--- type: research title: Qwen3.8-Flash-Next FP8 on tcclaviger — expert-offload decode tuning description: VERIFIED 2026-09-29 by measurement. The flag-level decode-tuning campaign on the ad hoc qwen-fp8 container (orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8, tcclaviger RDNA4 vLLM fork, 2x R9700): the >35 tok/s target is MET at 37.4-40.6 tok/s by turning prefetch OFF — telemetry measured its precision at 36%, wasting ~half of all gather traffic — while the original 26.8 tok/s incumbent (prefetch on) survived every other challenger. Residual upside is the fork's gather read path (effective ~6.5-9.5 GB/s vs 22.7 GB/s the link delivers at the same slab size). tags: - dankserver - r9700 - gfx1201 - qwen3.8 - fp8 - expert-offload - speculative-decoding - tuning timestamp: 2026-09-29 --- # Qwen3.8-Flash-Next FP8 — expert-offload decode tuning > **Question this page answers.** On the tcclaviger RDNA4 vLLM fork serving > `orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8` across both R9700s, what > runtime flags actually move decode at a 32k working point? > > **Answer in one line.** **37.44–40.57 tok/s — the >35 target is met — by > deleting `--expert-prefetch 1`.** Expert-cache telemetry measured the > prefetcher at **36% precision** (36,720 slabs prefetched, 13,369 served): > ~half of all gather traffic was useless copies, and the copies it "saved" > would have been installs anyway. The prefetch-on incumbent (26.8 tok/s) > survived every other challenger measured: gather geometry, spec depth, > TunableOp, scheduler batch knobs, concurrency, ngram-only drafting. This is the ad hoc `qwen-fp8` container on Dankserver (rootless podman, image `docker.io/tcclaviger/vllm:latest`, engine `v0.29.0.dev0+g2bdbbc8080`), NOT the `frank-primary` systemd service — the R9V production unit is untouched, so no stop-and-mask was required for any of this. All numbers are decode tok/s measured from the server's own `usage.completion_tokens / (wall - ttft)` — SSE chunk counting is invalid under speculative decoding because rejected drafts change the accounting. ## Deployment shape (the tuned incumbent) Serve script on the host (`/home/dank/serve_qwen_fp8.sh`), env hooks in CAPS. **PREFETCH must be unset** (omitting the flag; the fork's default is off): ``` MODEL=/home/dank/models/Qwen3.8-Flash-Next-Uncensored-FP8 (~172.8 GiB, 131 shards) IMAGE=docker.io/tcclaviger/vllm@sha256:da4029666668b90a0386c1ed23ea370c79916ad4e1a6ad1dd9db098b9fa069ea # image ID 3322dbbbc96d; local alias qwen-fp8-runtime:tuned-20260929; # the floating :latest tag moved once already — always launch by digest. # repo tag of this state: qwen-fp8-tuned-20260929 --tensor-parallel-size 2 --max-model-len 32768 --gpu-memory-utilization 0.98 --kv-cache-memory-bytes 2147483648 --max-num-seqs 4 --enable-prefix-caching --speculative-config '{"method":"mtp","num_speculative_tokens":4}' # NO --expert-prefetch: measured 36% precision, ~50% wasted gather traffic ``` Published to the host LAN via the serve script's `PORTMAP` hook (`PORTMAP=7773:7770`): health and completions answer on `http://192.168.0.235:7773` (verified host-side 2026-09-29; 256k config, decode 36.49/34.55/35.98 through the published path). Host port 7773 was chosen over 7770 because `frank-primary` (7770) is stopped but ENABLED — any future start would collide with a published 7770. Device notes carried from the bring-up: `--device /dev/dri` must be the WHOLE directory (individual renderD nodes enumerate but refuse contexts — "No CUDA GPUs are available" while device_count() reports 2); the image entrypoint execs `vllm serve` itself. The memory plan this produces (from the engine's own `expert_plan.py` log): | region | size | |---|---:| | expert cache space | 15.05 GiB = 6,574 slots x 2.344 MiB | |   VRAM-only resident layers | 3.52 GiB (layers 0-2, 1,536 experts) | |   swap region | 11.53 GiB (5,038 slots, 