hermes - 💡(How to fix) Fix [vision] torchvision import initializes CUDA context, causing gateway/agent to reserve ~7GB VRAM each

Describe the bug

After upgrading to v0.14.0, both hermes gateway and hermes agent processes each reserve ~7GB of GPU VRAM at startup, even when idle and not performing any vision tasks. This is caused by torchvision being installed in the hermes venv — importing torchvision initializes a CUDA context that pre-allocates memory.

Impact

• On systems with limited GPU memory (e.g., single GPU shared between inference services and Hermes), this ~14GB reservation significantly reduces available VRAM for other workloads (sglang, ComfyUI, etc.)
• Users who rely on external multimodal LLMs (GPT-4o, Claude, etc.) do not benefit from the local vision_analyze pixel-through feature, making this VRAM cost purely wasteful

Root cause

v0.14.0 introduced vision_analyze pixel-through to vision-capable models (#22955), which added torchvision as a dependency. When torchvision is imported, it loads libcudart and initializes a CUDA context, which reserves ~7GB per process on NVIDIA GPUs.

Reproduction

1. Upgrade to v0.14.0+
2. Start hermes gateway run
3. Run nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader
4. Observe gateway process reserving ~7GB VRAM

Environment

• Hermes Agent: v0.14.0
• GPU: NVIDIA RTX PRO 6000 Blackwell 96GB
• PyTorch: 2.11.0+cu130
• torchvision: 0.26.0

Suggested fix

• Lazy-import torchvision only when vision_analyze is actually called with a local vision model, not at gateway/agent startup
• Or add PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to reduce pre-allocation
• Or make torchvision an optional dependency that is only loaded when the active model supports local vision