Frank
c2116658f8
The conversation battery (scripts/bench-conversation.py, salted, same 10
prompts, 2026-09-20) measured three arms on the two R9700s:
arm I llama.cpp layer split 32:33 + DFlash2, Q8_0 geo decode 45.7 t/s, TTFT 0.72 s, burst-4 71.7 t/s
arm L llama.cpp tensor split 1:1 (RCCL), no drafter geo decode 30.5 t/s, TTFT 0.71 s, burst-4 54.3 t/s
arm R radiance vLLM TP=2 + DFlash2-FP8, MXFP4 geo decode 95.8 t/s, TTFT 0.21 s, burst-4 259.4 t/s
Radiance wins every axis, so it takes the alias. The board swap is what made
arm R possible: TP=2 was archived on 2026-09-16 because the second card sat
behind B650 chipset bridges with AtomicOpsCap: Routing-, and the ProArt
B850-Creator put both cards on CPU GPP root ports (Gen5 x8, Routing+, ReqEn+).
The R4D one-shot all-reduce needs no RCCL collective anyway.
- un-retire dankserver/frank-medium/: the TP=2 quadlet (banner rewritten —
it is the deployed shape now), radiance-entrypoint.sh, the healthcheck.
The replica pair and its affinity proxy stay archived; one process owns
both cards now.
- retire scripts/frank-medium.{service,env} + serve-frank-medium.sh and
bench-spec-ab.py to scripts/.AGENTTRASH/20260920-llama-layersplit/ as the
rollback path. build-llama-rdna4.sh and download-frank-medium-gguf.sh
stay: they are the tooling that rollback needs.
- deploy-remote.sh: deploy_llm_medium now gates on the image (pull once,
re-tag to the localhost name the quadlet resolves), the MXFP4 checkpoint,
the drafter, the pinned patch clone and the patched libr4d; retires the
llama root unit into the home trash instead of relinking it; then
install_quadlet + wait_container_healthy 1800s (first start compiles).
retire_vllm_medium_quadlets becomes retire_vllm_replica_quadlets — the
a/b/proxy trio only, since frank-medium is installed again.
- planner + test-changed map dankserver/frank-medium/** to frank-medium
and its seam; the scripts-trio mapping is gone with the files.
- tests/frank_medium_unit.bats rewritten for the container contract: boot
persistence of a GENERATED unit (no enable on this host's v1 generator),
TP=2 with the atomics-era NCCL workarounds kept out, the DFlash2 depth vs
the MXFP4 decode-kernel row band, the KV pin's coupling to the shape it
was calibrated at plus pool/VRAM arithmetic, the endpoint/tool/reasoning
contract, and the archive invariants.
- homepage card + docs updated, and they were stale twice over: they still
described the ajgazin Swift 35:30 / 393k payload, not the deployed shape.
Known regression, documented in the unit: vision. The MXFP4 checkpoint is
language_model_only and the launcher loads no projector, so images are a 400
here; the harness's vision role already routes to frank-small.
319 lines
17 KiB
Bash
319 lines
17 KiB
Bash
# Tunables for frank-medium (orcarouter/Qwen3.8-27B-Uncensored-GGUF Q8_0 +
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# z-lab/Qwen3.8-27B-DFlash2-GGUF Q4_K_M drafter), mainline llama.cpp on
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# Dankserver, 2x AMD Radeon AI PRO R9700 (gfx1201). Since 2026-09-16 this is
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# the SOLE LLM runtime on the box: the vllm-radiance replica pair and its
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# routing proxy are retired.
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#
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# Hardware: two R9700 (Navi 48, RDNA4, 32,624 MiB each), no XGMI, different
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# IOMMU groups -- every cross-card byte goes over PCIe. The Raphael iGPU
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# (gfx1036/Radeon 610M) enumerates as HIP device 2 and is hidden below so
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# nothing can drift onto shared system RAM.
