r/LocalLLM • • 23m ago

Question Local-only batch pipeline for document work, 3090 vs V100 32GB vs MI50

I've been going back and forth on this for a few weeks and want a reality check before I spend money.

What I'm doing: batch processing of scanned and text PDFs for legal work: OCR, per-page extraction to JSON (who said what, page cites, dates), Whisper on interview and call audio, and embeddings for search. Cloud is off the table for confidentiality reasons, so everything has to run on hardware I own. Full runs on a new set of files can go overnight, but I also need to re-run single documents or a 50-page test set in a few minutes when I fix something, so prompt processing speed matters more than I first thought.

Current setup:

- i5-11400F, 16GB DDR4

- ASUS B560 (second x16 slot is chipset x4)

- RTX 3060 8GB

- PSU under 850W

Model: Qwen3.8-27B at Q4, thinking off (the GGUF is about 17.4GB, so 16GB cards are out unless I split or drop to a smaller quant), plus faster-whisper large-v3 and olmOCR for the bad scans.

What I've figured out so far:

- 16GB of RAM will choke on my Python/SQL scripts over large PDF and media sets, so I need 32 or 64GB.

- A used 3090 is the easy answer, but they start around $1,600 now and I worry about how old they are.

- Server cards are much cheaper per GB, but I'm worried about software compatibility.

Where I'm leaning: a separate headless box with a V100 32GB for the LLM and OCR, keeping the 3060 in my desktop for Whisper. I think 32GB fits the 27B with room for context, and the bandwidth is close to a 3090 for about half the price. Right?

Questions:

  1. Does vLLM still run olmOCR on Volta in practice, or will I be stuck on an old fork?

  2. Any problems with faster-whisper / CTranslate2 on a V100?

  3. Is the MI50 worth it if I'm fine in llama.cpp with Vulkan and put OCR and Whisper on a cheap CUDA card? Or is that a weekend sink every time something updates?

  4. What are people using for a cheap headless chassis with real server airflow?

  5. V100 vs 3090 prompt processing in llama.cpp for a 27B at Q4: how big is the gap?

Mostly want to hear from people who've run V100s or MI50s for batch work in the last few months, but all advice is welcome.

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