Files
serverAppConfigs/scripts/frank-medium.env
T
Frank d53962fec5 frank-medium: serve the new R9700s, retire frank-small
The RTX 3090 left Dankserver; two AMD Radeon AI PRO R9700 cards (Navi 48,
gfx1201, 32,624 MiB each) replaced it. FreeToken/CUDA cannot run there at all,
so the `frank-small` service is retired and `frank-medium` takes the endpoint.

New service: charlie12345/ROCmFPX llama.cpp (built for gfx1201 against the
fork's self-contained ROCm 7.14 toolchain, so the host's stale /opt/rocm-6.3.3
never participates) serving kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF --
a Q8_0_ROCMFPX_AGENT (ftype 115) checkpoint that stock llama.cpp cannot load.
Placeholder config: --split-mode tensor across both cards, 131072 context,
1 slot, f16 KV, MTP draft head on ROCm0.

deploy-remote.sh grows retire_root_llm_small(): the CUDA-era unit is stopped,
disabled and moved to ~/.AGENTTRASH/20260915-frank-small-retire/host-units
before frank-medium starts, because `systemctl disable` has been observed to
delete the unit file itself on this host (2026-08-26 incident).

The retired frank-small repo files (env, unit, serve script) move to
scripts/.AGENTTRASH/, and the planner, dispatcher, service allow-list and the
deployment test seams now name frank-medium.

Follow-up (same session, measured): llama-bench on the host with the correct
multi-device syntax (-dev ROCm0/ROCm1) overturned the tensor choice:

  single R9700   pp4096 1160.9 / tg128 19.42  (d32768: 512 / 18.4)
  layer  2x      pp4096 1991.2 / tg128 18.76  (d32768: 859 / 17.6)
  tensor 2x       pp4096  271.8 / tg128 26.26 (d32768: 229 / 25.1)

This build has no RCCL, so tensor mode falls back to a "meta-backend
butterfly" allreduce and prefill collapses 7x. Served path with MTP:
layer 35.7-46.2 tok/s vs tensor 28.7. SPLIT_MODE=layer, draft pinned to
ROCm1 (the card holding the last target layers). Served prefix cache with
the draft head verified live: turn 2 reprocessed 22 tokens instead of 949.
2026-09-15 19:03:30 -07:00

