What GPU do I need to run dphn/Dolphin-Mistral-24B-Venice-Edition?
24.0B parameters, published in BF16. View on Hugging Face
Set up Dolphin-Mis…B-Venice-EditionDolphin-Mistral-24B-Venice-Edition is published by dphn on Hugging Face, with 547,462 downloads and 665 likes to date. It's a Mistral3ForConditionalGeneration model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Dolphin-Mistral-24B-Venice-Edition at its published (BF16) precision: 1× A100 on runpod, at $1.19/hr per GPU ($1.19/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Dolphin-Mistral-24B-Venice-Edition: common questions
Can Dolphin-Mistral-24B-Venice-Edition run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 53.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 on runpod at $1.19/hr.
What is the least VRAM Dolphin-Mistral-24B-Venice-Edition can run in?
13.4 GB, at INT4 (quantized), which fits a 16 GB card, against 53.7 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing Dolphin-Mistral-24B-Venice-Edition lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 on runpod at $1.19/hr. At INT4 (quantized) it drops to one RTX 3090 on akash at $0.147/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More dphn models
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- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