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#
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# WHY LAYER SPLIT AND NOT TENSOR (operator call, 2026-09-16): tensor parallel
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# needs a cross-card allreduce per layer, and this host cannot dispatch one.
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# RCCL's kernels carry a hidden hostcall-buffer parameter, and ROCr refuses it
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# on a device without PCIe atomics -- the second card sits behind the B650
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# chipset bridges (AtomicOpsCap: Routing-, read-only), so every collective that
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# touches it dies with hipErrorIllegalState. Measured on this box 2026-09-15
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# (llama-bench, pp4096 / tg128, f16 KV, fa on):
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#
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# single R9700 1160.9 / 19.42
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# layer 2x 1991.2 / 18.76 (859.1 / 17.64 at depth 32768)
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# tensor 2x 271.8 / 26.26 (no RCCL: butterfly fallback, -7x prefill)
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#
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# Layer split distributes layers in order; only the activation at the boundary
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# crosses the wire, once per forward pass. That is the "pipeline parallelism"
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# this service wanted, and it needs no collective at all.
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#
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# Runtime: mainline llama.cpp master fb27a525 (2026-09-16), gfx1201, built by
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# scripts/build-llama-rdna4.sh against a private ROCm 7.14.0a20260624 toolchain
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# that is bundled next to the binary (RPATH $ORIGIN/../lib, no LD_LIBRARY_PATH).
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# The ref is pinned by TWO features: DFlash2 (ggml-org/llama.cpp#27342 -- the
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# drafter is detected from its GGUF) and qwen35 MTP. Do not float it.
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#
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# Model: orcarouter/Qwen3.8-27B-Uncensored-GGUF @ Q8_0, 27.0 GiB, arch qwen35
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# (verified from the GGUF header 2026-09-19: 262144 trained window, 24 heads /
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# 4 KV heads, nextn_predict_layers=1). The file ALSO carries the model's MTP
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# head (blk.64.nextn.*), so --spec-type draft-mtp is available without a second
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# copy in VRAM -- one SPEC_TYPE change, the drafter below stays on disk.
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#
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# Drafter: z-lab/Qwen3.8-27B-DFlash2-GGUF @ Q4_K_M, 1.06 GiB (operator call
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# 2026-09-19: the Q4 rung -- the drafter's job is throughput, not output
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# quality, and the target verifies every drafted token). 5 layers, hidden
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# 5120, a block-diffusion proposal (block_size 8) under a 2048-token sliding
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# window (dflash.attention.sliding_window=2048, verified from the header).
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# The window is what makes it cheap HERE: its KV is bounded at 2048 positions
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# (~40 MiB) instead of spanning the context (see SIZING).
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#
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# Architecture (GGUF arch qwen35): 64 layers as 16 x (3 x (Gated DeltaNet ->
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# FFN) + 1 x (Gated Attention -> FFN)) -- 48 linear-attention layers whose
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# per-sequence state is CONSTANT-SIZE, 16 full-attention layers at 4 KV heads x
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# 256 head_dim. A token therefore costs 2 x 4 x 256 x 2 B = 64 KiB of KV at f16
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# in the attention layers only, plus ~0.16 GiB per sequence of f32 DeltaNet
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# state at any depth.
