Text generation · served with vLLM · ↓ 237 downloads a month · View on Hugging Face
IFM/K2-Think needs about 77.4 GB of GPU memory for conversations of up to 8,192 tokens. It runs on one Nvidia A100 from $0.92/h right now; best value: RTX PRO 6000 at $1.39/h.
Weights
66 GB
GPU memory needed
about 77.4 GB
Conversation length
8,192 tokens
Model allows
131,072 tokens
Memory · disk
40.8 GB · 113 GB
Architecture
Qwen2ForCausalLM
Licence
apache-2.0
Hardware and deploy
$1.39/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 K2-Think need?
About 77.4 GB for conversations of up to 8,192 tokens: 65.53 GB of weights, the cache for one full-length conversation and the runtime's own memory.
What is the cheapest GPU to run K2-Think?
Right now one Nvidia A100 at $0.92 an hour; RTX PRO 6000 at $1.39 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can K2-Think run on an RTX 4090?
Not on one: it needs about 77.4 GB and an RTX 4090 has 24 GB. Use a bigger GPU, or a smaller (quantized) version of the model.
How do I deploy K2-Think 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.