# frank-small — llama.cpp (master, CUDA sm_86) serving
# ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 on the RTX 3090, :7771.
#
# 2026-10-03 (Frank, second move): the rung went Q5_K_M -> Q6_K with the pool
# HELD at 401,408. The freed third slot funded exact f16 V first; the ~3.5 GiB
# still free funds the +875 MiB of weights (file delta 7,793,710,624 -
# 6,876,124,704). Projected landing ~2.7 GiB free — inside the 2-3 GiB floor,
# so MEASURE after the boot; if it lands under 2 GiB, step the pool down one
# 12,288-token (384 MiB) increment.
#
# 2026-10-03 (Frank): TWO streams instead of three, each one WIDE — 200,704
# tokens per conversation, above the 200,700 the harness compacts at — and the
# KV cache is now FULLY UNQUANTIZED (f16 K, f16 V). The two moves are the same
# move: llama.cpp divides --ctx-size across --parallel slots, so the slot given
# up is exactly the VRAM that pays for unquantized V. No further quantization
# anywhere on this endpoint.
#
# Three measurements on this card, this runtime, price the terms:
#   2 x 262,144, no projector: 20,190 MiB used / 4,386 free
#     KV = 524,288 tok x 24.5 KiB = 12,800 MiB => non-KV (weights 6,557 +
#     DeltaNet state ~25/slot + compute buffers + CUDA graphs) = 7,390 MiB
#   3 x 196,608, projector on: 22,972 MiB used / 1,153 free
#     KV = 14,113 MiB => non-KV WITH the vision tower = 8,859 MiB
#   3 x 180,224, projector on: 21,684 MiB used / 2,441 free (the 2026-10-02
#     shape) => non-KV = 8,748 MiB
# Two slots give back one DeltaNet state (~25 MiB). Priced at f16 K+V
# (32 KiB/token) this pool is 12,544 MiB, so the prediction is
# 12,544 + ~8,800 = ~20.9 GiB used, ~3.1 GiB free — the band the 2026-10-02
# shape hit, bought with precision instead of a slot. The 1024 ubatch
# re-prices the compute buffers upward, so MEASURE after any change: if the
# boot lands under 2 GiB free, step the pool down in 12,288-token
# (384 MiB at this rate) increments.
#
# KV arithmetic: attention KV exists only in the 8 full-attention layers
# (4 KV heads x 256 head_dim x 2 B) = 16 KiB/token for K and the same again for
# V => 32 KiB/token, so 12,288 tokens (= 6144/slot) is one 384 MiB step — that
# is the ladder to walk if this moves again. Two native windows (2 x 262,144)
# are NOT reachable with full-precision V: 524,288 x 32 KiB = 16.1 GiB of KV
# against a card with ~15.7 GiB of KV room once weights, the vision tower, the
# DeltaNet states and the graphs are loaded. 200,704 per stream is what full
# precision buys; q8_0 on V would buy 262,144 instead.
#
# The asymmetric-KV contract this endpoint was created for (Frank, 2026-10-01
# night: f16 K + q8_0 V) is now spent — it existed to make a third slot fit,
# and the third slot is gone. The flags stay explicit so the choice is visible
# in the argv either way.
LLAMA_BIN=/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server
MODEL_FILE=/home/dank/models/Ornith-Q8/Ornith-1.5-9B-Q8_0.gguf

# Vision: the Q6_K GGUF ships no `v.*` tensors either, so the projector file
# IS the vision capability (sha256 ff348f31..., 918,166,048 B, fetched by
# scripts/download-frank-small.sh; hardlinked into this dir from MiMo-Q5KM/,
# same bytes — the Q6_K dir is self-contained). Empty string = text-only on
# purpose.
MMPROJ_FILE=/home/dank/models/Ornith-Q8/mmproj-Ornith-1.5-9B-BF16.gguf

# Fleet endpoint: same port and alias as every frank-small before it, so the
# harness models.yml id and the homepage probe do not move.
ALIAS=frank-small
BIND_ADDR=0.0.0.0
LISTEN_PORT=7771

# Two independent conversations. llama.cpp divides --ctx-size across
# --parallel slots (n_ctx_slot = CTX_SIZE/PARALLEL), and a mamba-hybrid model
# cannot use unified KV, so each slot gets its own cache and its own window.
# 200,704 per slot is BELOW the native 262,144 — the price of paying for the
# window in full precision — and ABOVE the harness's compaction trigger, which
# is the point: a full-length request now lands in one stream instead of a 400
# exceed_context_size_error. Cost of the third slot going away: a third
# concurrent request queues behind a released slot instead of running.
CTX_SIZE=401408
PARALLEL=2

# Compute-buffer geometry. This endpoint passed neither flag until 2026-10-03,
# so it ran on llama.cpp's own defaults (2048/512); the pair is now 4096/2048.
# Both are tunables because the pair is a measurement, not a law — and the
# measurement is CARD-SPECIFIC. On the 2x R9700 layer-split sibling the ubatch
# curve peaks at 512 and every step up costs GiB of compute buffer for no
# speed (see frank-medium-batch-ubatch). On THIS CUDA 3090 the curve climbs
# monotonically to 2048: 710 -> 1188 t/s prefill from ubatch 1024 to 2048 on
# the 35B MoE sweep, 1944 -> 2423 t/s on the Ornith 8k sweep, with decode flat
# to under 1 t/s on every rung. Ubatch is a prefill knob; decode never moves.
#
# The logical batch is free — two independent sweeps agree that at a fixed
# ubatch, 2048/4096/8192 give identical prefill speed AND identical peak VRAM,
# because the compute buffers are sized by the ubatch alone. Doubling it costs
# nothing and buys nothing; it is here because it is the value the pair was
# measured at.
#
# THE RISK IS ALL IN THE UBATCH. The 1024 -> 2048 step cost ~1,244 MiB of peak
# graph VRAM on the 35B MoE (7,330 -> 8,574 MiB); this 9B hybrid has far
# smaller compute tensors, so the delta should be well under that, but the
# predecessor Q5_K_M shape recorded "ubatch 2048 failed the scratch boot" at
# ~3,571 MiB free. This shape ships with LESS free (2,815 MiB measured
# 2026-10-05). Compute buffers are allocated at graph capture during BOOT, so
# a failure is loud and fast, not a surprise at the first long prompt. MEASURE
# nvidia-smi free immediately after the deploy: under ~1.5 GiB, or a boot
# failure, step down to 4096/1536 (or 4096/1024, the last known-good rung).
BATCH_SIZE=4096
UBATCH_SIZE=2048

# The KV contract (Frank, 2026-10-03): unquantized K AND V. --flash-attn stays
# pinned on in the serve script — it is what made the q8_0-V path cheap and it
# remains the fast path for f16 KV.
CACHE_TYPE_K=f16
CACHE_TYPE_V=f16
NGL=99