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#
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# SIZING, the deployed shape (32:33 / ctx 327680 / 6144x512 / two slots /
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# DFlash2 Q4_K_M), measured 2026-09-19 after the model swap (rocm-smi):
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#
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# card 0 (x16) 26.9 GiB used, 5.7 GiB free
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# card 1 (x1) 27.8 GiB used, 4.8 GiB free
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#
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# The predecessor's measured table (rocm-smi, 2026-09-16, UD-Q8_K_XL target)
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# stays as the calibration history:
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#
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# CTX split batch slots drafter GPU 0 free GPU 1 free note (ubatch 1536)
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# 262144 1,1 2048 1 DFlash2 8.64 GiB 4.43 GiB day's start
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# 262144 39,26 6144 2 DFlash2 2.42 GiB 7.20 GiB
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# 262144 38,27 6144 2 DFlash2 3.85 GiB 5.77 GiB
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# 262144 37,28 6144 2 DFlash2 4.28 GiB 5.34 GiB
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# 262144 36,29 6144 2 DFlash2 4.71 GiB 4.91 GiB
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# 262144 35,30 6144 2 DFlash2 5.14 GiB 4.48 GiB
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# 262144 40,25 6144 2 DSpark 1.87 GiB 1.86 GiB (retired)
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#
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# ...and the same deployed shape re-measured at ubatch 512, after llama-bench
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# showed 512 is the throughput optimum and costs 3.71 GiB less in total (see the
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# batch block below):
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#
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# 262144 36,29 6144 512 2 DFlash2 6.01 GiB 7.32 GiB ubatch only
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# 262144 35,30 6144 512 2 DFlash2 6.44 GiB 6.90 GiB
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#
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# ONE -ts STEP IS ONE LAYER, AND LAYERS ARE NOT THE SAME SIZE. This is a hybrid
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# stack -- 16 x (3 Gated DeltaNet + 1 Gated Attention) blocks -- so moving a
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# DeltaNet layer shifts ~0.43 GiB while an attention layer shifts ~1.43 GiB
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# (0.45 of weights plus ~1.0 GiB of its own f16 KV at 262144). Both step sizes
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# were measured, which is why the balance is found by measurement rather than by
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# interpolating between splits.
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#
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# WHY THE SPLIT IS 32:33 (operator call, 2026-09-19). The operator asked for
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# the tensors BALANCED across the two cards, which for 65 layers is 32:33 --
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# the most even split that exists. The predecessor's 35:30 skew existed to lean
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# work onto the x16 card (ROCm0), because ROCm1's Gen4 x1 chipset path made it
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# the 98 % utilization bottleneck; the measured trade of the balanced point is
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# that the x1 card runs hotter while the x16 card idles a little more, and the
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# operator accepted that for symmetry. Do not re-skew without asking.
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#
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# THE TWO CARDS' PATHS ARE NOT SYMMETRIC (measured, 2026-09-17): ROCm0
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# (03:00.0) hangs off CPU root port 00:01.1 at Gen4 x16 end to end (~31.5 GB/s);
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# ROCm1 (0b:00.0) rides the B650 chipset through bridges that each negotiate
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# Gen4 x1 (~1.97 GB/s). The layer-split boundary itself is not the cost
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# (~5.2 MB of activation per 512-token ubatch rides the wire); the x1 card is
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# COMPUTE-bound, not PCIe-bound.
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#
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# A FAULT TO WATCH. A 39:26 split, at 1x batch / 1 slot, died on its eighth
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# request: the amdgpu driver logged SDMA0 UTCL2 page faults and the engine
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# surfaced `ROCm error: an illegal memory access was encountered` -> ggml_abort,
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# i.e. a fault in the cross-card boundary copy, this layer split's hot path. It
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# has not reproduced since, but the signature is worth recognising -- it is NOT
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# an OOM, and no amount of headroom prevents it.
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#
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# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE (HIP reads
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# those bare vars; 2026-08-13 incident). llama.cpp takes --host/--port/--temp
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# as flags only.
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# llama-server: mainline fb27a525 built for gfx1201 by scripts/build-llama-rdna4.sh.
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LLAMA_BIN=/home/dank/GitPackages/llama.cpp-dflash2/build-rdna4-rocm714/bin/llama-server
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# Payload, fetched with scripts/download-frank-medium-gguf.sh.
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MODEL_DIR=/home/dank/models/Qwen3.8-27B-Uncensored-GGUF
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MODEL_FILE=Qwen3.8-27B-Uncensored-Q8_0.gguf
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# The f16 vision projector, same repo as the target. llama-server loads it via
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# the explicit --mmproj flag (no autoload in this build); the serve script
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# refuses to start without it when vision is enabled, so a missing file is a
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# deploy error, not a text-only server that pretends to be multimodal.
