Model Releases
Gemma 4:e4b offloads to RAM despite having just half of VRAM used.
Users on the r/ollama subreddit reported that the **Gemma 4 E4B** model in Ollama offloads layers to RAM even when GPU VRAM is only partially utilized. This behavior is linked to how Ollama and lla...
Users on the r/ollama subreddit reported that the Gemma 4 E4B model in Ollama offloads layers to RAM even when GPU VRAM is only partially utilized. This behavior is linked to how Ollama and llama.cpp handle memory allocation — the official memory figures cover static model weights only, and the KV cache and long prompts add additional VRAM on top of that , which can cause the runtime to spill layers into system RAM sooner than expected. Ollama handles the GPU-CPU split automatically: if the model exceeds available VRAM, Ollama detects the available memory and splits layers between GPU and CPU without requiring manual tuning , meaning apparent "free" VRAM may still be insufficient once KV cache and runtime overhead are factored in.
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Source: model-releases