frank-small: int8 KV (per-token, per-head) — doubles the pool
fp8 KV is hardware-dead on the 3090 (sm_86, no cvt.e4m3x2), so int8 is the cheapest quant the TRITON_ATTN backend can run: pure ALU dequant, same backend, no fp8 tensor cores needed. 289,647 -> ~579k-token pool, two -> four full-context sessions. Quality verified against the bf16 baseline (needle-in-haystack at 213k + short battery) before the swap.
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@@ -29,15 +29,21 @@
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# for as many concurrent sessions as the card holds.
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# MAX_MODEL_LEN=262144 the model's max_position_embeddings
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# GPU_MEM_UTIL=0.95 the card is dedicated to this service
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# KV_CACHE_DTYPE=auto bf16 KV. fp8_e4m3 would halve the KV charge
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# (289,756 -> 623,666-token pool, one -> two
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# full-context sessions), but the 3090 is
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# sm_86 (Ampere) and has no native fp8: the
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# TRITON_ATTN backend Gemma4 forces emits
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# cvt.e4m3x2 instructions that exist only from
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# sm_89 (Ada), so fp8 KV cannot boot on this
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# card (measured 2026-09-19; the "sm_89
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# patch" experiment is in .AGENTTRASH).
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# KV_CACHE_DTYPE=int8_per_token_head int8 KV (per-token, per-head scales)
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# in the same TRITON_ATTN backend — pure ALU
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# dequant, no fp8 hardware needed, so it runs
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# on sm_86. fp8_e4m3 is the cheaper option but
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# the 3090 is sm_86 (Ampere) and has no native
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# fp8: the TRITON_ATTN backend Gemma4 forces
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# emits cvt.e4m3x2 instructions that exist only
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# from sm_89 (Ada), so fp8 KV cannot boot on
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# this card (measured 2026-09-19; the "sm_89
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# patch" experiment is in .AGENTTRASH). int8
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# halves the KV charge (289,647 -> ~579k-token
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# pool, two -> four full-context sessions) at
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# the cost of a dequant in the attention path;
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# quality verified against the bf16 baseline
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# (needle + short battery) on 2026-09-19.
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# MAX_NUM_SEQS=16 the batch ceiling; the KV pool, not this,
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# caps full-context concurrency
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# MAX_NUM_BATCHED_TOKENS=16384 the prefill chunk; larger chunks prefill
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@@ -71,11 +77,11 @@ ALIAS=frank-small
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BIND_ADDR=0.0.0.0
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LISTEN_PORT=8000
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# Full window, dedicated card, bf16 KV (see above), roomy batch ceiling,
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# Full window, dedicated card, int8 KV (see above), roomy batch ceiling,
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# 16k prefill chunk, 4-token MTP draft (the assistant carries 4 layers).
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MAX_MODEL_LEN=262144
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GPU_MEM_UTIL=0.95
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KV_CACHE_DTYPE=auto
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KV_CACHE_DTYPE=int8_per_token_head
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MAX_NUM_SEQS=16
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MAX_NUM_BATCHED_TOKENS=16384
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SPEC_NUM_TOKENS=4
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@@ -98,7 +98,7 @@ QUADLET_LARGE="$REPO_ROOT/dankclaw/frank-large/quadlet/frank-large.container"
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! grep -q 'HALOGEN_VISION_TOWER' "$QUADLET_LARGE"
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}
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@test "frank-small serves the full window with bf16 KV on a dedicated card" {
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@test "frank-small serves the full window with int8 KV on a dedicated card" {
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local maxlen kvdtype util seqs
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maxlen="$(sed -nE 's/^MAX_MODEL_LEN=([0-9]+)$/\1/p' "$ENV_FILE")"
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kvdtype="$(sed -nE 's/^KV_CACHE_DTYPE=(.+)$/\1/p' "$ENV_FILE")"
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@@ -106,10 +106,12 @@ QUADLET_LARGE="$REPO_ROOT/dankclaw/frank-large/quadlet/frank-large.container"
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seqs="$(sed -nE 's/^MAX_NUM_SEQS=([0-9]+)$/\1/p' "$ENV_FILE")"
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# The model's FULL trained window (262144), not a divided one.
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[ "$maxlen" = "262144" ]
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# bf16 KV: fp8_e4m3 would halve the pool charge, but the 3090 is
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# sm_86 (Ampere) — no native fp8, and the TRITON_ATTN backend that
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# Gemma4 forces emits cvt.e4m3x2 instructions that exist only from sm_89.
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[ "$kvdtype" = "auto" ]
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# int8 KV (per-token, per-head scales): fp8_e4m3 would halve the pool
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# charge further, but the 3090 is sm_86 (Ampere) — no native fp8, and
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# the TRITON_ATTN backend that Gemma4 forces emits cvt.e4m3x2
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# instructions that exist only from sm_89. int8 is pure ALU dequant in
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# the same backend, so it runs.
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[ "$kvdtype" = "int8_per_token_head" ]
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# The 3090 is dedicated to this service, so the engine may take most of
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# it; the KV pool, not the seq ceiling, caps full-context concurrency.
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[ "$util" = "0.95" ]
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