# Gemma Model Deployment: Handling VRAM Spikes > Source: > Published: 2026-07-27 02:02:12+00:00 # Gemma Model Deployment: Handling VRAM Spikes `OutOfMemoryError` before the first token is even generated.The crash happens during the weights loading sequence. Here is the specific error I'm seeing in my logs: ``` torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.4GB (GPU 0; 24.00GB total capacity), 18.1GB already allocated. ``` After digging into the telemetry, it seems like the issue isn't the model size itself, but how the tensors are being allocated in the KV cache or a temporary peak during the conversion from half-precision to quantized formats. I tried limiting the context window to 2048 to see if that would stabilize the footprint, but the spike occurs during the `from_pretrained` call, not during inference. I suspect there's a mismatch between the loader's memory mapping and the actual GPU shards. I'm currently testing if switching to a different backend or updating the transformers library resolves this, as it feels like a resource management bug rather than a hardware limitation. If anyone has a stable deployment config for this specific version of Gemma, I'd be interested in seeing your environment specs. [Next Brain Waves: The Next Frontier for Physical AI →](/en/threads/3924/)