diff --git a/scripts/frank-small.env b/scripts/frank-small.env
index d0f2a93..c84a785 100644
--- a/scripts/frank-small.env
+++ b/scripts/frank-small.env
@@ -29,21 +29,24 @@
 # for as many concurrent sessions as the card holds.
 #   MAX_MODEL_LEN=262144      the model's max_position_embeddings
 #   GPU_MEM_UTIL=0.95         the card is dedicated to this service
-#   KV_CACHE_DTYPE=int8_per_token_head  int8 KV (per-token, per-head scales)
-#                             in the same TRITON_ATTN backend — pure ALU
-#                             dequant, no fp8 hardware needed, so it runs
-#                             on sm_86. fp8_e4m3 is the cheaper option but
-#                             the 3090 is sm_86 (Ampere) and has no native
-#                             fp8: the TRITON_ATTN backend Gemma4 forces
-#                             emits cvt.e4m3x2 instructions that exist only
-#                             from sm_89 (Ada), so fp8 KV cannot boot on
-#                             this card (measured 2026-09-19; the "sm_89
-#                             patch" experiment is in .AGENTTRASH). int8
-#                             halves the KV charge (289,647 -> ~579k-token
-#                             pool, two -> four full-context sessions) at
-#                             the cost of a dequant in the attention path;
-#                             quality verified against the bf16 baseline
-#                             (needle + short battery) on 2026-09-19.
+#   KV_CACHE_DTYPE=turboquant_k8v4   TurboQuant KV: K at 8-bit, V at 4-bit,
+#                             per-16-element block scales packed INSIDE the
+#                             tensor (Hadamard-rotated, bf16 compute,
+#                             dedicated TURBOQUANT attention backend with
+#                             an explicit Ampere path). 2.67x smaller KV
+#                             than bf16 (289,647 -> ~772k-token pool, two
+#                             -> five-or-six full-context sessions).
+#                             The cheaper int8/fp8 per-token-head dtypes
+#                             CANNOT boot this model: they keep a separate
+#                             fp32 scale tensor per (token, head), and
+#                             Gemma4's hybrid head dims (SWA 256 / full
+#                             512) make the per-token page bytes 4160 vs
+#                             8256 — not an integer ratio, so vLLM's
+#                             unify_kv_cache_spec_page_size raises
+#                             NotImplementedError (measured 2026-09-19,
+#                             nine crash-loop restarts). fp8 KV is
+#                             hardware-dead on this card anyway (sm_86,
+#                             no cvt.e4m3x2).
 #   MAX_NUM_SEQS=16           the batch ceiling; the KV pool, not this,
 #                             caps full-context concurrency
 #   MAX_NUM_BATCHED_TOKENS=16384  the prefill chunk; larger chunks prefill
@@ -77,11 +80,11 @@ ALIAS=frank-small
 BIND_ADDR=0.0.0.0
 LISTEN_PORT=8000
 
-# Full window, dedicated card, int8 KV (see above), roomy batch ceiling,
+# Full window, dedicated card, turboquant k8v4 KV (see above), roomy batch ceiling,
 # 16k prefill chunk, 4-token MTP draft (the assistant carries 4 layers).
 MAX_MODEL_LEN=262144
 GPU_MEM_UTIL=0.95
-KV_CACHE_DTYPE=int8_per_token_head
+KV_CACHE_DTYPE=turboquant_k8v4
 MAX_NUM_SEQS=16
 MAX_NUM_BATCHED_TOKENS=16384
 SPEC_NUM_TOKENS=4
diff --git a/tests/frank_small_unit.bats b/tests/frank_small_unit.bats
index 369dfd3..2fdef35 100755
--- a/tests/frank_small_unit.bats
+++ b/tests/frank_small_unit.bats
@@ -98,7 +98,7 @@ QUADLET_LARGE="$REPO_ROOT/dankclaw/frank-large/quadlet/frank-large.container"
     ! grep -q 'HALOGEN_VISION_TOWER' "$QUADLET_LARGE"
 }
 
-@test "frank-small serves the full window with int8 KV on a dedicated card" {
+@test "frank-small serves the full window with turboquant k8v4 KV on a dedicated card" {
     local maxlen kvdtype util seqs
     maxlen="$(sed -nE 's/^MAX_MODEL_LEN=([0-9]+)$/\1/p' "$ENV_FILE")"
     kvdtype="$(sed -nE 's/^KV_CACHE_DTYPE=(.+)$/\1/p' "$ENV_FILE")"
@@ -106,12 +106,13 @@ QUADLET_LARGE="$REPO_ROOT/dankclaw/frank-large/quadlet/frank-large.container"
     seqs="$(sed -nE 's/^MAX_NUM_SEQS=([0-9]+)$/\1/p' "$ENV_FILE")"
     # The model's FULL trained window (262144), not a divided one.
     [ "$maxlen" = "262144" ]
-    # int8 KV (per-token, per-head scales): fp8_e4m3 would halve the pool
-    # charge further, but the 3090 is sm_86 (Ampere) — no native fp8, and
-    # the TRITON_ATTN backend that Gemma4 forces emits cvt.e4m3x2
-    # instructions that exist only from sm_89. int8 is pure ALU dequant in
-    # the same backend, so it runs.
-    [ "$kvdtype" = "int8_per_token_head" ]
+    # TurboQuant k8v4 KV (K 8-bit, V 4-bit, in-tensor block scales,
+    # dedicated TURBOQUANT backend with an Ampere path). The int8/fp8
+    # per-token-head dtypes cannot boot this model: their separate fp32
+    # scale tensor breaks the page-size ratio between Gemma4's SWA
+    # (head_dim 256) and full-attention (head_dim 512) layers, and fp8 is
+    # hardware-dead on sm_86 anyway.
+    [ "$kvdtype" = "turboquant_k8v4" ]
     # The 3090 is dedicated to this service, so the engine may take most of
     # it; the KV pool, not the seq ceiling, caps full-context concurrency.
     [ "$util" = "0.95" ]