# Kimi K3 Weights: Initial Deployment Notes

> Source: <https://promptcube3.com/en/threads/3993/>
> Published: 2026-07-27 18:28:39+00:00

# Kimi K3 Weights: Initial Deployment Notes

The specific error I hit looked like this:

```
RuntimeError: CUDA out of memory. Tried to allocate 2.4GB (GPU 0), total capacity 24GB, already allocated 21.2GB.
```

After digging through the logs, I realized the issue wasn't the model weights themselves, but the KV cache allocation during the first few inference passes. I had to manually tweak the `max_seq_len`

in my config to stop it from grabbing too much VRAM upfront.

If anyone is attempting a deep dive into Kimi K3 for a real-world AI workflow, I highly recommend starting with a GGUF or EXL2 quant to avoid these memory spikes. Once I capped the context window and optimized the loader, the throughput stabilized.

For those looking for a practical tutorial on getting this running, make sure your environment is updated to the latest transformers version. I'm still testing the reasoning capabilities compared to other open-weights models, but the deployment phase is finally sorted.

[Next Gemma Model Deployment: Handling VRAM Spikes →](/en/threads/3943/)