ZhishanQ/qwen3-embedding-redundancy-detector-0.6B needs about 2.9 GB of GPU memory. It runs on one RTX 3060 from $0.09/h right now; best value: RTX 5090 at $0.67/h.
Weights
1.2 GB
GPU memory needed
about 2.9 GB
Model allows
32,768 tokens
Memory · disk
8 GB · 42 GB
Architecture
Qwen3ForCausalLM
Licence
apache-2.0
Hardware and deploy
$0.67/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-embedding-redundancy-detector-0.6B need?
About 2.9 GB: 1.19 GB of weights and the runtime's own memory.
What is the cheapest GPU to run qwen3-embedding-redundancy-detector-0.6B?
Right now one RTX 3060 at $0.09 an hour; RTX 5090 at $0.67 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can qwen3-embedding-redundancy-detector-0.6B run on an RTX 4090?
Yes: it needs about 2.9 GB and an RTX 4090 has 24 GB.
How do I deploy qwen3-embedding-redundancy-detector-0.6B 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.
Prices are per hour for the whole machine and its disk, right now. Speed is Vast's DLPerf score of the machine, relative to the cheapest. Best value: the most speed per dollar. Only machines with a Vast reliability above 95% that download at 400 Mbit/s or more are offered: the machine fetches the model itself.