Looking at this error, the issue is clear: the llama-server is trying to allocate a 262,144 token context (262K tokens) but only has 1024 MiB of GPU memory available, which is insufficient for such a large context size.

The root cause is a memory allocation failure due to the context size being much larger than available GPU memory. A 262K token context would require significantly more memory than the 1024 MiB available, likely requiring several gigabytes of VRAM.

**Root Cause:** Insufficient GPU memory for the requested context size of 262,144 tokens.

**Next Two Actions:**

1. **Reduce the context size** - Lower the context length parameter (likely `--ctx-size` or similar) to a much smaller value that fits within the available 1024 MiB GPU memory (e.g., try 2048 or 4096 tokens)

2. **Check memory usage and consider model quantization** - Verify actual GPU memory usage and consider using a quantized version of the model (4-bit or 8-bit) which would reduce memory requirements significantly, or switch to a smaller model variant that requires less memory