## Turn 1

*   Error message: `llama-server: failed to allocate KV cache`
    *   Requested context (`ctx`): 262,144 tokens.
    *   Available GPU memory (`gpu memory free`): 1,024 MiB (1 GB).

    *   The KV (Key-Value) cache stores the attention states for all tokens in the context window.
    *   The memory required for the KV cache scales linearly with the context length, model dimension, number of layers, and precision (FP16, BF16, Q8, etc.).
    *   Requesting a context of 262k tokens requires a massive amount of VRAM (likely tens of gigabytes depending on the model).
    *   The system only has 1 GB of free VRAM.
    *   *Root Cause:* The requested context size is far too large for the available GPU memory.

    *   *Action 1: Reduce the context size.* The most immediate fix is to lower the `-c` or `--ctx-size` parameter to something the GPU can handle (e.g., 4096 or 8192).
    *   *Action 2: Offload fewer layers or use a smaller model/quantization.* If the user *needs* a larger context, they need to free up VRAM by moving layers to the CPU (reducing `-ngl`) or using a more compressed quantization (e.g., Q4_K_M instead of Q8). Alternatively, use KV cache quantization (e.g., `-ctk q8_0` or `-ctv q8_0` in newer llama.cpp versions).

    *   Root Cause: Out of Memory (OOM) due to an excessively large context window request relative to available VRAM.
    *   Action 1: Decrease the context size (`-c` parameter).
    *   Action 2: Reduce GPU layer offloading (`-ngl`) or enable KV cache quantization.