111-112/layer) | | host-backed experts | 52.74 GiB per rank, 23,040 experts | | KV reserve (one max-len request +8%) | 0.36 GiB of the 2 GiB pool | | CUDA graphs | 1.93 GiB (21 graphs; 1.32 GiB at `--max-num-seqs 2`) | | runtime overhead | 7.6 GiB (non-torch + buffers + activation) | ## Measured arms (2026-09-29) Decode tok/s at 166 / 1087 / 4064 prompt tokens, median of 3 runs after a 64-token warm pass, temperature 0, max_tokens 256: | Arm | 166 | 1087 | 4064 | Acceptance | Verdict | |---|---:|---:|---:|---:|---| | **MTP4, stock 8x16, PREFETCH OFF** | **40.57** | **37.44** | **37.57** | 54.0% | **NEW INCUMBENT — target met** | | MTP4, stock 8x16, prefetch ON | 26.80 | 25.15 | 26.81 | 60.7% | old incumbent (+51%/+40% worse) | | MTP4, prefetch ON (re-check) | 25.80 | 24.38 | 26.63 | 47.9% | reproduces (noise band ±1) | | MTP4, prefetch ON, gather 16x32 | 24.30 | 22.77 | 24.11 | 50.3% | −9% — ruled out | | MTP4, prefetch ON, gather 32x64 | 26.58 | 24.94 | 26.34 | 49.6% | parity — ruled out | | MTP3, prefetch ON | 24.47 | 24.41 | 24.44 | 66.7% | −9%, flat in ctx — ruled out | | MTP6, prefetch ON | 19.07 | 17.81 | 13.54 | 17.4% | collapses; crosses UVA threshold — ruled out | | MTP4, prefetch ON, MNBT 8192 | 29.32 | 24.12 | 23.65 | — | neutral — ruled out | | MTP4, prefetch ON, PREFETCH depth 2 | 25.83 | 25.06 | 25.87 | 56.0% | neutral — ruled out | | MTP4, prefetch ON, TunableOp w/ offload | 23.59 | (79.82*) | (79.89*) | (96.5%*) | **rejected — corrupts** | | 2-way concurrency, prefetch ON (2 streams) | — | — | 15.06 + 23.11 = **25.49 summed** | — | no gain vs 26.8 single | | ngram-only drafter | — | — | — | — | fork bug: PLE inputs unprepared | | 256k ctx (MAXLEN=262144, KV 4.5 / 4.03 GiB, prefetch off) | 36.65 / 36.48 | 34.21 / 34.13 | 34.77 / **35.02** | 53.0 / 39.4% | boots and serves; band 34.1-36.5 straddles 35 within noise; arena identical (5,648) at both KV sizes | \* The TunableOp numbers are invalid by construction: the tuned GEMMs changed generated behavior (repetition-loop, the server's "repetition detected" halt firing at ~140-146 tokens instead of the full 256, acceptance jumping 60.7% → 96.5%), the 79.8 tok/s figures were measured on those halted degenerate completions, and the text-only check on a fresh prompt came back 20.01 tok/s — *below* baseline. GEMM autotuning that changes output text is broken tuning. The decode curve without spec decode is dead flat 12.11 tok/s from 304 to 16,095 tokens — decode is context-independent on this shape, bound per-step by the expert gather, not by KV depth. ## Telemetry — the measurement that found the win `R4D_LRU_TELEMETRY=1` arms per-layer counters; a torch-profiler window (`/start_profile` → traffic → `/stop_profile`) makes the fork log and write `expert_cache_rank.json` beside the traces. **`torch_profiler_with_stack=false` is mandatory in the profiler config** — the default (true) grew a 6.5-min window's trace buffer ~40 GB and the kernel OOM-killed the engine core (anon-rss 95.2 GB, `oom-kill` in `journalctl -k`). The short window at 166 ctx (~15 s of traffic) completed cleanly with `with_stack=false`. The fork's own numbers (102 steps, both ranks identical): ``` hit rate 0.8245 (137,084/166,262) host installs 15,814 read-through 13,364 prefetched 36,720 served by prefetch (migrations) 13,369 ``` - **The LRU is good**: 82.45% hit rate overall; layers 0-2 pinned at 100%; swap-region layers 78-81%. The "arena holds only 27% of experts → 73% miss" arithmetic was wrong — temporal reuse carries it. - **The prefetcher is the poison**: 36,720 slabs moved to serve 13,369 (36% precision). The 23,351 unused copies are ~0.7 GB/step/rank of pure waste — roughly half of all gather traffic — and the 13,369 "savings" would have been installs anyway. Depth-1 prefetch also cannot cover the ~2,400-slab per-step working set with its 16 slots/layer (768 total). - Per-layer-step at a swap