serverAppConfigs/scripts/frank-medium.env

# Tunables for frank-medium (ajgazin/Swift-Qwen3.8-27B-Uncensored-Dynamic-MTP
# UD-Q8_K_XL + z-lab/Qwen3.8-27B-DFlash2-GGUF Q8_0 drafter), mainline llama.cpp
# on Dankserver, 2x AMD Radeon AI PRO R9700 (gfx1201).  Since 2026-09-16 this
# is the SOLE LLM runtime on the box: the vllm-radiance replica pair and its
# routing proxy are retired.
#
# Hardware: two R9700 (Navi 48, RDNA4, 32,624 MiB each), no XGMI, different
# IOMMU groups -- every cross-card byte goes over PCIe.  The Raphael iGPU
# (gfx1036/Radeon 610M) enumerates as HIP device 2 and is hidden below so
# nothing can drift onto shared system RAM.
#
# WHY LAYER SPLIT AND NOT TENSOR (operator call, 2026-09-16): tensor parallel
# needs a cross-card allreduce per layer, and this host cannot dispatch one.
# RCCL's kernels carry a hidden hostcall-buffer parameter, and ROCr refuses it
# on a device without PCIe atomics -- the second card sits behind the B650
# chipset bridges (AtomicOpsCap: Routing-, read-only), so every collective that
# touches it dies with hipErrorIllegalState.  Measured on this box 2026-09-15
# (llama-bench, pp4096 / tg128, f16 KV, fa on):
#
#   single R9700    1160.9 / 19.42
#   layer  2x       1991.2 / 18.76      (859.1 / 17.64 at depth 32768)
#   tensor 2x        271.8 / 26.26      (no RCCL: butterfly fallback, -7x prefill)
#
# Layer split distributes layers in order; only the activation at the boundary
# crosses the wire, once per forward pass.  That is the "pipeline parallelism"
# this service wanted, and it needs no collective at all.
#
# Runtime: mainline llama.cpp master fb27a525 (2026-09-16), gfx1201, built by
# scripts/build-llama-rdna4.sh against a private ROCm 7.14.0a20260624 toolchain
# that is bundled next to the binary (RPATH $ORIGIN/../lib, no LD_LIBRARY_PATH).
# The ref is pinned by TWO features: DFlash2 (ggml-org/llama.cpp#27342 -- the
# drafter is detected from its GGUF) and qwen35 MTP.  Do not float it.
#
# Model: ajgazin/Swift-Qwen3.8-27B-Uncensored-Dynamic-MTP-GGUF @ UD-Q8_K_XL,
# 29.29 GiB, arch qwen35, Unsloth Dynamic 3.0 (per-tensor types from Unsloth's
# own quant of the same size, on Unsloth's importance matrix).  Abliteration is
# orcarouter's single refusal direction projected out of 131 tensors; the vision
# tower loads from mmproj-BF16.gguf (see NO_MMPROJ).  The file also
# carries the model's MTP head (blk.64.nextn.*), so a file-level switch to
# --spec-type draft-mtp is available without a second copy in VRAM.
#
# Drafter: z-lab/Qwen3.8-27B-DFlash2-GGUF @ Q8_0, 1.92 GiB: 5 layers, hidden
# 5120, a block-diffusion proposal (block_size 8) under a 2048-token sliding
# window.  Decoding stays lossless -- the target verifies every drafted token.
# The window is what makes it cheap HERE: its KV is bounded at 2048 positions
# (~40 MiB) instead of spanning the context (see SIZING).
#
# Architecture (GGUF arch qwen35): 64 layers as 16 x (3 x (Gated DeltaNet ->
# FFN) + 1 x (Gated Attention -> FFN)) -- 48 linear-attention layers whose
# per-sequence state is CONSTANT-SIZE, 16 full-attention layers at 4 KV heads x
# 256 head_dim.  A token therefore costs 2 x 4 x 256 x 2 B = 64 KiB of KV at f16
# in the attention layers only, plus ~0.16 GiB per sequence of f32 DeltaNet
# state at any depth.
#
# SIZING, measured (rocm-smi, 2026-09-16).  Weights split across the two cards;
# the drafter AND its KV ride the second.  