What GPU do I need to run parler-tts/parler-tts-large-v1?
2.3B parameters, published in F32. View on Hugging Face
parler-tts-large-v1 is published by parler-tts on Hugging Face, with 12,656 downloads and 274 likes to date. It's a ParlerTTSForConditionalGeneration model built for text-to-speech, published natively in F32.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run parler-tts-large-v1 at its published (F32) precision: 1× RTX 3060 on simplepod, at $0.080/hr per GPU ($0.080/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More parler-tts models
- parler-tts-mini-multilingual-v1.1 (938M, F32)
- parler-tts-mini-v1 (878M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
