Video + text · served with vLLM · ↓ 111 downloads a month · View on Hugging Face
allenai/SAGE-MM-Qwen3-VL-8B-SFT needs about 24.4 GB of GPU memory for conversations of up to 16,384 tokens. It runs on 2x RTX A4000 from $0.33/h right now; best value: RTX 5090 at $0.67/h.
Weights
18 GB
GPU memory needed
about 24.4 GB
Conversation length
16,384 tokens
Model allows
262,144 tokens
Memory · disk
16.8 GB · 60 GB
Architecture
Qwen3VLForConditionalGeneration
Licence
apache-2.0
Versions of SAGE-MM-Qwen3-VL 8B
The same model packaged differently: a quantized version needs less GPU memory and runs on a cheaper machine.
$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 SAGE-MM-Qwen3-VL-8B-SFT need?
About 24.4 GB for conversations of up to 16,384 tokens: 17.53 GB of weights, the cache for one full-length conversation and the runtime's own memory.
What is the cheapest GPU to run SAGE-MM-Qwen3-VL-8B-SFT?
Right now 2x RTX A4000 at $0.33 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 SAGE-MM-Qwen3-VL-8B-SFT run on an RTX 4090?
Not on one: it needs about 24.4 GB and an RTX 4090 has 24 GB. An RTX 5090 (32 GB) is enough.
How do I deploy SAGE-MM-Qwen3-VL-8B-SFT 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.