frank-default-small: use q8_0 (not q8_KV) KV cache on ik_llama

The ik_llama fast q8_KV cache type crashes at model init on this
QWEN3MOE/MTP + multimodal arch at commit b166e269 with a ggml_view size
assert (ggml.c:5427) right after the output buffer is created, looping
the service. Empirically isolated by toggling cache type: q8_0 and f16
load and serve (smoke-tested correct output), q8_KV asserts. Switch to
q8_0 + Hadamard (K/V), which still gives the 8-bit cache + Hadamard
quality win. KB updated with the crash lesson.

scripts/frank-default-small.env

@@ -5,12 +5,12 @@
 # 2026-08-26 from commit b166e269) on RTX 3090 24 GB; Ryzen 7 7700X; 62 GB
 # RAM. Previous runtime was upstream llama.cpp b10121. MoE expert weights
 # pinned to CPU with an explicit --override-tensor regex (not --n-cpu-moe).
-# KV cache quantized to q8_KV with Hadamard transforms on both K and V.
-# --split-mode graph enabled (single GPU; retained per operator preference).
+# KV cache quantized to q8_0 with Hadamard transforms on both K and V.
+# Split mode none (single GPU; graph would require 2+ devices).
 #
 # Build flags (IK_FA note, see docs/LLM/ikllama-ornith-dankserver.md) must
 # ALWAYS include: -DGGML_CUDA_FA_ALL_QUANTS=ON and -DGGML_IQK_FA_ALL_QUANTS=ON
-# so the q8_KV + Hadamard KV cache combos compile into the CUDA/CPU FA
+# so the quantized + Hadamard KV cache combos compile into the CUDA/CPU FA
 # kernels. Replacing the HauhauCS Qwen3.6-35B-A3B model on 2026-08-24.
 # Switched from CRACK Q8_0 to Q6_K on 2026-08-25.
 #
@@ -67,12 +67,14 @@ OVERRIDE_BUFT=CPU
 # OVERRIDE_EXPERT below.
 N_GPU_LAYERS=999
 
-# KV cache on GPU, both K and V at q8_KV (fast quant type on ik_llama, PR 208)
-# plus Hadamard transforms on both caches to preserve quality at 8-bit.
-# For quantized KV below Q6_0 the docs recommend --k-cache-hadamard; we apply
-# --v-cache-hadamard to V as well.
-CACHE_TYPE_K=q8_KV
-CACHE_TYPE_V=q8_KV
+# KV cache on GPU, both K and V at q8_0 plus Hadamard transforms on both
+# caches to preserve quality at 8-bit. NOTE: ik_llama's fast q8_KV type
+# crashes at model init on this MTP/multimodal arch with a ggml_view size
+# assert (ggml.c:5427) at commit b166e269; plain q8_0 works. Docs recommend
+# --k-cache-hadamard for quantized KV below Q6_0; we apply --v-cache-hadamard
+# to V as well. Flash-attention is on by default.
+CACHE_TYPE_K=q8_0
+CACHE_TYPE_V=q8_0
 K_CACHE_HADAMARD=1
 V_CACHE_HADAMARD=1
 

scripts/serve-frank-small.sh

@@ -7,11 +7,14 @@
 # 35B-total / 3B-active MoE-dense hybrid with an MTP head bundled in the GGUF;
 # this service uses ngram-mod instead of MTP speculation.
 #
-# KV cache: q8_KV with Hadamard transforms on K and V (requires a build with
+# KV cache: q8_0 with Hadamard transforms on K and V (requires a build with
 # GGML_CUDA_FA_ALL_QUANTS=ON + GGML_IQK_FA_ALL_QUANTS=ON; see the env file).
+# The ik_llama fast q8_KV type crashes at init on this arch (ggml.c:5427);
+# q8_0 is used instead.
 #
 # Split mode is none: Dankserver has a single RTX 3090 and ik_llama rejects
 # split-mode graph with fewer than 2 devices (hard load error).
+
 #
 # Expert offload: the expert (FFN exps) tensors selected by the layer-bounded
 # --override-tensor pattern are pinned to CPU. Selected full-attention and