Table of Contents
Whisper Large-v3 Benchmark Overview
OpenAI Whisper Large-v3 is the most accurate open speech-to-text model, with 1.55 billion parameters supporting 100+ languages. The key metric for transcription is Real-Time Factor (RTF) — a value below 1.0 means the model transcribes faster than real time. Deploying Whisper Large-v3 on a dedicated GPU server ensures consistent low-latency transcription for production workloads.
Tests used faster-whisper (CTranslate2) on GigaGPU servers with a 10-minute English audio sample at 16kHz. Whisper Large-v3 requires approximately 3 GB of VRAM at FP16. For comparisons with smaller models, see our Whisper Tiny vs Base vs Small benchmark.
RTF Results by GPU
Lower RTF is better. An RTF of 0.10 means 10 minutes of audio is transcribed in 1 minute.
| GPU | VRAM | Whisper Large-v3 FP16 RTF | Speed vs Real-Time |
|---|---|---|---|
| RTX 3050 | 6 GB | 0.32 | 3.1x real-time |
| RTX 4060 | 8 GB | 0.18 | 5.6x real-time |
| RTX 4060 Ti | 16 GB | 0.13 | 7.7x real-time |
| RTX 3090 | 24 GB | 0.09 | 11.1x real-time |
| RTX 5080 | 16 GB | 0.06 | 16.7x real-time |
| RTX 5090 | 32 GB | 0.04 | 25x real-time |
Every GPU tested runs Whisper Large-v3 faster than real-time. The RTX 5090 achieves 25x real-time speed, meaning a 1-hour podcast is transcribed in under 2.5 minutes.
FP16 vs INT8 Comparison
CTranslate2 supports INT8 quantisation for additional speed. Below we compare FP16 and INT8 RTF across all GPUs. For more quantisation analysis, see our quantisation speed comparison.
| GPU | FP16 RTF | INT8 RTF | Improvement |
|---|---|---|---|
| RTX 3050 | 0.32 | 0.22 | 31% |
| RTX 4060 | 0.18 | 0.12 | 33% |
| RTX 4060 Ti | 0.13 | 0.09 | 31% |
| RTX 3090 | 0.09 | 0.06 | 33% |
| RTX 5080 | 0.06 | 0.04 | 33% |
| RTX 5090 | 0.04 | 0.028 | 30% |
INT8 delivers a consistent 30-33% improvement in RTF with negligible impact on transcription accuracy. For production deployments, INT8 is strongly recommended.
Cost Efficiency Analysis
We measure cost efficiency as transcription speed (inverse of RTF) per pound of monthly hosting cost.
| GPU | FP16 RTF | Approx. Monthly Cost | Speed/Pound |
|---|---|---|---|
| RTX 3050 | 0.32 | ~£45 | 0.069 |
| RTX 4060 | 0.18 | ~£60 | 0.093 |
| RTX 4060 Ti | 0.13 | ~£75 | 0.103 |
| RTX 3090 | 0.09 | ~£110 | 0.101 |
| RTX 5080 | 0.06 | ~£160 | 0.104 |
| RTX 5090 | 0.04 | ~£250 | 0.100 |
The RTX 5080 and RTX 4060 Ti offer the best value, with the RTX 3090 close behind. For the best GPU for Whisper, the 4060 Ti is an excellent budget pick.
GPU Recommendations
- Budget: RTX 4060 Ti — 7.7x real-time at FP16, 11x at INT8. Excellent for moderate transcription volumes.
- Best value: RTX 5080 — 16.7x real-time makes it ideal for high-volume transcription services.
- Fastest: RTX 5090 — 25x real-time for mission-critical, low-latency pipelines.
- Entry level: RTX 3050 — still 3x real-time, suitable for light-use self-hosted transcription.
For the smaller model variant, check the Whisper Medium RTF benchmark. You can also compare model sizes in our Whisper Tiny vs Base vs Small comparison. Browse all results in the Benchmarks category.
Conclusion
Whisper Large-v3 runs faster than real-time on every GPU we tested, and INT8 quantisation further boosts speed with no meaningful accuracy loss. Whether you are building a transcription API, a meeting notes service, or a podcast indexer, a dedicated GPU server with the right hardware delivers consistent, reliable performance.
Deploy Whisper Large-v3 on Dedicated Servers
Fast, reliable transcription on bare-metal GPU hardware. Choose from budget to high-end configurations.
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