docs+deploy: Dankserver now llama.cpp frank-default-small (HauhauCS Q6_K_P + DFlash)

Retire vllm-awq-qwen36 (AWQ) from target maps, README, deployment, KB index,
Dankserver specs, DFlash and FP8 docs. New dankserver runtime: upstream
llama.cpp, port 7770, alias frank-default-small, 3x262144 ctx, DFlash Q8_0
draft, MoE experts offloaded to CPU, attention+KV on GPU (K q8_0, V f16).
deploy.sh now maps dankserver to frank-default-small and symlinks its unit.
models.json: dankserver baseUrl -> :7770, contextWindow -> 262144.

docs/DFlash DSpark Speculative Decoding.md

@@ -115,7 +115,9 @@ The DFlash draft is not a standalone model. It must pair with its target model.
 
 ## Deployment on Dankserver (NVIDIA RTX 3090)
 
-Dankserver runs vLLM in a Podman container with the RTX 3090. The RTX 3090 is Ampere (compute capability 8.6). Use these conditions:
+The active Dankserver deployment runs **llama.cpp + DFlash** (`frank-default-small`), not vLLM. It serves the HauhauCS Qwen3.6-35B-A3B uncensored `Q6_K_P` on port 7770 with the `Alittlehammmer/Qwen3.6-35B-A3B-DFlash` Q8_0 draft, 3 slots at 262144 context, and MoE experts on CPU. See [[deployment]] and `scripts/serve-llama-frank-small.sh`.
+
+The vLLM material below documents the archived AWQ/DFlash path on the same hardware. The RTX 3090 is Ampere (compute capability 8.6). Use these conditions:
 
 - FP8 KV cache is valid on CUDA 12.
 - FP8 weights compute through a fallback path; no native FP8 tensor cores.