What GPU do I need to run ornith-ai/Ornith-1.5-397B-FP8?
403.4B parameters, published in F8_E4M3. View on Hugging Face
Ornith-1.5-397B-FP8 is published by ornith-ai on Hugging Face, with 155,589 downloads and 7 likes to date. It's a Qwen3_5MoeForConditionalGeneration model built for text-generation, published natively in F8_E4M3.
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 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Ornith-1.5-397B-FP8 at its published (F8_E4M3) precision: 5× RTX PRO 6000 on runpod, at $1.69/hr per GPU ($8.45/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More ornith-ai models
- Ornith-1.0-35B-FP8 (35.1B, F8_E4M3)
- Ornith-1.0-397B-FP8 (396.8B, F8_E4M3)
- Ornith-1.5-397B (403.4B, BF16)
- Ornith-1.0-397B (396.8B, BF16)
- Ornith-1.5-9B (9.7B, BF16)
- Ornith-1.5-35B-A3B (36.0B, BF16)
