Since prompt evaluation (prefill) is slower than generation (decoding), the bottleneck is likely in **compute throughput**, **memory bandwidth**, or **VRAM spilling**.

### 1. GPU Offloading & VRAM
*   **Check `n_gpu_layers`:** Ensure all layers are offloaded to the GPU (set to `-1` or the maximum number of layers).
*   **Verify VRAM Capacity:** If the model + KV cache exceeds your VRAM, `llama.cpp` will spill to System RAM, causing a massive performance drop during prefill.
*   **Check Memory Type:** Ensure you aren't running in "Partial Offload" mode where the KV cache is stored in System RAM while the weights are on the GPU.

### 2. Batching Parameters (The "Prefill" Knobs)
*   **Increase `n_batch`:** This controls how many tokens are processed in one parallel pass. For prefill, increase this (e.g., `2048` or `4096`) until you hit a performance plateau.
*   **Increase `n_ubatch`:** This is the number of tokens processed in a single "micro-batch." Ensure this is high enough to saturate your GPU's CUDA cores.

### 3. Attention Mechanism
*   **Enable Flash Attention:** Use the `-fa` or `--flash-attn` flag. This significantly reduces the memory and compute overhead of the attention mechanism during the prefill stage.
*   **Check `n_ctx`:** Ensure your context window (`-c`) is not unnecessarily large. A massive context window increases the memory footprint of the KV cache, which can slow down the initial processing of the prompt.

### 4. Hardware & Driver Check
*   **Thermal Throttling:** Monitor GPU temperatures. If the GPU hits a thermal limit during the heavy compute of prefill, it will downclock.
*   **Power Limits:** Ensure your GPU is receiving sufficient wattage (check `nvidia-smi` for power draw).
*   **CUDA/ROCm Version:** Ensure your drivers and toolkit are up to date. Outdated kernels can lead to inefficient memory management.

### 5. Model Specifics
*   **Quantization Type:** If using a very high-bit quantization (e.g., Q8_0 or F16), the compute requirements are much higher. Try a Q4_K_M or Q5_K_M to see if prefill speed improves.
*   **Model Architecture:** Some architectures (like MoE) handle prefill differently; ensure you are using the latest `llama.cpp` build to support the specific architecture of your model.

**Quick Summary Command Check:**
`--n_gpu_layers -1 --n_batch 2048 --n_ubatch 512 --flash-attn`