layer: ~30.6 routed, 24.8 hits, 3.3 installs, 2.6 read-through, 8.0 prefetched of which 1.7-3.3 useful. ## The wall, measured - **PCIe link (pcie-link-probe):** GPUs `03:00.0`/`07:00.0` LnkCap/LnkSta 32GT/s x16; root ports `00:01.1`/`00:01.3` cap x8. Effective **Gen5 x8 = 31.5 GB/s/direction**. `rocm-smi pcie_link_speed: 25` is the ASPM idle downshift, not the loaded rate. - **H2D (HIP probe, pinned):** 2.345 MiB (the engine's exact expert slab) **22.74 GB/s**; 16 MiB 28.19; 1 GiB 28.74 (91% of ceiling). Pinned ≈ pageable — staging is not the lever. Single-transfer latency at slab size 105.7 µs vs 78 µs ideal → ~21% per-transfer overhead. - **Engine's effective gather rate:** ~6.5-9.5 GB/s = **30-42% of what the link delivers at the same size**. The bottleneck is the fork's read path (chunk/lane geometry, per-transfer cost), not the wire. - **Duty cycle:** peak GPU util 100%, smooth ~29% with prefetch on — each step is ~100 ms of compute then hundreds of ms waiting on expert streaming. rocm-smi reports *utilization*, which reads ~0 while weights load during an arm's reload; VRAM is the honest panel there. ## What the remaining flags cannot do (all measured) - **Gather geometry** — 16x32 and 32x64 measured no better despite the fork's "tuned at the Gen4 ceiling" comment; the constants are already at parity on Gen5 x8. The read-path gap is structural, not a tuning knob. - **Spec depth** — 4 is the optimum both ways (with prefetch on). Depth 6 additionally proves the UVA read-through path (`step tokens > R4D_LRU_UVA_TOKENS=5`) is *slower* than copy-in. - **Prefetch anything** — depth 2 neutral, depth 1 the new negative; the tier is 3x too small for the working set and the predictor is 36% precise. `expert_prefetch_k/budget/slots` exist (defaults 20/14/16) but the precision result makes tuning them pointless. - **Arena growth via shrink** — `UTIL=0.99` fails boot (`Free memory on device cuda:1 (31.29/31.86)`); `--max-num-seqs 2` fails CUDA-graph capture (`Constraints violated (L['query_start_loc'].size()[0])`); and the MNBT 8192 arm already proved a ~1 GiB arena shift is decode-neutral in both directions. - **Concurrency** — under TP=2 both streams' tokens are computed on both cards, so each rank gathers the union of both streams' misses; the links do not add capacity and the shared arena thrashes. Summed 2-way (25.49) measured *below* single-stream (26.8) with prefetch on. - **ngram-only drafting** — fails at engine init: `RuntimeError: PLE inputs were not prepared`; the fork prepares this model's PLE inputs only on the MTP spec path. Needs a fork patch, not a config. - **V-only q8 KV** — stock `--kv-cache-dtype` offers fp8 variants only (K+V together); a V-only q8 needs a fork patch. ## Fork bugs / traps found (for the engine authors or a future patch) 1. `CLAV_TUNABLEOP_SWEEP=1` is silently skipped whenever expert offload is active unless `CLAV_TUNABLEOP_WITH_OFFLOAD=1` is also set (`tunableop_warmup.py`). 2. TunableOp-with-offload, once forced on: +6.7 min warmup, no persistent cache (container-local), and corrupt numerics (repetition loops, early-EOS halts, invalid measurements). Rejected outright. 3. The torch-profiler stop path hangs the workers on ROCm when `torch_profiler_with_stack` is true (default): >10 min hangs on two attempts while the AsyncLLM wrapper reports "stopped successfully" in 1 s; the default stack-tracing grew a 6.5-min window's buffer ~40 GB and OOM-killed the engine core. With `with_stack=false` the stop completes and the telemetry report runs. The telemetry counters remain reachable ONLY through that profiler stop — no direct RPC trigger exists (`telemetry_report` has exactly one caller, `gpu_worker.py`). 