What GPU do I need to run mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic?
27.4B parameters, published in F8_E4M3. View on Hugging Face
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic is published by mconcat on Hugging Face, with 71,622 downloads and 16 likes to date. It's a Qwen3_5ForConditionalGeneration 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 Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-FP8-Dynamic at its published (F8_E4M3) precision: 1× RTX 5090 on vastai, at $0.444/hr per GPU ($0.444/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 mconcat models
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
- gpt2 (137M, F32)
- Qwen3.5-9B (9.7B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen2.5-7B-Instruct (7.6B, BF16)
