What GPU do I need to run MuScriptor/muscriptor-large?
1.4B parameters, published in F32. View on Hugging FaceGated
Set up muscriptor-largemuscriptor-large is published by MuScriptor on Hugging Face, with 5,345 downloads and 377 likes to date. It's a unlisted-architecture model built for text-generation, published natively in F32, 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) |
|---|---|---|---|---|---|
| FP32 | 5.1 GB | 6.1 GB | RTX 3070 | 1 | $0.088/hr |
| FP8 (quantized) | 1.3 GB | 1.5 GB | RTX 4070 | 1 | $0.121/hr |
| INT4 (quantized) | 0.6 GB | 0.8 GB | RTX 3070 | 1 | $0.088/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 muscriptor-large at its published (F32) precision: 1× RTX 3070, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
muscriptor-large: common questions
Does muscriptor-large fit on a 8 GB GPU?
Yes. At FP32 it needs 6.1 GB of VRAM, so an 8 GB card holds it with 1.9 GB to spare. A 6 GB card is not enough for it at FP32.
Do I need approval to download muscriptor-large?
Yes. MuScriptor 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 6.1 GB the model needs once you have them.
Can muscriptor-large run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 5.1 GB, or 6.1 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 2.5 GB, or 3.1 GB with overhead. That moves it onto a 6 GB card instead of an 8 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM muscriptor-large can run in?
0.8 GB, at INT4 (quantized), which fits a 6 GB card, against 6.1 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More MuScriptor 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: RTX 3070 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