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MM_PROJ_FILE=mmproj-Qwen3.8-27B-Uncensored-f16.gguf
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DRAFT_MODEL_DIR=/home/dank/models/Qwen3.8-27B-DFlash2-GGUF
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DRAFT_MODEL_FILE=Qwen3.8-27B-DFlash2-Q4_K_M.gguf
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# Fleet identity. 7770 is the fleet's dankserver endpoint; the alias is the id
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# at /v1/models and what every client asks for.
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ALIAS=frank-medium
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BIND_ADDR=0.0.0.0
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LISTEN_PORT=7770
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# Device placement. HIP_VISIBLE_DEVICES hides the Raphael iGPU (HIP device 2)
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# so nothing can drift onto shared system RAM; DEVICES is what llama.cpp splits
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# across, and neither of the two cards has an XGMI link to the other.
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HIP_VISIBLE_DEVICES=0,1
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DEVICES=ROCm0,ROCm1
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SPLIT_MODE=layer
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# 32:33 -- balanced (operator call 2026-09-19): 65 layers split as evenly as
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# they can be, ROCm1 (the last-layers card, which also carries the drafter and
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# the output layers) holding the extra one. See the WHY THE SPLIT IS 32:33
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# block above for the trade against the predecessor's 35:30 x16-lean.
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TENSOR_SPLIT=32,33
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NGL=999
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# Auto-fit has no business resizing a context we set explicitly.
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FIT=off
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FLASH_ATTN=on
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# 327680 positions over TWO slots, so each conversation gets 163840 --
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# --parallel DIVIDES --ctx-size, so more slots would shrink every
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# conversation rather than grow the cache. 163840 is inside the checkpoint's
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# trained 262144 window, so the whole pool is trained territory (no rope
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# extrapolation, unlike the predecessor's 393216).
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CTX_SIZE=327680
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PARALLEL=2
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# MEASURED 2026-09-16 with scripts/bench-batch-ubatch.sh on this box: llama-bench,
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# a 64k prefill plus a 128-token decode with 64k of KV already resident, 3 reps,
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# one invocation per cell, the deployed split and f16 KV. --ubatch-size is the
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# physical batch the graph is built at -- and the only one of the two knobs that
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# costs VRAM:
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#
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# batch ubatch prefill tok/s GPU 0 / GPU 1 free
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# 6144 1536 1164.96 +- 2.30 10.28 / 11.71 GiB
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# 6144 768 1135.03 +- 1.09 11.29 / 13.28 GiB
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# 6144 512 1192.93 +- 0.91 11.63 / 13.81 GiB <- deployed
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# 6144 384 1068.04 +- 0.79 11.80 / 14.07 GiB
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# 6144 256 1045.98 +- 1.69 11.97 / 14.33 GiB
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# 4096 1536 1161.90 +- 0.96 10.28 / 11.71 GiB
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# 2048 1536 1170.23 +- 0.86 10.28 / 11.71 GiB
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# 2048 512 1193.88 +- 1.01 11.63 / 13.81 GiB
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#
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# THE PLATEAU STARTS AT 512. 512 -> 1536 buys ~2 % of prefill -- inside the
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# run-to-run spread, and the 512 cells were the faster pair in BOTH batch
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# columns -- while costing 1.35 GiB on ROCm0 and 2.10 GiB on ROCm1, so the
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# deployment takes the cheap end. BELOW 512 the SMs are no longer saturated:
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# 384 and 256 fall ~11 % and ~12 %. The logical batch is free either way --
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# 2048, 4096 and 6144 land within 2 % of each other at a fixed ubatch and use
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# the SAME VRAM, because the compute buffers are sized by ubatch alone.
