Gemma Model Deployment: Handling VRAM Spikes

A developer reports that deploying Google's Gemma model causes a CUDA out-of-memory error during the weights loading sequence, with 18.1GB already allocated on a 24GB GPU. The spike occurs during the from_pretrained call, not during inference, and limiting the context window to 2048 does not resolve it. The developer suspects a resource management bug and is testing backend or library updates.

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/