4. ngram spec method does not prepare PLE inputs (above). ## Where the headroom lives now The 35 tok/s target is met (37.4-40.6) with prefetch off. What is left: 1. **Fork patch to the gather read path** — the ~6.5-9.5 → 22.7 GB/s software gap at slab size. If the gather reached wire rate, decode lands in the 60+ tok/s band before acceptance effects. Code work in `expert_lru.py`'s gather (`R4D_LRU_GATHER_*` is read at import; the copy-in path itself is compiled — see `/app/r4dhip/r4d.so`). 2. **256k ctx — measured, final**: boots at `MAXLEN=262144` with `KV_BYTES=4831838208` or `4328521728` alike — a 262,144-token window needs **4.0 GiB** KV (hybrid attention + mamba pages ≈ 16 KiB/token, not the 32k plan's 9,190 B/token; 3 GiB boot-refuses and the engine estimates max len 184,224 at that pool; the exact-4.0 override loses ~20 MB to accounting and boots as 3.98 < 4.0). Decode band **34.1-36.5** across both KV sizes — the arena is **identical (5,648 slots)** at 4.5 and 4.03 GiB (the planner buckets; the trim went to margin, not slots), so the window costs ~10% decode vs the 32k config and no KV trim recovers it. The alternative that keeps the full 37.4-40.6 band is `MAXLEN=184224` at 3 GiB KV. Correction: the planner sizes the arena AFTER the KV override (the earlier "before" reading was wrong) — the arena is what remains. 3. **2+ slots at long ctx** — the KV pool post-spec holds ~66k tokens at 2 GiB (~2x 32k streams); concurrency summed *below* parity at 4064 ctx, so slots are a capacity feature, not a throughput one. Two full 256k streams need ~8-10 GiB of KV — that fits the arithmetic only by cutting the arena below usefulness; slots-at-256k is a fork-patch or smaller-payload question. ## Reproducing Host-side scratch scripts (2026-09-29): `/home/dank/serve_qwen_fp8.sh` (the serve script with all hooks), `/home/dank/mtp_measure.py` (usage-counter decode measure, 3 depths), `/home/dank/mtp_measure_short.py` (single depth, for profile windows), `/home/dank/bench2way.py` (concurrency), plus the arm scripts `arm_*.sh` / `sweep_*.sh` and the detached runner `/home/dank/job.sh` (outputs under `/home/dank/jobs/NAME.out|.rc|.done`). The measure scripts are `podman cp`'d into the container after every recreate (the serve script re-copies them; a recreate wipes container-local files). ## 2026-10-01 — production cutovers (qwen FP8+MTP4, then bf16 no-spec) Two same-day cutovers moved this page's findings into production: 1. **Qwen FP8 + MTP4 replaced the failed GLM/DFlash2 experiment** (the GLM window is documented in [[frank-primary-glm53-flash-w4a16]]; git tag `frank-primary-fp8-mtp-20261001` is the last commit of that shape). The production trio took MAXLEN 200000 x 3, fp8 KV, MTP4, prefetch off — measured 37.4-40.6 tok/s at ~54% acceptance. 2. **The overnight redo campaign re-measured EVERY lever at matched geometry** (prefetch OFF everywhere, MAXLEN 200000/SEQS 3/fp8 baseline) and concluded the day: the incumbent cutover to **bf16 KV, no speculative decode** (Frank's call). ### What the redo campaign measured (all at shallow depth, 166/1087/4064) | lever | verdict | |---|---| | prefetch (re-confirmed) | the whole night's +40-51% lives in deleting it; the old 12.11 "no-spec baseline" was itself prefetch-taxed | | gather geometry | **R4D_LRU_GATHER_CHUNKS=32 / LANES=64 = +18.7%** (49.25 @4064), the win GROWS with depth (39.5@166 -> 54.1@1087); g8x16 mild, g64x128 past peak | | KV dtype | bf16 (`auto`) +4% decode on fp8-era numbers but halves attention-KV capacity (measured 14,047 vs 7,926 B/token; mamba states unaffected) | | spec family | no-spec fastest once the tax was gone (41.46/41.29/41.47); MTP4's drafter reserves ~2.6 GiB/rank beside the pool; untaxed MTP re-measures never completed | | MNBT | 512 doubles TTFT (2.46 s vs 1.36 s @4064); 1024-4096 interchangeable; 12288 -2.5% + arena; **locked 1024** | | UVA | 2048 POISON (-39%); 8192 ~parity | | arena | flat ±1.4% from ~6,830 down to ~5,500 slots; knee below | | six slots | every SEQS-6 KV sizing refused at engine init | Depth caveat: every number above is at a near-empty