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Bark TTS Benchmark Overview
Bark by Suno is an open text-to-speech model capable of generating highly natural speech with emotion, laughter, and non-verbal sounds. Unlike simpler TTS models, Bark uses a transformer architecture that is more compute-intensive but produces remarkably expressive audio. A dedicated GPU server is recommended for consistent low-latency speech generation.
All tests were conducted on GigaGPU servers measuring end-to-end latency (prompt to audio output) for a standard 15-word English sentence. Bark requires approximately 5 GB of VRAM. For comparisons with other TTS models, see our TTS latency benchmarks hub.
Latency Results by GPU
Lower latency is better. We measure milliseconds from text input to complete audio output.
| GPU | VRAM | Bark Latency (ms) | Notes |
|---|---|---|---|
| RTX 3050 | 6 GB | 4,800ms | Fits in VRAM but slow |
| RTX 4060 | 8 GB | 2,900ms | Noticeable delay |
| RTX 4060 Ti | 16 GB | 2,100ms | Approaching usable latency |
| RTX 3090 | 24 GB | 1,500ms | Good for non-real-time use |
| RTX 5080 | 16 GB | 950ms | Sub-second generation |
| RTX 5090 | 32 GB | 620ms | Best latency tested |
Bark is inherently slower than lightweight TTS models due to its autoregressive transformer architecture. The RTX 5090 at 620ms is the only GPU that achieves sub-second latency for a standard sentence, while the RTX 5080 comes close at 950ms.
Sentence Length Impact
Bark’s latency scales with output length. Below we compare short (8 words), medium (15 words), and long (30 words) sentences.
| Sentence Length | RTX 3090 (ms) | RTX 5090 (ms) |
|---|---|---|
| Short (8 words) | 850 | 350 |
| Medium (15 words) | 1,500 | 620 |
| Long (30 words) | 2,800 | 1,150 |
Latency roughly doubles as sentence length doubles. For real-time applications, consider chunking long text into shorter segments and streaming audio output.
Cost Efficiency Analysis
We measure cost efficiency as inverse latency (generations per second) per pound of monthly hosting cost.
| GPU | Latency (ms) | Approx. Monthly Cost | Gen/s per Pound |
|---|---|---|---|
| RTX 3050 | 4,800 | ~£45 | 0.0046 |
| RTX 4060 | 2,900 | ~£60 | 0.0057 |
| RTX 4060 Ti | 2,100 | ~£75 | 0.0063 |
| RTX 3090 | 1,500 | ~£110 | 0.0061 |
| RTX 5080 | 950 | ~£160 | 0.0066 |
| RTX 5090 | 620 | ~£250 | 0.0065 |
The RTX 5080 and RTX 5090 are nearly tied on cost efficiency, with the RTX 4060 Ti close behind. For the best GPU for TTS, the RTX 5080 offers the optimal balance.
GPU Recommendations
- Budget: RTX 4060 Ti — 2.1 seconds per sentence is acceptable for non-real-time applications like audiobook generation.
- Best value: RTX 5080 — sub-second latency at the best cost efficiency.
- Lowest latency: RTX 5090 — 620ms enables near-interactive voice applications.
- Alternative: For faster TTS, consider Kokoro TTS which trades expressiveness for speed.
Compare Bark with other TTS models in our XTTS-v2 latency benchmark or the Kokoro TTS results. Browse all benchmarks in the Benchmarks category.
Conclusion
Bark produces the most expressive open-source TTS audio available, but its transformer architecture means higher latency than lightweight models. For applications where voice quality and expressiveness matter more than raw speed, Bark on a dedicated GPU server with an RTX 5080 or RTX 5090 is the recommended setup.
Deploy Bark TTS on Dedicated Hardware
GPU servers optimised for text-to-speech workloads with low latency and full root access.
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