What GPU do I need to run cyankiwi/Ornith-1.0-35B-AWQ-FP8?
35.1B parameters, published in F8_E4M3. View on Hugging Face
Ornith-1.0-35B-AWQ-FP8 is published by cyankiwi on Hugging Face, with 45,723 downloads and 2 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-1.0-35B-AWQ-FP8 at its published (F8_E4M3) precision: 1× L40 on massecompute, at $0.772/hr per GPU ($0.772/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 cyankiwi models
- gemma-4-E4B-it-AWQ-INT4 (8.2B, F16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