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#
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# The prefill figure is a property of the SHAPE, not of this knob: the same
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# sweep with no depth in front of it (depth 0) runs 1543 tok/s at ubatch 256 and
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# 1723 at 1536 -- the plateau is in the same place, and the ~32 % between the two
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# tables is what attending over 64k of resident KV costs. Decode is 15.88 tok/s
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# at depth 65536 on EVERY ubatch (17.80 at depth 0), so this knob cannot touch
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# generation at all.
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#
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# 8192/2048 stays untested under this drafter (the "2020 MiB pp buffer fails on
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# ROCm1" note was measured under DSpark, when it had 0.08 GiB free), and there is
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# no longer a reason to want it: batch buys nothing measurable here.
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BATCH_SIZE=6144
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UBATCH_SIZE=512
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# Threads cover tokenization, sampling and HTTP only: every layer is on a GPU.
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NUM_THREADS=8
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THREAD_BATCH=8
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# f16 K/V: an agentic checkpoint is the wrong place to spend KV precision.
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CACHE_TYPE_K=f16
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CACHE_TYPE_V=f16
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# Context checkpoints: 0 -- no per-slot KV snapshots at all. The build's
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# default is 32 (this binary's --help), so the 0 below is a deliberate
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# deviation, not an inherited default: a checkpoint is a VRAM->host-RAM copy
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# of part of a slot's KV, taken so a partially-matching prompt can be restored
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# instead of re-prefilled. This endpoint trades that away -- its reuse comes
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# from the slot prompt and from the RAM-side prompt cache below. Stated
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# explicitly so the tunable stays visible rather than merely absent.
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CTX_CHECKPOINTS=0
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# PROMPT CACHE, HOST RAM -- this is the one cache tunable that is NOT VRAM.
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# --cache-ram caps the total bytes of saved prompt states
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# (server_prompt_cache, tools/server/server-task.cpp): update() evicts
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# oldest-first past the cap, and alloc() REFUSES outright any single state
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# bigger than the whole cap. A state is the serialized KV of a whole sequence
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# plus its DeltaNet state plus the draft context's own KV, so at this shape it
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# costs ~68 KiB per token --
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# 16 full-attention layers x 4 kv heads x 256 dim x 2 B x 2 (K,V) = 64 KiB
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# plus the draft context's window-bounded KV (a ~40 MiB constant for DFlash2)
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# -- which makes the cap a TOKEN budget in disguise: 16384 MiB holds ~240k
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# tokens, i.e. a FULL 163840-token slot (~10.6 GiB) with ~5 GiB of the budget
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# spare for a second entry.
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#
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# The cost is host RAM, allocated on demand and never pinned (plain heap
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# vectors), so no VRAM number above moves. dankserver has 62 GiB beside the
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# media-stack co-tenants, so the cache is bounded rather than unlimited (-1):
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# the seam test refuses a budget over half the host's RAM.
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CACHE_RAM=16384
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# Sampling defaults for requests that send none -- the values the vLLM
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# predecessor served on this endpoint (`--override-generation-config`), so a
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# client sees no change across the runtime swap. The model card's own
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# recommendation is temp 1.0 / top_p 0.95; the harness sets its own per model in
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# models.yml, so this is the floor, not the contract.
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TEMPERATURE=0.7
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TOP_P=0.95
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TOP_K=20
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MIN_P=0.0
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PRESENCE_PENALTY=0.0
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# Reasoning on, projected into reasoning_content (deepseek format) with no
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# budget: the model decides how long to think.
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REASONING=on
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REASONING_FORMAT=deepseek
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REASONING_BUDGET=-1
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# Chat template: empty = the one embedded in the GGUF (Qwen3.8's own).
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CHAT_TEMPLATE_FILE=
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CHAT_TEMPLATE_KWARGS='{"enable_thinking":true,"preserve_thinking":true}'
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# Speculative decoding: the DFlash2 sidecar (operator call 2026-09-19). The
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# target file also carries its own MTP head (nextn_predict_layers=1), so
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# draft-mtp is one SPEC_TYPE change away -- the drafter file stays on disk.
