docs: frank-default-large llama.cpp draft-dflash deployment + benchmark results; retire Ling vLLM references

docs/DFlash DSpark Speculative Decoding.md

@@ -162,15 +162,17 @@ docker run --rm --name vllm-qwen36-dflash \
 
 ## Deployment on Dankclaw (AMD Strix Halo gfx1151)
 
-Dankclaw runs vLLM-ROCm as its primary runtime. The active service (`frank-default-large`, port 7770) serves the Ling-3.0-flash CIRU INT4 (W4A16) model with native **MTP speculative decoding** (K1, lossless). MTP uses a NextN head preserved in the checkpoint, so no separate draft model is needed.
+Dankclaw now runs llama.cpp as its runtime for `frank-default-large` (port 7770), serving the HauhauCS Qwen3.6-27B uncensored `Q8_K_P` model with a **DFlash draft model** (`draft-dflash`) on the ROCm (HIP) backend. The earlier vLLM-ROCm CIRU INT4 Ling-3.0-flash runtime (native MTP) was retired because the model had problems.
 
-Ling-3.0-flash facts:
+Current facts:
 
-- Base model: inclusionAI/Ling-3.0-flash, 124B total / 5.1B active (hybrid linear attention: KDA + MLA).
-- The MTP head is served in-band; vLLM detects it and shares the target embedding and lm_head weights with the draft head.
-- The earlier ROCmFPX llama.cpp service (raulvidis ROCmFP4 MTP GGUF) was retired because it was defective. vLLM now handles concurrency and tool calling correctly.
+- Main model: HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive `Q8_K_P` (dense 27B).
+- Draft model: Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp `Q8_0` (block size 16).
+- Spec command: `--spec-type draft-dflash` with `--spec-draft-n-max 8`.
+- Backend `--device ROCm0`; `--gpu-layers all --fit off`; KV `q8_0` K / `f16` V.
+- Context: 3 x 200700 = 602100; batch 2048 / ubatch 512.
 
-If you want DFlash or DSpark on Dankclaw, use a vLLM speculative-config draft that pairs with the target model. DFlash needs a non-causal attention backend; the ROCm flash_attn backend must be validated before production use. MTP is the proven, lossless path on gfx1151.
+Benchmark result: ROCm beats Vulkan, and DFlash-alone generates faster than a DFlash+ngram layer at 4k. See [[frank-default-large-benchmark]] for full numbers and the memory-bound batch/ubatch limits.
 
 ## Notes