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Deploy When Available is now GA
Queue for any GPU spec, even one that's fully rented out, and we'll deploy it the moment capacity opens up. No more refreshing the console or running a sniping tool.
All
1. Create Network Storage (2TB), Use the CA-MTL-4 region (recommended for now).
2. Spin Up a Pod using Runpod official Pytorch template and mount the network volume you just created, once the pod is running, connect to Jupyter Lab
3. Download the Model
- In the Jupyter terminal, install the Hugging Face CLI and download the model:
4. Launch the Instant Cluster
- Choose Standard Cluster
- Select the network volume you created
- Set Pod Count to 2 (default is 2)
- Pick H200 SXM GPU type
- Select Runpod Pytorch official template
- Click Edit Template and set container disk size to be 2000 GB
- Click Deploy Cluster to launch it.
1. installation on Node 0 with a shared volume
2. Node 1 Instructions
3. You should see the following:
4. Run on node with ip as host.
Open AI Implementation
Expected Output:
Known Issues
Currently as of July 21st, vllm library is not up to date so need to build from nightly builds.
https://github.com/MoonshotAI/Kimi-K2/issues/19
Gotchas
uv environment on a Network Volume is slow to initialize ray, recommend any python environments be ran on the machine itself instead of on the Network Volume.
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Author profile: Brendan McKeag
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