KV. Deep-context behavior (the gather win growing with depth, bf16's decode cost at depth) is only sampled at 3 depths; a 64k-192k-depth probe is the flagged gap. ### The deployed shape (2026-10-03 shape G: 3 slots x 200704, fp8, precise memory, MNBT 2048, arena at the flag ceiling) ``` FRANK_PRIMARY_MAXLEN=200704 200704-token window FRANK_PRIMARY_SEQS=3 --max-num-seqs 3 (SEQS=2 CANNOT boot - below) FRANK_PRIMARY_KV_BYTES=3166500000 PER RANK (fp8, 7,892 B/token -> 400,700 tokens) FRANK_PRIMARY_KV_DTYPE=fp8 --kv-cache-dtype fp8 (quantized) FRANK_PRIMARY_MNBT=2048 --max-num-batched-tokens 2048 (arena-free, see shape F) FRANK_PRIMARY_UTIL=1.0 budget = total x util, forced to the le=1 ceiling FRANK_PRIMARY_CUDAGRAPH_BASE_MB=0 / _PER_GRAPH_MB=12 -e VLLM_CUDAGRAPH_MEM_* FRANK_PRIMARY_PRECISE_MEMORY=1 --expert-precise-memory (overhead 7.60 -> 5.25 GiB) FRANK_PRIMARY_GPU_MAX_HW_QUEUES=2 -e GPU_MAX_HW_QUEUES=2 (HSA queues/GPU) FRANK_PRIMARY_CACHE_DIR=/home/dank/.cache/frank-primary mounted at /root/.cache R4D_LRU_GATHER_CHUNKS=32 / R4D_LRU_GATHER_LANES=64 (container env) NO --speculative-config, NO --expert-prefetch ``` ### Shape F/G (2026-10-03): where the free VRAM actually is, and what forcing it cost `rocm-smi` showed **3.57 GiB free per R9700** at the deployed shape E (28.28 GiB used of 31.86 GiB, after hours of real traffic — so it is the high-water mark, not an idle artifact). Growing the expert arena into it is NOT a matter of raising `--gpu-memory-utilization`, because the arena is ``` arena = budget - non-expert - KV reserve - CUDA graphs - runtime overhead budget = device_total x gpu_memory_utilization # expert_plan.py:567 ``` and every term on the right is either fixed or a lie: | term | plan charges | measured reality | reachable from our side? | |---|---|---|---| | `budget` | 31.22 GiB (0.98) | field is `le=1`, so 31.86 GiB max | **yes** — and shape G takes it to `1.0` | | `non-expert weights` | 6.28 GiB | checkpoint census | no | | `KV reserve` | 1.91 GiB (registry 9,190 B/token x 200704 +8%) | real pool bills 2.93 GiB at 7,892 B/token | no (registry constant) | | `CUDA graphs` | 0.55 GiB = 256 MiB + 52 MiB x 6 | `Graph capturing finished in 3 secs, took 0.31 GiB` | **yes** — `VLLM_CUDAGRAPH_MEM_BASE_MB` / `_PER_GRAPH_MB` | | `runtime overhead` | 5.25 GiB (`--expert-precise-memory`) | far lower at idle | no (registry constant) | Shape F took the graph term (0.55 -> 0.32 GiB) and nudged util to 0.982: **arena 17.52 GiB / 7,653 slots measured**, +131 slots. Shape G takes the graph term further (0.32 -> 0.07 GiB) at the same util. **Shape G takes the graph term to the measured floor, util to the law.** The graph base pool is the actual over-charge: shape F's own boot reported `Graph capturing finished in 4 secs, took -0.06 GiB` — a **negative** capture delta, so the 256 MiB base was fiction. Charging `0 base + 12 MiB/graph` (0.07 GiB for the shipped 6-graph plan, and still per-graph so an 18-graph spec-decode shape pays 0.21 GiB) lands the plan at budget 31.29 / graphs 0.07 / **arena 17.77 GiB / 7,762 slots measured**. The KV ask went to `3,166,500,000` B/rank, which the engine's own 7,891.8 B/token price lands at **401,408 tokens measured / 2.00x concurrency** — the first shape holding two full-length conversations with real spare. (The ask/landed delta is block alignment: 196 x 2048. The price is shape-dependent, 7,701 B/token at 262144.) **UTIL 1.00 is not a knife-edge on this fork, it is UNBOOTABLE — measured 2026-10-03.** One deploy took the field maximum to buy the last 0.57 GiB of arena; both workers refused init BEFORE loading a single weight, twice: `ValueError: Free memory on device cuda:N (31.29/31.86 GiB ...)