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 , 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 →