Yes, the RTX 5090 handles DeepSeek and Whisper together with ease. With 32GB GDDR7 VRAM, the RTX 5090 can run Whisper Large-v3 alongside even the 14B DeepSeek distill in FP16 with VRAM to spare. This is ideal for building end-to-end voice AI assistants on a single GPU.
The Short Answer
YES. Whisper Large-v3 (~3GB) + DeepSeek 14B FP16 (~28GB) = ~31GB. Fits within 32GB.
Whisper Large-v3 is relatively lightweight at roughly 3GB of VRAM. This leaves 29GB on the RTX 5090 for the LLM component. The DeepSeek R1 7B distill in FP16 uses about 14GB, leaving 15GB free. Even the 14B distill in FP16 at ~28GB combined with Whisper fits within 32GB, though tightly. For VRAM details on each model individually, see our DeepSeek VRAM requirements and Whisper VRAM requirements guides.
VRAM Analysis
| Combined Configuration | Whisper VRAM | DeepSeek VRAM | Total | RTX 5090 (32GB) |
|---|---|---|---|---|
| Whisper Large-v3 + DeepSeek 7B INT4 | ~3GB | ~5GB | ~8GB | Fits easily |
| Whisper Large-v3 + DeepSeek 7B FP16 | ~3GB | ~14GB | ~17GB | Fits well |
| Whisper Large-v3 + DeepSeek 14B INT4 | ~3GB | ~8.5GB | ~11.5GB | Fits easily |
| Whisper Large-v3 + DeepSeek 14B FP16 | ~3GB | ~28GB | ~31GB | Tight fit |
| Whisper Large-v3 + DeepSeek 32B INT4 | ~3GB | ~20GB | ~23GB | Fits well |
The RTX 5090 opens up configurations that are impossible on smaller cards. Running DeepSeek 14B in full FP16 alongside Whisper gives you the best reasoning quality without quantisation compromises. For the 7B distill, you have enormous headroom to add a third model, such as a TTS engine, for a complete voice-in voice-out pipeline.
Performance Benchmarks
| Workload | RTX 5090 (Solo) | RTX 5090 (Combined) | Impact |
|---|---|---|---|
| Whisper Large-v3 (RTF) | 0.025x | 0.03x | ~20% slower |
| DeepSeek 7B FP16 (tok/s) | ~98 | ~92 | ~6% slower |
| DeepSeek 14B FP16 (tok/s) | ~52 | ~46 | ~12% slower |
| DeepSeek 14B INT4 (tok/s) | ~68 | ~63 | ~7% slower |
Performance impact is modest because Whisper and the LLM rarely run simultaneously in a pipeline. Whisper transcribes first, then the LLM generates a response. With both loaded, switching between them is instant with no model loading delay. Concurrent execution incurs a 6-20% penalty depending on the configuration. More comparisons are available on our benchmarks page.
Setup Guide
Run faster-whisper and Ollama as separate services:
# Terminal 1: Whisper API via faster-whisper
pip install faster-whisper
faster-whisper-server --model large-v3 \
--device cuda --compute_type float16 \
--host 0.0.0.0 --port 8080
# Terminal 2: DeepSeek via Ollama
ollama run deepseek-r1:14bFor a production pipeline, chain the services with a lightweight orchestrator:
# vLLM for DeepSeek with controlled VRAM allocation
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-14B \
--max-model-len 4096 \
--gpu-memory-utilization 0.80 \
--host 0.0.0.0 --port 8000Setting --gpu-memory-utilization 0.80 reserves roughly 6GB for Whisper and system overhead.
Recommended Alternative
For a more budget-friendly voice AI pipeline, the RTX 5080 runs Whisper + 7B LLM with 16GB. You lose the ability to run larger DeepSeek variants in FP16, but it is significantly cheaper.
For other RTX 5090 workloads, see the multi-LLM guide, LLaMA 3 70B INT4 analysis, or Flux.1 FP16 guide. Compare all GPU tiers in our cheapest GPU for inference guide and browse servers on our dedicated GPU hosting page.
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