The loader's compute buffers are part
# of what is seen below -- they are allocated at load, not per request, and they
# scale with --ubatch-size:
#
#   CTX     split  batch  slots  drafter  GPU 0 free  GPU 1 free   note (ubatch 1536)
#   262144   1,1   2048   1      DFlash2   8.64 GiB    4.43 GiB   day's start
#   262144  39,26  6144   2      DFlash2   2.42 GiB    7.20 GiB
#   262144  38,27  6144   2      DFlash2   3.85 GiB    5.77 GiB
#   262144  37,28  6144   2      DFlash2   4.28 GiB    5.34 GiB
#   262144  36,29  6144   2      DFlash2   4.71 GiB    4.91 GiB
#   262144  35,30  6144   2      DFlash2   5.14 GiB    4.48 GiB
#   262144  40,25  6144   2      DSpark    1.87 GiB    1.86 GiB   (retired)
#
# ...and the same deployed shape re-measured at ubatch 512, after llama-bench
# showed 512 is the throughput optimum and costs 3.71 GiB less in total (see the
# batch block below):
#
#   262144  36,29  6144   512    2      DFlash2   6.01 GiB    7.32 GiB   ubatch only
#   262144  35,30  6144   512    2      DFlash2   6.44 GiB    6.90 GiB   <- deployed
#
# ONE -ts STEP IS ONE LAYER, AND LAYERS ARE NOT THE SAME SIZE.  This is a hybrid
# stack -- 16 x (3 Gated DeltaNet + 1 Gated Attention) blocks -- so moving a
# DeltaNet layer shifts ~0.43 GiB while an attention layer shifts ~1.43 GiB
# (0.45 of weights plus ~1.0 GiB of its own f16 KV at 262144).  Both step sizes
# were measured, which is why the balance is found by measurement rather than by
# interpolating between splits.
#
# 262144 is the window the model ships (n_ctx_train) and it fits with room, so
# the fleet gets the whole thing; the arithmetic that used to cap this endpoint
# at 114688 was the vLLM predecessor's, not this runtime's.  If a future change
# adds VRAM (a longer drafter, quantized KV, more slots) re-read the numbers
# rather than assuming, and prefer CTX_SIZE over --gpu-layers.
#
# WHY THE SPLIT IS 35:30.  Three effects decide it, and all three are measured:
#   * THE DRAFTER AND ITS OUTPUT LAYERS RIDE ROCm1.  It carries the drafter's
#     weights, the draft graph and the last target layers, so it takes fewer
#     than half the layer count.
#   * THE UBATCH BUFFERS COST ROCm1 MORE THAN ROCm0, and shrinking them pays
#     ROCm1 back more: 1536 -> 512 freed 1.30 GiB on ROCm0 but 2.41 GiB on
#     ROCm1 at this shape.  Read the other way, that is exactly why the smaller
#     batch moved the balance toward ROCm1 -- and cost ROCm0 one layer.
#   * THE LAYER GRANULARITY IS THE FLOOR ON HOW WELL ANY SPLIT CAN BALANCE.  One
#     -ts step is one layer -- ~0.43 GiB DeltaNet, ~1.43 GiB attention -- so the
#     reachable minimum gap is comparable to one step rather than to zero.  35:30
#     sits at 0.46 GiB, i.e. one DeltaNet layer's worth, against 1.31 GiB at
#     36:29.
# It also leaves the most room: 6.44 / 6.90 GiB free clears the operator's 1.5
# GiB-per-card floor by ~5 GiB, so no headroom has to be bought out of CTX_SIZE
# and the full window stays.
#
# A FAULT TO WATCH.  A 39:26 split, at 1x batch / 1 slot, died on its eighth
# request: the amdgpu driver logged SDMA0 UTCL2 page faults and the engine
# surfaced `ROCm error: an illegal memory access was encountered` -> ggml_abort,
# i.e. a fault in the cross-card boundary copy, this layer split's hot path.  It
# has not reproduced (36:29 served this file's verification battery and the e2e
# gate, and 35:30 is the deployed shape), but the signature is worth recognising
# -- it is NOT an OOM, and no amount of headroom prevents it.
#
# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE (HIP reads
# those bare vars; 2026-08-13 incident).  llama.cpp takes --host/--port/--temp
# as flags only.

