LLM + Whisper Pipeline on RTX 5080: Performance Benchmark & Cost, Category: Benchmarks, Slug: llm-whisper-pipeline-on-rtx-5080-benchmark, Excerpt: LLM + Whisper Pipeline benchmarked on RTX 5080: LLaMA 3 8B + Whisper Large-v3, concurrent performance, VRAM breakdown, and cost analysis., Internal links: 9 –>

Transcribe audio and run LLM inference at the same time, on one GPU, without either model stepping on the other. That is the promise of co-hosting, and the RTX 5080 (16 GB VRAM) delivers on it surprisingly well. We tested LLaMA 3 8B (INT4) alongside Whisper Large-v3 on a GigaGPU dedicated server, and the Blackwell architecture’s improved memory bandwidth keeps both models humming even when sharing the bus.

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

Side-by-Side Performance

All models loaded simultaneously in GPU memory. Throughput figures reflect concurrent operation with shared VRAM and compute.

How the Memory Splits

The INT4 quantisation of LLaMA 3 is the key enabler here. It shrinks the LLM footprint enough that both models fit within 16 GB with 6.4 GB to spare. That headroom is generous — enough for extended KV caches, longer audio buffers, or even a lightweight classification model if your pipeline needs one.

Cost Advantage

At £189/mo you get a machine that handles both speech transcription and LLM generation with no inter-service latency. The 5080 actually outperforms the 3090 on concurrent throughput (57.4 vs 43.4 tok/s for the LLM) thanks to faster Blackwell cores and the efficiency gains of INT4 quantisation. Compare everything at our benchmark page.

Why This Pairing Works

The LLM + Whisper combination is the backbone of products like meeting assistants, customer support bots with voice input, and podcast summarisation tools. The 5080 handles the concurrent load smoothly at 16.1x real-time Whisper and 57.4 tok/s LLM generation. If your use case involves streaming audio that needs near-instant transcription followed by LLM analysis, this is one of the most cost-effective single-GPU setups available. For FP16 LLM precision or heavier concurrent loads, step up to the RTX 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 5080, Whisper Large-v3 on RTX 5080.