What GPU do I need to run robbyant/lingbot-world-fast?
18.5B parameters, published in F32. View on Hugging Face
Set up lingbot-world-fastlingbot-world-fast is published by robbyant on Hugging Face, with 10,892 downloads and 22 likes to date. It's a unlisted-architecture model built for image-to-video, published natively in F32.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activation memory and allocator fragmentation. Diffusion and video models carry no KV-cache. The real driver of extra memory is output resolution and frame count, which this flat overhead does not model. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 69.1 GB | 82.9 GB | RTX PRO 6000 | 1 | $1.53/hr |
| cheaper alt. | V100 | 6 | $0.528/hr | ||
| FP8 (quantized) | 17.3 GB | 20.7 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 4070 | 2 | $0.242/hr | ||
| INT4 (quantized) | 8.6 GB | 10.4 GB | RTX 3060 | 1 | $0.110/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 lingbot-world-fast at its published (F32) precision: 1× RTX PRO 6000, at $1.53/hr per GPU ($1.53/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
lingbot-world-fast: common questions
Can lingbot-world-fast run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 82.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 96.0 GB RTX PRO 6000 at $1.53/hr.
Can lingbot-world-fast run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 69.1 GB, or 82.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 34.5 GB, or 41.4 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM lingbot-world-fast can run in?
10.4 GB, at INT4 (quantized), which fits a 12 GB card, against 82.9 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.
Does quantizing lingbot-world-fast lower the GPU bill?
Yes. At FP32 the cheapest live fit is one RTX PRO 6000 at $1.53/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More robbyant 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 PRO 6000 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.