# llama-server: mainline fb27a525 built for gfx1201 by scripts/build-llama-rdna4.sh.
LLAMA_BIN=/home/dank/GitPackages/llama.cpp-dflash2/build-rdna4-rocm714/bin/llama-server

# Payload, fetched with scripts/download-frank-medium-gguf.sh.
MODEL_DIR=/home/dank/models/Swift-Qwen3.8-27B-Uncensored-Dynamic-MTP-GGUF
MODEL_FILE=Swift-Qwen3.8-27B-Uncensored-Dynamic-MTP-UD-Q8_K_XL.gguf
# The BF16 vision projector, same repo as the target.  llama-server autoloads
# any mmproj*.gguf beside the model; the serve script refuses to start without
# it when vision is enabled, so a missing file is a deploy error, not a
# text-only server that pretends to be multimodal.
MM_PROJ_FILE=mmproj-BF16.gguf
DRAFT_MODEL_DIR=/home/dank/models/Qwen3.8-27B-DFlash2-GGUF
DRAFT_MODEL_FILE=Qwen3.8-27B-DFlash2-Q8_0.gguf

# Fleet identity.  7770 is the fleet's dankserver endpoint; the alias is the id
# at /v1/models and what every client asks for.
ALIAS=frank-medium
BIND_ADDR=0.0.0.0
LISTEN_PORT=7770
# Device placement.  HIP_VISIBLE_DEVICES hides the Raphael iGPU (HIP device 2)
# so nothing can drift onto shared system RAM; DEVICES is what llama.cpp splits
# across, and neither of the two cards has an XGMI link to the other.
HIP_VISIBLE_DEVICES=0,1
DEVICES=ROCm0,ROCm1
SPLIT_MODE=layer

# 36:29 -- one layer over the 35:30 balance, moved to buy headroom on ROCm1,
# the card that carries the drafter, its KV, the output layers and the heavier
# compute buffers, now that the context grew to 327680 (see below).  Fit at
# the deployed point (36:29, ctx 327680, batch 6144 x ubatch 512, two slots,
# DFlash2 + ngram-mod, mmproj on ROCm0): ROCm0 29.19 / ROCm1 26.04 GiB of
# 31.86, i.e. 2.67 / 5.82 GiB free and a 3.15 GiB gap BY CONSTRUCTION -- the
# balance point at this context is 34:31 (0.11 GiB gap) and the operator chose
# headroom on the drafter card over balance.  See SIZING above for the
# measurement table; one -ts step is one LAYER, worth ~0.43 GiB (DeltaNet) or
# ~1.43 GiB (attention), so do not move it without re-measuring.
TENSOR_SPLIT=36,29

NGL=999
# Auto-fit has no business resizing a context we set explicitly.
FIT=off
FLASH_ATTN=on

# 327680 positions over TWO slots, so each conversation gets 163840 --
# --parallel DIVIDES --ctx-size, so more slots would shrink every
# conversation rather than grow the cache.  The checkpoint's trained window is
# 262144 and the GGUF carries NO rope scaling (freq_base 1e7, no YaRN keys),
# so positions beyond 262144 extrapolate on the base rope: it runs, and 1.25x
# is a modest ask of it, but the extra 65k is untrained territory.
CTX_SIZE=327680
PARALLEL=2

