What GPU do I need to run autotrust/JEV-27B?
26.9B parameters, published in BF16. View on Hugging Face
Set up JEV-27BJEV-27B is published by autotrust on Hugging Face, with 168,077 downloads and 53 likes to date. It's a Qwen3_5ForCausalLM model built for text-classification, 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 50.1 GB | 60.1 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/hr | ||
| FP8 (quantized) | 25.0 GB | 30.1 GB | RTX 4080 Super | 1 | $0.338/hr |
| INT4 (quantized) | 12.5 GB | 15.0 GB | RTX A5000 | 1 | $0.176/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 JEV-27B at its published (BF16) precision: 1× A100, at $1.21/hr per GPU ($1.21/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
JEV-27B: common questions
Can JEV-27B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 60.1 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
What is the least VRAM JEV-27B can run in?
15.0 GB, at INT4 (quantized), which fits a 16 GB card, against 60.1 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 JEV-27B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More autotrust models
- JEV-27B-VL (27.8B, BF16)
- GEV-26B-Decide (25.8B, BF16)
- JEV-9B (9.0B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
