OpenGVLab/VideoChat-R1-thinking_7B needs about 21.7 GB of GPU memory for conversations of up to 16,384 tokens. It runs on one RTX 3090 from $0.26/h right now; best value: RTX PRO 5000 at $0.67/h.
Weights
17 GB
GPU memory needed
about 21.7 GB
Conversation length
16,384 tokens
Model allows
128,000 tokens
Memory · disk
16.3 GB · 59 GB
Architecture
Qwen2_5_VLForConditionalGeneration
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 VideoChat-R1-thinking_7B need?
About 21.7 GB for conversations of up to 16,384 tokens: 16.58 GB of weights, the cache for one full-length conversation and the runtime's own memory.
What is the cheapest GPU to run VideoChat-R1-thinking_7B?
Right now one RTX 3090 at $0.26 an hour; RTX PRO 5000 at $0.67 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can VideoChat-R1-thinking_7B run on an RTX 4090?
Yes: it needs about 21.7 GB and an RTX 4090 has 24 GB.
How do I deploy VideoChat-R1-thinking_7B 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 chat 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.