Whisper + ffmpeg GPU farm on unmetered bare-metal.
Speech-to-text and video transcode at scale on a dedicated RTX PRO GPU — run Whisper large-v3 for subtitles and transcripts, and NVENC ffmpeg for the encode ladder, on a flat monthly box instead of a per-minute API. Unmetered to move the media.
Why a GPU box beats the transcription API.
Cloud speech-to-text bills per minute of audio; a transcode API bills per output minute. A dedicated GPU is a flat monthly price — transcribe and encode a whole archive around the clock and the cost doesn't move.
The same GPU runs Whisper large-v3 for accurate transcripts and subtitles and NVENC ffmpeg for the H.264/H.265 encode ladder — one machine covers the whole media-processing pipeline.
Batch jobs pull large source files and push transcripts, subtitles and re-encoded video. The unmetered port means all that I/O costs nothing on top of the flat price.
Sensitive audio and video never leave a box you control. Single-tenant, in the jurisdiction you pick, with root access and no third-party retaining your content.
Sized for Whisper / ffmpeg farm.
Starter / Production / High-load — with capacity in the metrics that matter for this app.
- CPUEPYC + RTX PRO 4500
- RAM128 GB
- Storage4 TB NVMe
- Port10 Gbit/s
- Trafficunmetered
- CPUEPYC + RTX PRO 6000
- RAM256 GB
- Storage8 TB NVMe
- Port10 Gbit/s
- Trafficunmetered
- CPU2× RTX PRO 6000
- RAM512 GB
- Storage16 TB NVMe
- Port25 Gbit/s
- Trafficunmetered
On by default.
Whisper (or faster-whisper / whisper.cpp) and NVENC-enabled ffmpeg installed with CUDA and PyTorch, ready for batch jobs and a queue.
A whole RTX PRO 4500 or 6000 Blackwell — all the VRAM and NVENC engines are yours, no shared slice, no queue behind other tenants.
10 / 25 Gbit/s at flat pricing — pull big source files and push transcripts and re-encodes with no per-GB egress.
Multi-terabyte NVMe for source media and intermediate files, so batch jobs aren't disk-bound.
Attach a Storage Vault for the source library and finished outputs — scale the archive into hundreds of terabytes.
Full root to script pipelines, plus easy to front with a small REST/queue service for your app.
Whisper & GPU transcode hosting — speech-to-text and NVENC at scale
OpenAI's Whisper is the open speech-to-text model that produces accurate transcripts and subtitles across dozens of languages, and NVENC ffmpeg is the standard way to transcode video efficiently on a GPU. Run at any real volume — a media archive, a podcast network, a subtitling pipeline, a compliance workflow — and the cloud APIs that bill per minute get expensive fast. A dedicated GPU flips that to a flat, predictable cost.
Hostfory runs Whisper and NVENC ffmpeg on dedicated RTX PRO Blackwell GPUs. The same box transcribes with Whisper large-v3 and encodes your H.264/H.265 ladder, so one machine covers the whole media-processing pipeline, and because it's a flat monthly server you can run it flat-out around the clock without a per-minute meter. The port is unmetered, so pulling large source files and pushing transcripts, subtitles and re-encoded video costs nothing extra, and multi-terabyte NVMe keeps batch jobs off a slow disk.
Your audio and video never leave a machine you control — single-tenant, in the jurisdiction you choose, with full root access to script the pipeline and a Storage Vault for the source and output archive. Servers provision in a few hours with Whisper, CUDA, PyTorch and NVENC ffmpeg ready; pair it with the ComfyUI GPU box for a complete AI-media stack.
Pair it up.
Whisper / ffmpeg farm questions, answered.
How is this cheaper than a transcription API?
Which Whisper models can I run?
Can I transcode video on the same box?
Is the bandwidth metered?
Is my data private?
Can I wire it into my app?
Where do large archives live?
Pairs with the rest of the fleet.
Deploy Whisper / ffmpeg farm today.
In-stock configs provision in 1–3 hours. Migration from another host is on us.