frank-default-small: use split-mode none on single-GPU dankserver

--split-mode graph requires >= 2 devices in ik_llama.cpp and errors hard
at model load on a single RTX 3090 ("It is not possible to use split mode
'graph' with less than 2 devices"), looping the service. Set SPLIT_MODE
=none (the default), which works with the --override-tensor CPU expert
offload. KB updated with the two-device requirement so we do not assume
graph degrades gracefully on one GPU.

scripts/frank-default-small.env

@@ -76,9 +76,11 @@ CACHE_TYPE_V=q8_KV
 K_CACHE_HADAMARD=1
 V_CACHE_HADAMARD=1
 
-# Split mode: graph (multi-GPU graph split; on a single 3090 this is a no-op
-# for correctness but kept per operator preference).
-SPLIT_MODE=graph
+# Split mode: none. Dankserver has a single RTX 3090 and ik_llama requires
+# >= 2 devices for split-mode graph ('It is not possible to use split mode
+# graph with less than 2 devices' is a hard load error). none is correct
+# here and still works with OVERRIDE_EXPERT CPU expert offload.
+SPLIT_MODE=none
 
 VERBOSITY=4
 # Store context checkpoints in RAM to hold peak GPU memory down.

scripts/serve-frank-small.sh

@@ -10,10 +10,8 @@
 # KV cache: q8_KV 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).
 #
-# Split mode graph + --override-tensor CPU expert offload. Per ik_llama
-# README, `--split-mode graph` + partial GPU offload can occasionally produce
-# incoherent output; the documented fallback is `-cuda graphs=0`. Smoke-test
-# after each model load.
+# 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