How much GPU memory does Qwen3-VL-Embedding-2B-FP8 need?

About 4.9 GB: 2.85 GB of weights and the runtime's own memory.

What is the cheapest GPU to run Qwen3-VL-Embedding-2B-FP8?

Right now one RTX 3060 at $0.09 an hour; RTX 5070 Ti at $0.19 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.

Can Qwen3-VL-Embedding-2B-FP8 run on an RTX 4090?

Yes: it needs about 4.9 GB and an RTX 4090 has 24 GB.

How do I deploy Qwen3-VL-Embedding-2B-FP8 as an API?

Pick a machine on its GridPup page and press Deploy: GridPup rents the GPU, downloads the model from Hugging Face and serves it at an https address with keys and OpenAI's embeddings API, billed by the hour.

Models / Qwen3-VL-Embedding / Qwen3-VL-Embedding 2B

shigureui/Qwen3-VL-Embedding-2B-FP8

Embeddings · served with vLLM · ↓ 3.4K downloads a month · View on Hugging Face

shigureui/Qwen3-VL-Embedding-2B-FP8 needs about 4.9 GB of GPU memory. It runs on one RTX 3060 from $0.09/h right now; best value: RTX 5070 Ti at $0.19/h.

Weights: 2.9 GB
GPU memory needed: about 4.9 GB
Model allows: 262,144 tokens
Memory · disk: 8.3 GB · 44 GB
Architecture: Qwen3VLForConditionalGeneration
Licence: mit

Versions of Qwen3-VL-Embedding 2B

The same model packaged differently: a quantized version needs less GPU memory and runs on a cheaper machine.

Version	Runtime	Weights	GPU memory	Cheapest machine now
OriginalQwen/Qwen3-VL-Embedding-2B	vLLM	4.3 GB	6.6 GB	$0.09/h RTX 3060
AWQ 4bit (LifetimeMistake)LifetimeMistake/Qwen3-VL-Embedding-2B-AWQ-4bit	vLLM	2.8 GB	4.8 GB	$0.09/h RTX 3060
FP8 (shigureui)shigureui/Qwen3-VL-Embedding-2B-FP8	vLLM	2.9 GB	4.9 GB	$0.09/h RTX 3060

Hardware and deploy

Endpoint name

$0.19/h from your balance while it runs, billed by the hour; stop it from its page. GridPup rents the machine, which downloads the model from Hugging Face (usually ready in 10 to 15 minutes), and gives you an https address, keys and a Playground.

Questions

How much GPU memory does Qwen3-VL-Embedding-2B-FP8 need?: About 4.9 GB: 2.85 GB of weights and the runtime's own memory.
What is the cheapest GPU to run Qwen3-VL-Embedding-2B-FP8?: Right now one RTX 3060 at $0.09 an hour; RTX 5070 Ti at $0.19 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can Qwen3-VL-Embedding-2B-FP8 run on an RTX 4090?: Yes: it needs about 4.9 GB and an RTX 4090 has 24 GB.
How do I deploy Qwen3-VL-Embedding-2B-FP8 as an API?: Pick a machine on its GridPup page and press Deploy: GridPup rents the GPU, downloads the model from Hugging Face and serves it at an https address with keys and OpenAI's embeddings API, billed by the hour.

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shigureui/Qwen3-VL-Embedding-2B-FP8

Embeddings · served with vLLM · ↓ 3.4K downloads a month · View on Hugging Face

shigureui/Qwen3-VL-Embedding-2B-FP8 needs about 4.9 GB of GPU memory. It runs on one RTX 3060 from $0.09/h right now; best value: RTX 5070 Ti at $0.19/h.

Weights

2.9 GB

GPU memory needed

about 4.9 GB

Model allows

262,144 tokens

Memory · disk

8.3 GB · 44 GB

Architecture

Qwen3VLForConditionalGeneration

Licence

mit

Versions of Qwen3-VL-Embedding 2B

The same model packaged differently: a quantized version needs less GPU memory and runs on a cheaper machine.

Version	Runtime	Weights	GPU memory	Cheapest machine now
OriginalQwen/Qwen3-VL-Embedding-2B	vLLM	4.3 GB	6.6 GB	$0.09/h RTX 3060
AWQ 4bit (LifetimeMistake)LifetimeMistake/Qwen3-VL-Embedding-2B-AWQ-4bit	vLLM	2.8 GB	4.8 GB	$0.09/h RTX 3060
FP8 (shigureui)shigureui/Qwen3-VL-Embedding-2B-FP8	vLLM	2.9 GB	4.9 GB	$0.09/h RTX 3060

Hardware and deploy

Endpoint name

Questions

How much GPU memory does Qwen3-VL-Embedding-2B-FP8 need?: About 4.9 GB: 2.85 GB of weights and the runtime's own memory.
What is the cheapest GPU to run Qwen3-VL-Embedding-2B-FP8?: Right now one RTX 3060 at $0.09 an hour; RTX 5070 Ti at $0.19 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can Qwen3-VL-Embedding-2B-FP8 run on an RTX 4090?: Yes: it needs about 4.9 GB and an RTX 4090 has 24 GB.
How do I deploy Qwen3-VL-Embedding-2B-FP8 as an API?: Pick a machine on its GridPup page and press Deploy: GridPup rents the GPU, downloads the model from Hugging Face and serves it at an https address with keys and OpenAI's embeddings API, billed by the hour.