What GPU do I need to run Alibaba-NLP/gte-Qwen2-7B-instruct?
7.6B parameters, published in F32. View on Hugging Face
Set up gte-Qwen2-7B-instructgte-Qwen2-7B-instruct is published by Alibaba-NLP on Hugging Face, with 117,005 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 28.4 GB | 34.0 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | V100 | 3 | $0.264/hr | ||
| FP8 (quantized) | 7.1 GB | 8.5 GB | RTX 4070 | 1 | $0.121/hr |
| INT4 (quantized) | 3.5 GB | 4.3 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 gte-Qwen2-7B-instruct at its published (F32) precision: 1× RTX A6000, at $0.363/hr per GPU ($0.363/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gte-Qwen2-7B-instruct: common questions
Can gte-Qwen2-7B-instruct run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 34.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX A6000 at $0.363/hr.
Can gte-Qwen2-7B-instruct run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 28.4 GB, or 34.0 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 14.2 GB, or 17.0 GB with overhead. That moves it onto a 24 GB card, which the FP32 weights do not fit. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM gte-Qwen2-7B-instruct can run in?
4.3 GB, at INT4 (quantized), which fits a 6 GB card, against 34.0 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 gte-Qwen2-7B-instruct lower the GPU bill?
Yes. At FP32 the cheapest live fit is one RTX A6000 at $0.363/hr. At INT4 (quantized) it drops to one RTX 3070 at $0.088/hr, provided a quantized checkpoint exists for it.
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)
- Tongyi-DeepResearch-30B-A3B (30.5B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
Related reading: RTX A6000 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.
