Full Voice Pipeline on RTX 3090: Performance Benchmark & Cost, Category: Benchmarks, Slug: voice-pipeline-on-rtx-3090-benchmark, Excerpt: Full Voice Pipeline benchmarked on RTX 3090: Whisper Large-v3 + LLaMA 3 8B + Coqui XTTS-v2, concurrent performance, VRAM breakdown, and cost analysis., Internal links: 9 –>

A complete voice AI agent needs three models working in sequence: Whisper to hear, an LLM to think, and Coqui XTTS to speak. Can all three fit on a single RTX 3090 (24 GB VRAM)? We loaded Whisper Large-v3, LLaMA 3 8B, and Coqui XTTS-v2 onto one card on a GigaGPU dedicated server and ran the full pipeline end to end. The result: a working voice agent in 4.12 seconds per turn — tight on memory, but fast enough for conversational use.

Models tested: Whisper Large-v3 + LLaMA 3 8B + Coqui XTTS-v2

End-to-End Pipeline Latency

Sequential pipeline execution. Each stage completes before the next begins. All models pre-loaded in GPU memory.

VRAM: Packed to the Brim

Three models, 24 GB of weights, 24 GB of VRAM. It works because the pipeline is sequential — only one model actively computes at a time while the others sit idle in memory. But there is genuinely no room for error. If you need longer context windows or want to handle overlapping requests, INT4 quantisation on the LLM frees about 12 GB and makes the whole system much more manageable.

Three Models, One Invoice

Consolidating three models onto one GPU saves 67% versus running each on its own card. At £149/mo for a complete voice pipeline, the 3090 is the most affordable way to self-host a full voice agent server. Compare GPU options at our benchmark page.

Is 4.12 Seconds Fast Enough?

For a voice assistant, 4.12 seconds from end of speech to start of response is acceptable for many use cases — help desks, booking systems, information kiosks. Users are accustomed to brief pauses in phone-based interactions. Where it falls short is rapid back-and-forth dialogue or interruption handling. For tighter latency targets, the RTX 5080 (2.93s) or RTX 5090 (2.2s) bring the pipeline under the 3-second threshold that feels truly conversational. See our guides on Whisper hosting, Coqui TTS hosting, and speech model hosting.

Quick deploy:

docker compose up -d  # faster-whisper + llama.cpp + xtts containers with --gpus all

See our LLM hosting guide, Whisper hosting guide, and all benchmark results. Related benchmarks: LLaMA 3 8B on RTX 3090, Whisper Large-v3 on RTX 3090.