Voice AI applications need two things running at once: a speech-to-text model to understand what the user said, and an LLM to generate a useful response. We loaded both LLaMA 3 8B and Whisper Large-v3 simultaneously on a single NVIDIA RTX 3090 (24 GB VRAM) using a GigaGPU dedicated server to see whether one GPU can handle the full transcription-to-reasoning pipeline.
Models tested: LLaMA 3 8B + Whisper Large-v3
Concurrent Performance
| Component | Metric | Solo | Concurrent |
|---|---|---|---|
| LLaMA 3 8B (FP16) | Tokens/sec | 62 | 43.4 |
| Whisper Large-v3 | Real-time factor | 0.08 | 0.1 |
| Whisper Large-v3 | Processing speed | 12.5x | 10.0x |
All models loaded simultaneously in GPU memory. Throughput figures reflect concurrent operation with shared VRAM and compute.
VRAM Allocation
| Component | VRAM |
|---|---|
| Combined model weights | 21.1 GB |
| Total RTX 3090 VRAM | 24 GB |
| Free headroom | ~2.9 GB |
It fits, but just barely. The 3090’s 24 GB VRAM holds both models with about 2.9 GB to spare — enough for standard inference but tight if you need large KV caches or long audio clips. Keep an eye on memory utilisation under peak concurrent load, and consider INT4 quantisation for the LLM if you want more breathing room.
Savings from Co-hosting
| Cost Metric | Value |
|---|---|
| Server cost (single GPU) | £0.75/hr (£149/mo) |
| Equivalent separate GPUs | £1.50/hr |
| Savings vs separate servers | 50% |
Running both models on one card at £0.75/hr halves the cost compared to dedicating a GPU to each model. For teams building Whisper-based transcription that feeds into LLM-powered analysis — think meeting summarisation or call centre analytics — this single-GPU approach dramatically simplifies the architecture. Compare all options at our benchmark page.
The Bottom Line
The RTX 3090 is the entry point for single-GPU LLM + Whisper pipelines. At 10x real-time transcription and 43.4 tok/s generation running concurrently, it handles the core loop of most voice AI products: listen, transcribe, think, respond. The VRAM is tight but workable. If you want more headroom or faster generation, look at the RTX 5080 or 5090.
Quick deploy:
docker compose up -d # llama.cpp + faster-whisper containers with --gpus all
See our LLM hosting guide, Whisper hosting guide, best GPU for Whisper, and all benchmark results. Related benchmarks: LLaMA 3 8B on RTX 3090, Whisper Large-v3 on RTX 3090.
Deploy LLM + Whisper Pipeline on RTX 3090
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