` — the worker demands free > budget and budget == total, so `le=1` on the CLI field is a trap: the engine dies in a 2-restart crash loop with zero payload progress. So `0.982` is a LAW, not a preference, and the "why 0.982" answer now has two proofs: this ValueError, and the historical ceiling deaths (the 2x262144 bf16 boot at 0.13 GiB free; the 5x262144 boot at "0 bytes free" on a real 995-token request). Everything beyond ~7,912 slots needs the registry edited inside the image (the fork's log line says exactly that: *"Update the registry entry if this drifts"*), not a deploy. **MNBT 1024 -> 2048 is free on every axis the plan can see.** The registry overhead model is `base + 0.22 GiB x max(0, (nbt - 4096)/1024)` anchored at nbt 4096, so any rung up to 4096 charges the identical 5.25 GiB; the fitted 5.25 was itself fitted at nbt 4096, which means 2048 is covered by an activation figure measured at a **larger** batch. SEQS 3 boots at both 1024 and 2048 (measured 2026-10-02 — the `query_start_loc` `ConstraintViolationError` is a SEQS property). The costs are a one-time recompile (the compile **range** is part of the cache key, so `FRANK_PRIMARY_CACHE_DIR` pays once) and a wider prefill chunk, which the 09-29 sweep found decode-interchangeable across 1024-4096 while 512 doubled TTFT at 4064 ctx (2.46 s vs 1.36 s). **`--expert-precise-memory` is the arena lever that does not touch the window.** It attacks the planner's overhead term (shape F above attacks the graph term — the third and last term this repo can reach), and the flag swaps the default registry figure for the fitted one. Measured 2026-10-02 by a boot probe on the shape-D base (production stopped, candidate booted AS the service; numbers are the engine's own plan lines): | plan line | shape D | **shape E** | |---|---|---| | `runtime overhead (non-torch + buffers + activation)` | 7.60 GiB | **5.25 GiB** (`precise (gfx1201 TP2 nbt4096/seqs16/len262144)`) | | `expert cache space` | 14.87 GiB | **17.22 GiB** | | slots | 6,496 | **7,522** (+1,026, +16%) | | `swap region` | - | 13.7 GiB / 5,986 slots, 133-134 per layer | | boot | - | healthy in 420 s, no OOM | `'expert_precise_memory': True` also appears in `non-default args`, so a silent argparse drop is visible in the boot log. The flag lands **in the arena, not the pool** - `--kv-cache-memory-bytes` is untouched at 3.15e9/rank (option A), because the pool is not an arena lever and cutting it further would only cost concurrency: a 2.2e9 pool was probed in the same window and reports 277,686 tokens / **1.38x**, so it is the conservative fallback if free VRAM ever binds, not the shipped value. **The compile cache is a boot-cost fix, and it was worth 173 s per boot.** Measured 2026-10-02: the image ships **no `/root/.cache` at all** (a fresh container: `No such file or directory`), and after a boot that directory holds `vllm` at **1,154,714,333 B in 4,665 files** plus `comgr` at 229 MB, all inside the container's ephemeral layer. Every `podman rm -f` - i.e. every deploy, restart and reboot - discards it, and the engine re-pays: ``` Using cache directory: /root/.cache/vllm/torch_compile_cache/b265bd396a/rank_0_0/backbone Dynamo bytecode transform time: 4.99 s Compiling a graph for compile range (1, 1024) takes 173.19 s saved AOT compiled function to /root/.cache/vllm/torch_compile_cache/torch_aot_compile/… torch.compile took 181.20 s in total init engine (profile, create kv cache, warmup model) took 217.26 s (compilation: 181.20 s) ``` Mounting a host directory at `/root/.cache` makes every later boot load the AOT artifact instead. The mount is deliberately the **parent**, not `/root/.cache/vllm`: `VLLM_CACHE_ROOT` defaults to the `vllm` child, and `comgr` is the HIP-kernel JIT cache, so one mount persists both — and it hides nothing, because the image ships no `/root/.cache`. The cache key includes the compile range, so the MNBT choice costs exactly one compilation. Entries are version- and config-hashed: a stale entry is a miss, never a wrong answer. **Proven live on the deployed endpoint, 2026-10-02, consecutive boots