Ornith-1.0-35B-FP8 GPU Requirements: VRAM & Cheapest GPU
Ornith-1.0-35B-FP8 has about 35.1B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.
To run Ornith-1.0-35B-FP8 for inference at FP16, you need roughly 77 GB of VRAM. The cheapest fit on Spheron is A100 80GB at about $0.85/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
- 01$0.85/hr$0.85/hr1× A100 80GBCHEAPESTAmpere · HBM2e
- 02$0.86/hr$0.86/hr1× RTX PRO 6000 96GBBlackwell · GDDR7
- 03$0.61/hr$1.22/hr2× L40S 48GBAda Lovelace · GDDR6
- 04$1.77/hr$1.77/hr1× H200 141GBHopper · HBM3e
- 05$1.88/hr$1.88/hr1× GH200 96GBGrace Hopper · HBM3
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run Ornith-1.0-35B-FP8
Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.
| Precision | Inference | LoRA fine-tune | Full fine-tune |
|---|---|---|---|
| FP16 | 77 GB | 115 GB | 306 GB |
| INT8 | 38 GB | 57 GB | 153 GB |
| INT4 | 19 GB | 29 GB | 77 GB |
Cheapest GPU to run Ornith-1.0-35B-FP8 by precision
Full precision. Best quality, highest memory.
8-bit quantized. ~2x smaller, minimal quality loss.
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Ornith-1.0-35B-FP8
Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For Ornith-1.0-35B-FP8, an on-demand A100 80GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.
Similar models
Compare GPU requirements for models in the same class.
Ornith-1.0-35B-FP8 GPU questions
Ornith-1.0-35B-FP8 has about 35.1B parameters. At FP16 it needs roughly 77 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 19 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron is A100 80GB at about $0.85/hr. If you quantize to INT4, RTX 4090 24GB at about $0.58/hr runs it for less. Pricing is aggregated live from 5+ data center partners with per-minute billing.
LoRA fine-tuning adds roughly 50% on top of inference memory, so it usually fits the same class of GPU. Full fine-tuning holds gradients and optimizer state and needs about 4x the inference VRAM, which often means multiple GPUs for Ornith-1.0-35B-FP8. The VRAM matrix above shows the exact estimate for each setup.
We read the parameter count directly from the model's safetensors metadata on HuggingFace, then estimate peak VRAM from weights, activations, KV cache, and framework overhead at your chosen precision. The estimate lands within about 15% of real-world use for most transformer models.
Quantized to INT4, Ornith-1.0-35B-FP8 needs about 19 GB of VRAM, so it fits on a 24 GB RTX 4090 with room for a small batch. At FP16 it needs roughly 77 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.