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# n_max 7 is the measured DFlash2 winner (see the table below); the drafter
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# rides ROCm1, the card that holds the last layers.
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SPEC_TYPE=draft-dflash
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SPEC_DRAFT_N_MAX=7
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SPEC_DRAFT_NGL=999
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SPEC_DRAFT_DEVICE=ROCm1
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SPEC_DRAFT_TYPE_K=f16
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SPEC_DRAFT_TYPE_V=f16
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# DFlash2 vs the model's own MTP head -- MEASURED 2026-09-16 on the predecessor
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# payload (temperature 0, 1k prompt / 512 out; the autoregressive baseline and
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# both MTP depths were run in one session, same binary, same prompts):
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#
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# none 17.7 tok/s card1 26.07 GB
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# draft-mtp n=3 32.4 tok/s 58 % accepted card1 29.35 GB
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# draft-mtp n=7 26.7 tok/s 33 % accepted card1 29.65 GB
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# draft-dflash 7 43.5 tok/s 41 % accepted card1 29.42 GB
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#
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# All four produced IDENTICAL greedy text, so verification is lossless
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# whichever proposer runs. DFlash2 is the faster proposer (2.46x over
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# autoregressive, 34 % over the best MTP). The MTP head adds ~3.3 GB to the
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# card that holds it, against 1.06 GB for the Q4_K_M drafter's file -- at the
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# 327680 pool the drafter's window-bounded KV (~40 MiB) leaves headroom on
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# both cards, so the speed wins.
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#
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# THE TWO CANNOT BE CHAINED. `--spec-type draft-mtp,draft-dflash` dies at load:
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# context type MTP requested but model doesn't contain MTP layers
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# failed to create MTP context
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# A chain has ONE draft context, and MTP builds against whatever model that
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# context holds -- here the DFlash2 file, which has no MTP layers. MTP's head is
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# only loaded when MTP IS the draft model ("creating MTP draft context against
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# the target model ..."), so a chain of the two is a structural dead end, not a
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# tuning question. See serverAppConfigs-docs/LLM/frank-medium-llamacpp-dflash2.md.
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# ngram-mod: the draftless n-gram matcher (CPU hash table in host RAM, no VRAM,
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# no model file, no draft context). It joins the chain unconditionally --
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# unlike MTP, which needs a draft context and therefore cannot share one with
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# DFlash2. NOT CURRENTLY ACTIVE: SPEC_TYPE is draft-dflash alone, and the serve
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# script only passes --spec-ngram-mod-* when the type contains "ngram". The
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# window below is retained because the DFlash2 + ngram chain was measured free
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# on prose and 2.6x on prompt-echo -- one SPEC_TYPE change brings it back.
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#
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# WINDOW MEASURED 2026-09-16 (chain at each window, decode tok/s):
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#
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# n_min/n_max prose 1k/512 prompt-echo 1.5k/400
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# 48/64 43.9 200.6
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# 16/32 36.0 161.0
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# 8/16 36.2 113.8
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#
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# The long window is the right one, and not only because it wins on echo: a
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# SHORT window makes the matcher propose on short fragments of ordinary prose,
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# where the proposals are rejected and the verification still costs -- which is
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# the "ngram hurts" result the fleet measured on other payloads twice before.
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# With 48/64 the chain is byte-identical to DFlash2 alone on prose (same 119
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# drafts / 49 accepted) and on the identity prompt (70/53).
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#
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# Do not shorten these without re-running scripts/bench-spec-ab.py: the
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# no-penalty property is what makes the chain safe as a default.
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SPEC_NGRAM_N_MATCH=24
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SPEC_NGRAM_N_MIN=48
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SPEC_NGRAM_N_MAX=64
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# Vision enabled: llama-server loads mmproj-Qwen3.8-27B-Uncensored-f16.gguf
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# (931 MiB, lands on ROCm0 -- the fit charges it there) via the explicit
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# --mmproj flag. This keeps frank-medium multimodal across the model swap.
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NO_MMPROJ=0
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