What GPU do I need to run audnai/penclaw-GLM-5.3-abliterated?
753.3B parameters, published in BF16. View on Hugging FaceGated
Set up penclaw-GLM-5.3-abliteratedpenclaw-GLM-5.3-abliterated is published by audnai on Hugging Face, with 1,658 downloads and 323 likes to date. It's a GlmMoeDsaForCausalLM model built for text-generation, 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 | 1403.2 GB | 1683.8 GB | No capable live offer found | – | – |
| FP8 (quantized) | 701.6 GB | 841.9 GB | No capable live offer found | – | – |
| INT4 (quantized) | 350.8 GB | 421.0 GB | A100 | 6 | $7.27/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.
penclaw-GLM-5.3-abliterated: common questions
Can penclaw-GLM-5.3-abliterated run on a single GPU?
Not on a desktop card. At BF16 it needs 1683.8 GB of VRAM, more than a single 32 GB desktop card holds. No card currently listed on the marketplace both supports BF16 and has enough VRAM for it, so how many it would take is not something this page can answer today.
Do I need approval to download penclaw-GLM-5.3-abliterated?
Yes. audnai 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 1683.8 GB the model needs once you have them.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More audnai 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: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
