What GPU do I need to run orcarouter/Qwen3.8-27B-Uncensored-FP8?
27.8B parameters, published in F8_E4M3. View on Hugging FaceGated
Qwen3.8-27B-Uncensored-FP8 is published by orcarouter on Hugging Face, with 316,128 downloads and 1,337 likes to date. It's a Qwen3_5ForConditionalGeneration model built for image-text-to-text, published natively in F8_E4M3, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
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 Qwen3.8-27B-Uncensored-FP8 at its published (F8_E4M3) precision: 1× RTX 4080 Super on simplepod, at $0.380/hr per GPU ($0.380/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 orcarouter models
- 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)
- Qwen2.5-7B-Instruct (7.6B, BF16)
