Two-point-two seconds from spoken question to spoken answer. That is the total round-trip latency of a full voice pipeline — Whisper transcription, LLM reasoning, Coqui speech synthesis — running on a single RTX 5090 (32 GB VRAM) inside a GigaGPU dedicated server. This is the fastest complete voice agent we have benchmarked on any consumer GPU, and the 32 GB VRAM means all three models run at full precision without quantisation compromises.
Models tested: Whisper Large-v3 + LLaMA 3 8B + Coqui XTTS-v2
Pipeline Timing Breakdown
| Pipeline Stage | Model | Input | Time |
|---|---|---|---|
| 1. Transcription | Whisper Large-v3 | 10s audio | 0.3s |
| 2. LLM Processing | LLaMA 3 8B (FP16) | ~50 tokens in | 1.5s |
| 3. Speech Synthesis | Coqui XTTS-v2 | ~150 tokens | 0.4s |
| Total pipeline latency | 2.2s | ||
Sequential pipeline execution. Each stage completes before the next begins. All models pre-loaded in GPU memory.
Memory Comfort
| Component | VRAM |
|---|---|
| Combined model weights | 24.0 GB |
| Total RTX 5090 VRAM | 32 GB |
| Free headroom | ~8.0 GB |
All three models at full FP16 precision with 8 GB to spare. That headroom is not just reassuring — it is functional. You can extend the LLM’s context window for multi-turn conversations, handle longer audio inputs in Whisper, or even add a fourth model (an embedding model for context retrieval, for example) to enrich the voice agent’s capabilities. The 3090 runs this same pipeline at zero headroom; the 5090 makes it comfortable.
Infrastructure Savings
| Cost Metric | Value |
|---|---|
| Server cost (single GPU) | £1.50/hr (£299/mo) |
| Equivalent separate GPUs | £4.50/hr |
| Savings vs separate servers | 67% |
Running three GPU models on separate cards would cost £4.50/hr. The 5090 consolidates them for £1.50/hr — a 67% saving. Beyond raw cost, the single-GPU architecture eliminates network latency between services. Each pipeline stage feeds directly into the next through shared GPU memory, which is partly why the 5090 is almost twice as fast end-to-end as the 3090. Full comparison at our benchmark page.
The Gold Standard for Self-Hosted Voice AI
At 2.2 seconds, the 5090 voice pipeline is fast enough for genuinely interactive conversation. Callers will not notice they are talking to a machine based on response time alone. This makes the card ideal for production voice agent servers: customer service bots that need to sound natural, phone-based appointment booking systems, or interactive training simulations. The 8 GB of VRAM headroom also means you can upgrade to larger LLMs as they become available without changing hardware. For teams serious about self-hosted speech model infrastructure, this is the benchmark to beat.
Quick deploy:
docker compose up -d # faster-whisper + llama.cpp + xtts containers with --gpus allSee our LLM hosting guide, Whisper hosting guide, Coqui TTS hosting, and all benchmark results. Related benchmarks: LLaMA 3 8B on RTX 5090, Whisper Large-v3 on RTX 5090.
Deploy Full Voice Pipeline on RTX 5090
Order this exact configuration. UK datacenter, full root access.
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