139 lines
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# Tunables for frank-medium (kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF,
# charlie12345/ROCmFPX llama.cpp, Dankserver, 2x AMD Radeon AI PRO R9700).
#
# Hardware (2026-09-15): the RTX 3090 left Dankserver and two Radeon AI PRO
# R9700 cards (Navi 48, gfx1201, RDNA4, 32,624 MiB each, PCIe 5.0 x16) took
# its place. The Raphael iGPU (gfx1036) is still enumerated by HIP as the
# third device. There is no XGMI link between the two cards and they sit in
# different IOMMU groups, so any cross-GPU traffic goes over PCIe.
#
# Runtime: charlie12345/ROCmFPX (llama.cpp fork, ROCmFPX quant family; the
# Q8_0_ROCMFPX_AGENT tensor type exists nowhere else - stock llama.cpp cannot
# load this GGUF). Built for gfx1201 with the fork's self-contained ROCm
# 7.14 toolchain so the host's stale /opt/rocm-6.3.3 install is never used:
#
# cd /home/dank/GitPackages/charlie-ROCmFPX
# env JOBS=8 ./scripts/build-rocmfp4-rocm714-local.sh
#
# gfx1201 must be the build target: a gfx1200 binary links, loads the model
# and then segfaults on this card (fork's docs/BUILD-AMD-ARCHITECTURES.md).
#
# Model: kingjones Qwen3.8-27B-ROCmFPX-Q8_0-AGENT - Q8_0_ROCMFPX_AGENT
# (ftype 115), 26.28 GiB, 8.39 bpw, arch qwen35 (MoE), quantized from a
# 51.3 GiB BF16 source. The AGENT routing keeps 12 more attention-output
# projections at full Q8_0 than the plain 8-bit build, which buys a higher
# MTP draft acceptance (0.953 vs 0.911) and that is the whole point of this
# checkpoint. Model card documents 131,072 context.
#
# Parallelism (--split-mode layer, operator call 2026-09-15 "pipeline
# parallelism"): layers are distributed across the two cards in order and
# activations hop PCIe at the boundary. Measured with llama-bench
# (pp4096 / tg128 / same at depth 32768, f16 KV, fa on, 3 reps):
#
# single R9700 1160.9 / 19.42 (fits: 26.27 GiB weights, ~5 GiB left)
# layer 2x 1991.2 / 18.76 859.1 / 17.64
# tensor 2x 271.8 / 26.26 228.6 / 25.09
#
# Tensor parallel wins decode (+40 %) but collapses prefill 7x: this build
# has no RCCL (llama.cpp logs "AllReduce init failed; falling back to
# meta-backend butterfly"), so every layer pays a slow cross-card reduction.
# Agentic traffic is prefill-heavy, so layer split wins the workload. Revisit
# if the fork ships a working RCCL tensor path.
#
# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE. The
# ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
# incident). llama.cpp reads --host/--port/--temp as CLI flags, never as
# env vars.
# charlie12345/ROCmFPX llama-server (self-contained ROCm 7.14 build, RPATH
# points at the bundled runtime libs; no LD_LIBRARY_PATH needed).
LLAMA_BIN=/home/dank/GitPackages/charlie-ROCmFPX/build-rdna4-rocm714/bin/llama-server
# Model package, fetched with scripts/download-q8agent.sh:
# Qwen3.8-27B-Q8_0_ROCMFPX_AGENT.gguf 26.28 GiB main weights
# mtp-Qwen3.8-27B-Q4_0.gguf 1.68 GiB MTP draft head
# mmproj-Qwen3.8-27B-BF16.gguf 0.93 GiB vision tower (unused)
MODEL_DIR=/home/dank/models/kingjones-Qwen3.8-27B-ROCMFPX-Q8_0-AGENT
MODEL_FILE=Qwen3.8-27B-Q8_0_ROCMFPX_AGENT.gguf
DRAFT_MODEL_FILE=mtp-Qwen3.8-27B-Q4_0.gguf
# Alias + network. The port is the fleet's dankserver endpoint; the alias is
# what clients ask for, and it replaces the retired `frank-small`.
ALIAS=frank-medium
BIND_ADDR=0.0.0.0
LISTEN_PORT=7770
# Device placement. HIP_VISIBLE_DEVICES hides the iGPU (device 2) so nothing
# can drift onto shared system RAM; DEVICES is what llama.cpp splits across.
HIP_VISIBLE_DEVICES=0,1
DEVICES=ROCm0,ROCm1
SPLIT_MODE=layer
# TENSOR_SPLIT empty = layers distributed by free VRAM (equal here).
TENSOR_SPLIT=
NGL=999
# Auto-fit has no business resizing a context we set explicitly.
FIT=off
FLASH_ATTN=on
# 131,072 tokens, one slot (the model card's documented window; the GGUF
# reports n_ctx_train 262144, untested by the model author). Layer split
# keeps each layer's KV on the card that owns the layer, so the window is
# bounded by per-card free memory: ~5.6 GiB of KV per card at 131072 f16.
CTX_SIZE=131072
PARALLEL=1
# 26.28 GiB of weights + MTP draft + KV cache leaves ~15 GiB per card at this
# context, so the batch shape is chosen for prefill throughput, not memory.
BATCH_SIZE=2048
UBATCH_SIZE=512
NUM_THREADS=8
THREAD_BATCH=8
# f16 K/V: an agent checkpoint is the wrong place to spend KV precision.
CACHE_TYPE_K=f16
CACHE_TYPE_V=f16
CTX_CHECKPOINTS=8
CACHE_RAM=8192
# Sampling: the checkpoint's instruct table.
TEMPERATURE=0.7
TOP_P=0.8
TOP_K=20
MIN_P=0.0
PRESENCE_PENALTY=1.5
# Reasoning on; DeepSeek format projects the trace into reasoning_content
# instead of literal thinking tags in content.
REASONING=on
REASONING_FORMAT=deepseek
REASONING_BUDGET=-1
# Chat template: empty = the template embedded in the GGUF. Point this at
# /home/dank/serverAppConfigs/scripts/chat-template.jinja to force the repo's
# Sharp agent template instead.
CHAT_TEMPLATE_FILE=
CHAT_TEMPLATE_KWARGS='{"enable_thinking":true,"preserve_thinking":true}'
# MTP speculative decoding. n_max 6 / p_min 0.6 is the fork's measured
# sweet spot for a MTP head with high acceptance; p_min 0.0 drafts
# low-probability tokens that get rejected and makes MTP a net loss.
SPEC_TYPE=draft-mtp
SPEC_DRAFT_N_MAX=6
SPEC_DRAFT_N_MIN=0
SPEC_DRAFT_P_MIN=0.6
SPEC_DRAFT_P_SPLIT=0.10
SPEC_DRAFT_NGL=999
SPEC_DRAFT_TYPE_K=f16
SPEC_DRAFT_TYPE_V=f16
SPEC_DRAFT_POLL=1
SPEC_DRAFT_POLL_BATCH=1
# The draft head rides the card that holds the target model's LAST layers:
# the MTP head consumes the final hidden state, so pinning it to ROCm1 keeps
# the per-token hop off the wire. (Under the abandoned tensor split the
# draft must also be pinned to exactly one device or the meta-device KV
# allocation aborts: GGML_ASSERT(buffer) in ggml-backend.cpp, measured
# 2026-09-15.)
SPEC_DRAFT_DEVICE=ROCm1