of this exact shape:** | boot | engine log | |---|---| | cold (deploy) | `Compiling a graph for compile range (1, 1024) takes 179.24 s` → `torch.compile took 188.26 s in total` | | warm (restart) | `Directly load the compiled graph(s) for compile range (1, 1024) from the cache, took 2.143 s` → `torch.compile took 3.97 s in total` → `init engine (profile, create kv cache, warmup model) took 41.13 s (compilation: 3.98 s)` | The host directory held **1.4 GB in 4,707 files** after the cold boot. Time to `/health` is dominated by the 172.8 GiB payload load (the warm boot reached health in 490 s), so the win is the ~184 s of inductor/AOT build rather than a proportional cut in boot time. `GPU_MAX_HW_QUEUES=2` caps the HSA hardware queues per GPU. Verified a real knob rather than a no-op - the literal is read out of `/opt/rocm/core-10.0/lib/libhsa-runtime64.so` - and shipped deliberately alongside the 32x64 gather geometry. It is **not** A/B measured. **The pool, not the slot count, sets how many conversations live at once.** Measured live at this shape: `GPU KV cache size: 398,658 tokens, Maximum concurrency for 200,704 tokens per request: 1.99x` (3,146,129,408 B reserved). The per-token price is **shape-dependent**: the 262144 shape priced at 7,701 B/token (6,100,000,000 B -> 792,069 tokens), this pool at 7,892 - ~2.5% dearer, so a concurrency figure never extrapolates across geometries. The endpoint advertises 3 slots: a third LONG request queues rather than being refused, and shorter requests use the spare blocks. Verified: three concurrent 67-token requests answered in parallel, ~4.1 s each. Two contexts at 200704 are a capacity contract, not a throughput one (2-way concurrency summed BELOW parity at 4064 ctx). #### `--max-num-seqs 2` is impossible on this model (measured 2026-10-02) Four single-variable boot probes against the then-deployed shape, production stopped for each: | MAXLEN | SEQS | MNBT | result | |---|---|---|---| | 262144 | 2 | 1024 | **Engine core initialization failed** | | 262144 | 2 | 2048 | **Engine core initialization failed** | | 262144 | 3 | 1024 | healthy - 792,069 tokens, 3.02x, arena 6,248 | | 262144 | 3 | 2048 | healthy - the then-deployed incumbent | The failure is `torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (L['query_start_loc'].size()[0]) ... specialized it to be a constant (3)`, raised from AOT `build_guards(strict_error=True)` during `determine_available_memory -> profile_run -> _dummy_run`. `model_runner.py` sizes that input buffer `max_num_reqs + 1` and `_dummy_run` sets `num_reqs = min(max_num_batched_tokens, max_num_reqs)`, so the width is `SEQS + 1` for ANY `MNBT >= SEQS`: 3 at SEQS 2, 4 at SEQS 3. **MNBT is not a lever here** - it only moves the compile RANGE ((1,1024) vs (1,2048)), which is why the identical error appears at both rungs. `--enforce-eager` bypasses the path at the cost of CUDA graphs. The same failure class killed the 2026-10-01 bf16 2 x 262144 shape. #### The arena lever is `max_model_len`, not `--kv-cache-memory-bytes` The fork's planner sizes expert cache as `budget - non-expert - KV reserve - graphs - overhead`, and the KV reserve is registry-priced at **9190 B/token x max_model_len** - not from the `--kv-cache-memory-bytes` value: | MAXLEN | KV reserve | arena | |---|---|---| | 262144 | 2.48 GiB | 14.30 GiB / **6,248 slots** | | 200704 | 1.91 GiB | 14.87 GiB / **6,496 slots** | D(reserve) 0.57 GiB = D(arena) 0.57 GiB = +248 slots. The 6.1e9 -> 3.15e9 pool cut (2.75 GiB) bought **zero** arena slots. It buys runtime HEADROOM, which is still worth having - the shape it replaced died with `0 bytes free` on a real 995-token request. Grow the arena by lowering `max_model_len`, or by taking the overhead term with `--expert-precise-memory` (below); the pool is the headroom lever only. #### The other arena lever: `--expert-precise-memory` (overhead 7.60 -> 5.25 GiB) The flag is now ON. It was refused on 2026-10-01 for a reason that does not generalize: the 2 x 262144 bf16 shape was already at the ceiling (weights 6.28 + pool 6.80 + graphs 0.55 + arena 16.65 + non-torch ~1.45 = 31.73 of 31.86 GiB, NRestarts=3 at ~6.7 min each), so the 2.35 GiB the flag moves into the arena was the last stack over the line, not the cause. The plain bf16 2 x 262144 boot also did not stay up: 0 bytes free at runtime, and a real client request (995-token prompt, `max_tokens=261149`) killed the worker with `Tried to allocate 50.00 MiB ... 