Yes, the RTX 5080 can run Whisper and an LLM together. With 16GB GDDR7 VRAM, the RTX 5080 has enough capacity to keep Whisper loaded alongside a quantised 7B language model. This makes it a solid single-GPU solution for voice-to-text-to-response pipelines common in AI assistants and call-centre automation.
The Short Answer
YES. Whisper Large-v3 (~3GB) plus a 7B LLM in INT4 (~5GB) totals ~8GB, well within 16GB.
The typical voice AI pipeline loads Whisper for speech-to-text and an LLM for generating responses from the transcript. Whisper Large-v3 uses approximately 3GB of VRAM. A 7B LLM such as Mistral 7B in INT4 requires about 5GB. Combined, that is roughly 8GB, leaving 8GB of free VRAM for KV cache, batch processing, and OS overhead. Check our Whisper VRAM requirements guide for detailed memory breakdowns by model size.
VRAM Analysis
| Combined Configuration | Whisper VRAM | LLM VRAM | Total | RTX 5080 (16GB) |
|---|---|---|---|---|
| Whisper Large-v3 + Mistral 7B INT4 | ~3GB | ~5GB | ~8GB | Fits easily |
| Whisper Large-v3 + LLaMA 3 8B INT4 | ~3GB | ~5.5GB | ~8.5GB | Fits easily |
| Whisper Large-v3 + DeepSeek 7B FP16 | ~3GB | ~14GB | ~17GB | No |
| Whisper Large-v3 + Mistral 7B INT8 | ~3GB | ~7.5GB | ~10.5GB | Fits |
| Whisper Medium + Mistral 7B INT4 | ~1.5GB | ~5GB | ~6.5GB | Fits easily |
The INT4 quantised LLM option is the most practical. You can even fit Whisper Large-v3 alongside a 7B LLM in INT8, which retains better quality than INT4, with about 5GB to spare for KV cache and concurrent requests.
Performance Benchmarks
| Workload | RTX 5080 (Solo) | RTX 5080 (Combined) | Impact |
|---|---|---|---|
| Whisper Large-v3 (RTF) | 0.04x | 0.05x | ~25% slower |
| Mistral 7B INT4 (tok/s) | ~90 | ~82 | ~9% slower |
| LLaMA 3 8B INT4 (tok/s) | ~85 | ~77 | ~10% slower |
Running both models simultaneously incurs a modest performance penalty of roughly 10-25%. Whisper takes the bigger hit because its encoder runs in brief intensive bursts that compete for memory bandwidth. However, in a typical pipeline where Whisper finishes transcription before the LLM generates a response, there is minimal overlap and performance remains close to solo figures. Compare throughput across all GPUs on our benchmarks page.
Setup Guide
Run Whisper via faster-whisper and the LLM via Ollama as separate services:
# Terminal 1: Whisper API server
pip install faster-whisper
python -c "
from faster_whisper import WhisperModel
model = WhisperModel('large-v3', device='cuda', compute_type='float16')
# Wrap in your preferred API framework (FastAPI, Flask)
"
# Terminal 2: LLM via Ollama
ollama run mistral:7b-instruct-q4_K_MFor a unified pipeline, use a framework that chains Whisper output directly into the LLM. Both models stay resident in VRAM, so there is no loading delay between transcription and response generation.
Recommended Alternative
If you need the LLM in FP16 or want to add a third model (such as a TTS engine), the RTX 3090 with 24GB provides more headroom. For an even more capable multi-model setup, see whether the RTX 5090 can run DeepSeek + Whisper.
For dedicated Whisper benchmarks, see our Whisper model size comparison. For other RTX 5080 workloads, check the DeepSeek on 5080 or Flux.1 on 5080 guides. Browse all options on our dedicated GPU hosting page or in the GPU Comparisons category.
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