What GPU do I need to run dealignai/GLM-5.3-Flash-ABLITERATED-FP8?
321.3B parameters, published in F8_E4M3. View on Hugging Face
Set up GLM-5.3-Fla…-ABLITERATED-FP8GLM-5.3-Flash-ABLITERATED-FP8 is published by dealignai on Hugging Face, with 676 downloads and 15 likes to date. It's a Glm5NextForConditionalGeneration 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 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run GLM-5.3-Flash-ABLITERATED-FP8 at its published (F8_E4M3) precision: 8× L40 on runpod, at $0.690/hr per GPU ($5.52/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 dealignai models
- GLM-5.3-Flash-UNCENSORED-FP8 (321.3B, F8_E4M3)
- Qwen3.8-Flash-Next-UNCENSORED-FP8 (180.0B, F8_E4M3)
- GLM-5.3-CYBERSECURITY-FP8 (753.3B, F8_E4M3)
- GLM-5.3-UNCENSORED-FP8 (753.3B, F8_E4M3)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
