What GPU do I need to run Alibaba-NLP/gte-Qwen2-7B-instruct?
7.6B parameters, published in F32. View on Hugging Face
gte-Qwen2-7B-instruct is published by Alibaba-NLP on Hugging Face, with 121,575 downloads and 483 likes to date. It's a Qwen2ForCausalLM model built for sentence-similarity, published natively in F32.
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 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run gte-Qwen2-7B-instruct at its published (F32) precision: 1× A40 on runpod, at $0.440/hr per GPU ($0.440/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Alibaba-NLP models
- gte-Qwen2-1.5B-instruct (1.8B, F32)
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
- gpt2 (137M, F32)
- Qwen3.5-9B (9.7B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
