What GPU do I need to run bottlecapai/ThinkingCap-Qwen3.8-27B?
27.8B parameters, published in BF16. View on Hugging FaceGated
Set up ThinkingCap-Qwen3.8-27BThinkingCap-Qwen3.8-27B is published by bottlecapai on Hugging Face, with 1,591 downloads and 150 likes to date. It's a Qwen3_5ForConditionalGeneration model built for image-text-to-text, published natively in BF16, 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 51.7 GB | 62.1 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/hr | ||
| FP8 (quantized) | 25.9 GB | 31.0 GB | RTX 4080 Super | 1 | $0.338/hr |
| INT4 (quantized) | 12.9 GB | 15.5 GB | RTX A5000 | 1 | $0.176/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 ThinkingCap-Qwen3.8-27B at its published (BF16) precision: 1× A100, at $1.21/hr per GPU ($1.21/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
ThinkingCap-Qwen3.8-27B: common questions
Can ThinkingCap-Qwen3.8-27B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 62.1 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
Do I need approval to download ThinkingCap-Qwen3.8-27B?
Yes. bottlecapai gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 62.1 GB the model needs once you have them.
What is the least VRAM ThinkingCap-Qwen3.8-27B can run in?
15.5 GB, at INT4 (quantized), which fits a 16 GB card, against 62.1 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing ThinkingCap-Qwen3.8-27B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More bottlecapai models
- bert-base-uncased (110M, F32)
- 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)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