# MEASURED 2026-09-16 with scripts/bench-batch-ubatch.sh on this box: llama-bench,
# a 64k prefill plus a 128-token decode with 64k of KV already resident, 3 reps,
# one invocation per cell, the deployed split and f16 KV.  --ubatch-size is the
# physical batch the graph is built at -- and the only one of the two knobs that
# costs VRAM:
#
#   batch  ubatch   prefill tok/s        GPU 0 / GPU 1 free
#   6144   1536    1164.96 +- 2.30      10.28 / 11.71 GiB
#   6144    768    1135.03 +- 1.09      11.29 / 13.28 GiB
#   6144    512    1192.93 +- 0.91      11.63 / 13.81 GiB   <- deployed
#   6144    384    1068.04 +- 0.79      11.80 / 14.07 GiB
#   6144    256    1045.98 +- 1.69      11.97 / 14.33 GiB
#   4096   1536    1161.90 +- 0.96      10.28 / 11.71 GiB
#   2048   1536    1170.23 +- 0.86      10.28 / 11.71 GiB
#   2048    512    1193.88 +- 1.01      11.63 / 13.81 GiB
#
# THE PLATEAU STARTS AT 512.  512 -> 1536 buys ~2 % of prefill -- inside the
# run-to-run spread, and the 512 cells were the faster pair in BOTH batch
# columns -- while costing 1.35 GiB on ROCm0 and 2.10 GiB on ROCm1, so the
# deployment takes the cheap end.  BELOW 512 the SMs are no longer saturated:
# 384 and 256 fall ~11 % and ~12 %.  The logical batch is free either way --
# 2048, 4096 and 6144 land within 2 % of each other at a fixed ubatch and use
# the SAME VRAM, because the compute buffers are sized by ubatch alone.
#
# The prefill figure is a property of the SHAPE, not of this knob: the same
# sweep with no depth in front of it (depth 0) runs 1543 tok/s at ubatch 256 and
# 1723 at 1536 -- the plateau is in the same place, and the ~32 % between the two
# tables is what attending over 64k of resident KV costs.  Decode is 15.88 tok/s
# at depth 65536 on EVERY ubatch (17.80 at depth 0), so this knob cannot touch
# generation at all.
#
# 8192/2048 stays untested under this drafter (the "2020 MiB pp buffer fails on
# ROCm1" note was measured under DSpark, when it had 0.08 GiB free), and there is
# no longer a reason to want it: batch buys nothing measurable here.
BATCH_SIZE=6144
UBATCH_SIZE=512

# Threads cover tokenization, sampling and HTTP only: every layer is on a GPU.
NUM_THREADS=8
THREAD_BATCH=8

# f16 K/V: an agentic checkpoint is the wrong place to spend KV precision.
CACHE_TYPE_K=f16
CACHE_TYPE_V=f16
# Context checkpoints: 0 is the build's default -- no per-slot KV snapshots, so
# a re-sent or rewound conversation re-prefills instead of rewinding.  Stated
# explicitly so the tunable stays visible rather than merely absent.
CTX_CHECKPOINTS=0
CACHE_RAM=8192

# Sampling defaults for requests that send none -- the values the vLLM
# predecessor served on this endpoint (`--override-generation-config`), so a
# client sees no change across the runtime swap.  The model card's own
# recommendation is temp 1.0 / top_p 0.95; the harness sets its own per model in
# models.yml, so this is the floor, not the contract.
TEMPERATURE=0.7
TOP_P=0.95
TOP_K=20
MIN_P=0.0
PRESENCE_PENALTY=0.0

# Reasoning on, projected into reasoning_content (deepseek format) with no
# budget: the model decides how long to think.
REASONING=on
REASONING_FORMAT=deepseek
REASONING_BUDGET=-1

# Chat template: empty = the one embedded in the GGUF (Qwen3.8's own).
CHAT_TEMPLATE_FILE=
CHAT_TEMPLATE_KWARGS='{"enable_thinking":true,"preserve_thinking":true}'

