GPU × use-case guide · Text-to-speech

Is the T4 a good GPU for text-to-speech?

The T4 is a turing NVIDIA GPU with 16 GB GDDR6 (300 GB/s), 65 TFLOPS BF16 / 65 TFLOPS FP8, and a 70 W TDP. Text-to-speech workloads care most about low-latency streaming synthesis; small models, so value cards win. Here's how the T4 measures up.

What models fit on a single T4?

Weights only, reserving ~25% of the 16 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: none (needs multi-GPU) at BF16, Llama 3.1 8B at FP8, Qwen 2.5 14B at INT4. Bigger models need tensor-parallel across 8 cards.

T4 for text-to-speech, specifically

Text-to-speech is throughput-bound rather than VRAM-bound, so the T4's 300 GB/s of bandwidth and 65 TFLOPS matter more than raw capacity. Because it's latency-sensitive, run it at low batch sizes on a fast framework (vLLM ≈ 85% MFU) rather than maximising batch. Size it precisely on the calculator.

T4 pricing across providers

Verdict

At 16 GB, the T4 is a value card best suited to smaller text-to-speech models (up to Qwen 2.5 14B with INT4 quantisation). For larger models you'll want more VRAM or multi-GPU.