| Thunder Compute | RTX A6000 48 GB $0.35/h · L40 $0.79/h · A100 80 GB $1.09/h | Per minute while running | First 100 GB per GPU included; snapshots $0.05/GB/month | No egress fees; CUDA 13 and PyTorch preinstalled; North America |
| Lambda | A100 40 GB $1.99/h | Not stated | Per GB used | Data-centre GPUs only; no egress fees |
| Google Cloud | L4 VM (g2-standard-4) $0.71/h on demand, $0.40 spot | Per second | Separate | A useful reference price |

Prices last checked: 7 October 2026. RunPod’s Secure and Community prices differ; check which one the console shows before you start.

## How much GPU time does an hour of audio take?

The faster-whisper README benchmarks the same 13-minute audio file with the `large-v2` model on an NVIDIA RTX 3070 Ti 8 GB (CUDA 12.4, beam size 5):

| Implementation | Precision | Time for 13 min of audio | Speed | VRAM |
| --- | --- | --- | --- | --- |
| openai/whisper | fp16 | 2 min 23 s | about 5.5× real time | 4.7 GB |
| faster-whisper | fp16 | 1 min 3 s | about 12× real time | 4.5 GB |
| faster-whisper, batch size 8 | fp16 | 17 s | about 46× real time | 6.1 GB |
| faster-whisper, batch size 8 | int8 | 16 s | about 49× real time | 4.5 GB |

At about 12× real time, one hour of audio needs about 5 minutes of GPU; batched, about 1.3 minutes. On a RunPod RTX 4090 at $0.74 an hour that is roughly **$0.06 per hour of audio unbatched and under $0.02 batched**, assuming a 4090 is at least as fast as the benchmark’s 3070 Ti and ignoring model loading. Benchmark your own recordings: phone audio, language and model size all change the numbers.

## RunPod: cheapest per-second GPUs

- Per-second billing for pods and serverless workers, no data-transfer fees.

- A 24 GB card (A5000, 3090, L4) runs `large-v3` in fp16 or int8 comfortably.

- Serverless suits an API with idle periods: you pay from worker start to stop, including cold start.

- **Catches:** stopped pods still pay $0.20 per GB per month for volume disk; when your balance hits $0, pods stop and pods without a network volume are deleted.

[Open RunPod](/runpod/) Partner link

## Thunder Compute: cheapest 48 GB card

- RTX A6000 48 GB at $0.35 an hour with 6 vCPU and 48–64 GB RAM included; per-minute billing only while the instance runs.

- First 100 GB of disk per GPU included, no egress fees, spending alerts and reminders for running instances.

- Comes with CUDA 13 and PyTorch preinstalled; the docs warn not to reinstall CUDA. faster-whisper uses CTranslate2 CUDA kernels, so run a short test first.

- No T4, L4 or RTX 4090 options; North America only.

[Open Thunder Compute](/thundercompute/) Partner link

## Running it

- Convert recordings to 16 kHz mono first; it cuts upload and decode time ([convert Asterisk recordings](/guides/convert-asterisk-recordings-to-mp3/)).

- Install faster-whisper in a virtual environment and cache the model on a network volume so restarts do not download it again.

- Use batched inference and int8 for long queues; keep beam size 5 for accuracy.

- Delete or stop instances when the queue is empty; set a spending alert.

Our step-by-step setup is in [transcribe Asterisk recordings with faster-whisper](/guides/transcribe-asterisk-recordings-faster-whisper/). For a handful of files, the free [call recording transcription tool](/tools/call-recording-transcription/) may be all you need.

**Related:** [Transcribe Asterisk recordings with faster-whisper](/guides/transcribe-asterisk-recordings-faster-whisper/) · [Asterisk call recording with MixMonitor](/guides/asterisk-call-recording-mixmonitor/) · [Call recording transcription tool](/tools/call-recording-transcription/)

## GPU Cloud at a glance

In short: RunPod vs Thunder Compute (plus Lambda and Google Cloud) for self-hosted Whisper and faster-whisper: GPU prices, billing, storage, and the cost per hour of audio from the published benchmark.

GPU Cloud: what this guide covers.

GPU Cloud: the questions this guide answers.

## Frequently asked questions

### Which GPU do I need for Whisper large-v3?

faster-whisper runs large models in about 3–6 GB of VRAM in the project’s benchmark, so any 24 GB card (RTX A5000, 3090, 4090, L4) is plenty, with room for batching.

### How much does it cost to transcribe an hour of audio on a cloud GPU?

Based on the faster-whisper benchmark (about 12× real time unbatched, about 46× batched) and a $0.74-an-hour RTX 4090, roughly $0.02–0.06 of GPU time per hour of audio, before storage and startup time.

### Is a CPU enough?

For small volumes, yes. The README shows the smaller “small” model running about 5–15× real time on an 8-thread Intel i7-12700K. For large models and big backlogs, a GPU is far faster and usually cheaper per hour of audio.

### RunPod or Thunder Compute?

RunPod for the cheapest small cards, per-second billing and serverless endpoints. Thunder Compute for a 48 GB card at $0.35 an hour with disk included and no egress fees.

### Are call recordings safe on a cloud GPU?

Treat them as personal data: use a provider and region that fit your privacy obligations, encrypt storage, delete audio after processing, and tell callers they are recorded where the law requires it.