# Speculative decoding: the DFlash2 block-diffusion drafter PLUS the draftless
# n-gram matcher.  DFlash2's GGUF carries block_size 8, so at most 7 draft
# tokens per verification step (llama.cpp asserts n_max <= block_size - 1).
# The drafter rides ROCm1, the card that ends up holding the target's last
# layers.
SPEC_TYPE=draft-dflash,ngram-mod
SPEC_DRAFT_N_MAX=7
SPEC_DRAFT_NGL=999
SPEC_DRAFT_DEVICE=ROCm1
SPEC_DRAFT_TYPE_K=f16
SPEC_DRAFT_TYPE_V=f16

# MTP (the model's own head) vs the drafter -- MEASURED 2026-09-16 (temperature
# 0, 1k prompt / 512 out; the autoregressive baseline and both MTP depths were
# run against the deployed configuration in one session, same binary, same
# prompts):
#
#   none            17.7 tok/s                     card1 26.07 GB
#   draft-mtp  n=3  32.4 tok/s  58 % accepted       card1 29.35 GB
#   draft-mtp  n=7  26.7 tok/s  33 % accepted       card1 29.65 GB
#   draft-dflash 7  43.5 tok/s  41 % accepted       card1 29.42 GB
#
# All four produced IDENTICAL greedy text, so verification is lossless whichever
# proposer runs.  DFlash2 is worth 2.46x over autoregressive and 34 % over the
# best MTP; deeper MTP drafting costs more than it returns (n=7 accepts 33 % of
# 147 proposals, n=3 accepts 58 % of 72).  The MTP head is not free either:
# selecting it loads blk.64.nextn.* and adds ~3.3 GB to the card that holds it,
# against 2.06 GB for the drafter's file.  Do not switch this without
# re-measuring.
#
# THE TWO CANNOT BE CHAINED. `--spec-type draft-mtp,draft-dflash` dies at load:
#   context type MTP requested but model doesn't contain MTP layers
#   failed to create MTP context
# A chain has ONE draft context, and MTP builds against whatever model that
# context holds -- here the DFlash2 file, which has no MTP layers. MTP's head is
# only loaded when MTP IS the draft model ("creating MTP draft context against
# the target model ..."), so a chain of the two is a structural dead end, not a
# tuning question. See serverAppConfigs-docs/LLM/frank-medium-llamacpp-dflash2.md.

# ngram-mod: the draftless n-gram matcher (CPU hash table in host RAM, no VRAM,
# no model file, no draft context). It joins the chain unconditionally --
# unlike MTP, which needs a draft context and therefore cannot share one with
# DFlash2. Verified on the loaded server: with ngram-mod as the ONLY spec type,
# a prose prompt produces ZERO drafts (plain autoregressive) while a
# prompt-echo prompt accepts 100 % of them, so its drafts are unmistakable.
#
# WINDOW MEASURED 2026-09-16 (chain at each window, decode tok/s):
#
#   n_min/n_max    prose 1k/512   prompt-echo 1.5k/400
#   48/64          43.9           200.6     <- deployed
#   16/32          36.0           161.0
#    8/16          36.2           113.8
#
# The long window is the right one, and not only because it wins on echo: a
# SHORT window makes the matcher propose on short fragments of ordinary prose,
# where the proposals are rejected and the verification still costs -- which is
# the "ngram hurts" result the fleet measured on other payloads twice before.
# With 48/64 the chain is byte-identical to DFlash2 alone on prose (same 119
# drafts / 49 accepted) and on the identity prompt (70/53).
#
# Do not shorten these without re-running scripts/bench-spec-ab.py: the
# no-penalty property is what makes the chain safe as a default.
SPEC_NGRAM_N_MATCH=24
SPEC_NGRAM_N_MIN=48
SPEC_NGRAM_N_MAX=64

# Vision enabled: llama-server autoloads mmproj-BF16.gguf from MODEL_DIR
# (888 MiB, lands on ROCm0 -- the fit above charges it there).  frank-small
# (Dankclaw, RTX 3090) remains the fleet's dedicated image endpoint; this
# makes frank-medium multimodal too, which is why the seam flips from
# "text only" to "vision loads with the model".
NO_MMPROJ=0