What GPU do I need to run ornith-ai/Ornith-1.0-397B-FP8?
396.8B parameters, published in F8_E4M3. View on Hugging Face
Set up Ornith-1.0-397B-FP8Ornith-1.0-397B-FP8 is published by ornith-ai on Hugging Face, with 422,068 downloads and 186 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 369.6 GB | 443.5 GB | RTX PRO 6000 | 5 | $7.66/hr |
| INT4 (quantized) | 184.8 GB | 221.7 GB | RTX A6000 | 5 | $1.81/hr |
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 Ornith-1.0-397B-FP8 at its published (F8_E4M3) precision: 5× RTX PRO 6000, at $1.53/hr per GPU ($7.66/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Ornith-1.0-397B-FP8: common questions
Can Ornith-1.0-397B-FP8 run on a single GPU?
No. At FP8 (native) it needs 443.5 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 96.0 GB RTX PRO 6000, and it takes 5 of them.
Is Ornith-1.0-397B-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 443.5 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 221.7 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
How many GPUs do I need to run Ornith-1.0-397B-FP8?
5 at FP8 (native). It needs 443.5 GB of VRAM and the cheapest capable live offer is a 96.0 GB RTX PRO 6000, so 5 of them come to $7.66/hr in total.
Does quantizing Ornith-1.0-397B-FP8 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is 5 RTX PRO 6000 cards at $7.66/hr. At INT4 (quantized) it drops to 5 RTX A6000 cards at $1.81/hr, provided a quantized checkpoint exists for it.
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-1.5-397B (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)
- Ornith-1.5-35B-A3B-FP8 (36.0B, F8_E4M3)
Related reading: RTX PRO 6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
