What GPU do I need to run orcarouter/GLM-5.3-Flash-Uncensored-FP8?
321.3B parameters, published in F8_E4M3. View on Hugging FaceGated
Set up GLM-5.3-Flash-Uncensored-FP8GLM-5.3-Flash-Uncensored-FP8 is published by orcarouter on Hugging Face, with 2,576 downloads and 147 likes to date. It's a Glm5NextForConditionalGeneration model built for text-generation, 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 299.3 GB | 359.1 GB | RTX 4090 | 8 | $3.53/hr |
| INT4 (quantized) | 149.6 GB | 179.6 GB | RTX A5000 | 8 | $1.41/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 GLM-5.3-Flash-Uncensored-FP8 at its published (F8_E4M3) precision: 8× RTX 4090, at $0.441/hr per GPU ($3.53/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
GLM-5.3-Flash-Uncensored-FP8: common questions
Can GLM-5.3-Flash-Uncensored-FP8 run on a single GPU?
No. At FP8 (native) it needs 359.1 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 48.0 GB RTX 4090, and it takes 8 of them.
Do I need approval to download GLM-5.3-Flash-Uncensored-FP8?
Yes. orcarouter 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 359.1 GB the model needs once you have them.
Is GLM-5.3-Flash-Uncensored-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 359.1 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 179.6 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
How many GPUs do I need to run GLM-5.3-Flash-Uncensored-FP8?
8 at FP8 (native). It needs 359.1 GB of VRAM and the cheapest capable live offer is a 48.0 GB RTX 4090, so 8 of them come to $3.53/hr in total.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More GLM-5 models
- GLM-5.3-Flash (321.3B, F8_E4M3)
- GLM-5.3-Flash-BF16 (321.3B, BF16)
- GLM-5.3-Flash-FP8 (321.3B, F8_E4M3)
- GLM-5.3-Flash-UNCENSORED-FP8 (321.3B, F8_E4M3)
- GLM-5.3-Flash (321.3B, BF16)
Related reading: RTX 4090 pricing and specs, and The best GPUs for AI, ranked.
