What GPU do I need to run farbodtavakkoli/OTel-2.0-LLM-31B-IT?
32.1B parameters, published in BF16. View on Hugging Face
Set up OTel-2.0-LLM-31B-ITOTel-2.0-LLM-31B-IT is published by farbodtavakkoli on Hugging Face, with 6,777,482 downloads and 14 likes to date. It's a unlisted-architecture model built for text-generation, published natively in BF16.
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 OTel-2.0-LLM-31B-IT at its published (BF16) precision: 1× RTX PRO 6000 on simplepod, at $1.00/hr per GPU ($1.00/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
OTel-2.0-LLM-31B-IT: common questions
Can OTel-2.0-LLM-31B-IT run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 71.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 95.0 GB RTX PRO 6000 on simplepod at $1.00/hr.
What is the least VRAM OTel-2.0-LLM-31B-IT can run in?
17.9 GB, at INT4 (quantized), which fits a 24 GB card, against 71.8 GB at BF16. 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 OTel-2.0-LLM-31B-IT lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX PRO 6000 on simplepod at $1.00/hr. At INT4 (quantized) it drops to one RTX 3090 on akash at $0.147/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More farbodtavakkoli 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)
