docs: Update vLLM latest installation instructions with CUDA and non-CUDA paths

[sneha-rudra]

1. This change updates the vLLM installation section in README.md to clearly distinguish between environments that already have CUDA libraries installed and those that do not.
2. It provides separate installation commands for each scenario using vLLM version 0.11.0, clarifies when to use the direct install versus the extra index for PyTorch nightly wheels
3. Adds tips for troubleshooting CUDA or torch import errors on different setups.

This helps ensure users select the correct installation steps for their environment and resolve common setup issues.

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Installing vLLM without CUDA wheel leaves torch CPU-only

The new "CUDA already installed" path tells users to run uv pip install vllm==0.11.0 ... with no extra index, claiming vLLM will “detect your CUDA setup and manage the correct torch version automatically”. PyPI only ships CPU-only torch wheels; without the download.pytorch.org index, pip installs the CPU build even when CUDA libraries are present. Launching vllm serve after this install fails with Torch not compiled with CUDA enabled or runs on CPU, defeating the purpose for most GPU containers. The docs should still instruct users to install a CUDA-enabled torch wheel (e.g. via the extra index or explicit torch==...+cuXXX).

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