What GPU do I need to run Qwen/Qwen2.5-3B-Instruct?
A 3.1B language model for chat and instruction-following. 3.1B parameters, published in BF16. View on Hugging Face
Qwen2.5-3B-Instruct is published by Qwen on Hugging Face, with 7,635,044 downloads and 557 likes to date. It's a Qwen2ForCausalLM model built for text-generation, published natively in BF16.
What Qwen2.5-3B-Instruct is
Qwen2.5-3B-Instruct is a 3.1B-parameter language model published by Alibaba (Qwen) on Hugging Face. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from Qwen2.5-3B-Instruct's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
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 | 5.7 GB | 6.9 GB | RTX 3060 | 1 | $0.110/hr |
| FP8 (quantized) | 2.9 GB | 3.4 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 1.4 GB | 1.7 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 Qwen2.5-3B-Instruct at its published (BF16) precision: 1× RTX 3060, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen2.5-3B-Instruct: common questions
Does Qwen2.5-3B-Instruct fit on a 8 GB GPU?
Yes. At BF16 it needs 6.9 GB of VRAM, so an 8 GB card holds it with 1.1 GB to spare. A 6 GB card is not enough for it at BF16.
What is the least VRAM Qwen2.5-3B-Instruct can run in?
1.7 GB, at INT4 (quantized), which fits a 6 GB card, against 6.9 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.
How to run Qwen2.5-3B-Instruct
Run Qwen2.5-3B-Instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve Qwen/Qwen2.5-3B-Instruct --tensor-parallel-size 1Run Qwen2.5-3B-Instruct with Ollama
Verified against Ollama's own library listing.
ollama run qwen2.5:3bRun Qwen2.5-3B-Instruct with GGUF quantizations
Prebuilt GGUF weights published at bartowski/Qwen2.5-3B-Instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf bartowski/Qwen2.5-3B-Instruct-GGUFSource: https://huggingface.co/bartowski/Qwen2.5-3B-Instruct-GGUF
Deploy Qwen2.5-3B-Instruct on Aquanode
Aquanode has no one-click deploy template for Qwen2.5-3B-Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 3060 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen2.5 models
- Qwen2.5-Coder-3B-Instruct (3.1B, BF16)
- Qwen2.5-3B (3.1B, BF16)
- Qwen2.5-3B-Instruct (3.1B, BF16)
- VibeThinker-3B (3.1B, BF16)
- Qwen2.5-Coder-3B (3.1B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