0 bytes free`. At 200704 x 3 the flag fits with room and is worth **+1,026 slots (+16%)**. What made it affordable is the window trim, not a pool cut: the arena is DEVICE-resident, the pool is not part of the budget's arithmetic, and the 200704 window's KV reserve (1.91 GiB) plus a smaller attention footprint is what the plan needed. The pool stays at 3.15e9/rank deliberately (option A) - cutting it further would cost concurrency (2.2e9 -> 1.38x) and buy no slots. Boot-probed 2026-10-02 (transient systemd units at the production uid + memlock, full-log capture; the rootless bench probe had masked the production memory environment). The night searched two concurrency contracts: | rung | result | |---|---| | 3 x 262144, bf16, KV 11,047,010,304 | **OOM at graph capture** — `Tried to allocate 76.00 MiB ... 0 bytes free`, 30.43 GiB of 31.86 GiB already allocated by PyTorch | | 3 x 245760, bf16 | **OOM identically** | | 3 x 237568, bf16 | PASS — KV 718,606 tokens | | 2 x 262144, bf16 + `--expert-precise-memory` | **DEPLOYED then crashed** — 0.13 GiB free at boot, NRestarts=3 (the ceiling, not the flag) | | 2 x 262144, bf16 (no precise flag) | did not stay up either: 0 bytes free at runtime, worker killed by a real request | | 3 x 200704, fp8, pool sized for 2 contexts | **DEPLOYED 2026-10-02 (shape D)**: 398,658 tokens = 1.99x concurrency, arena 14.87 GiB / 6,496 slots, the 2.75 GiB/rank pool cut returned as runtime headroom | | **3 x 200704, fp8, + `--expert-precise-memory`** | shape E (2026-10-02): same 1.99x pool, overhead 5.25 GiB, arena 17.22 GiB / 7,522 slots, boot healthy in 420 s | | 3 x 200704, fp8, MNBT 2048, graph term 0.32 GiB, util 0.982 | shape F (2026-10-03): arena 17.52 GiB / **7,653 slots** measured, budget 31.29, `Estimated CUDA graph memory: 0.32 GiB`, KV unchanged 398,658 | | 3 x 200704, fp8, graph term 0.07 GiB, util **1.00**, KV 3.1665e9 | **DID NOT BOOT (2026-10-03)**: `ValueError: Free memory on device cuda:N (31.29/31.86 GiB)` on both ranks, twice, zero weights loaded — the worker demands free > budget and budget == total. UTIL 1.0 is unbootable, full stop | | **3 x 200704, fp8, graph term 0.07 GiB, util 0.982, KV forced to 400,700** | **DEPLOYED 2026-10-03 (shape G)**: arena **17.77 GiB / 7,762 slots** measured (+109 over F, +240 over E), KV **401,408 tokens / 2.00x** concurrency measured, capture 0.31/0.10 GiB vs the 0.07 charge (under-charged, boots and holds), driver used 30.97 GiB -> **0.89 GiB real free per card** held through a live 4,096-token generation (~38-39 tok/s single-request, +13% vs the 33.99 shape-E baseline); rollback is UTIL or KV_BYTES down a notch | Six full-length contexts cannot fit at any KV dtype: the 6-seq graph/activation footprint leaves only 9.18 GiB for the pool, and 6 × 262144 needs 12.47 GB even at fp8. The old "KV_BYTES is PER RANK, 9/16 GiB OOM" law was the MTP4 WORKSPACE reservation, not a VRAM wall. Failures are clean and diagnostic: the engine reports `torch.OutOfMemoryError: Tried to allocate X GiB. GPU N has ... of which Y GiB is free`, and the fork's giant ERROR lines are split by journald — read them via `journalctl -o json`, not `-o cat` (and `podman logs`, which is only available while the container lives). Rollback to the 2026-09-29 fp8+MTP4 shape: git tag `frank-primary-fp8-mtp-20261001` (env trio + seam re-pinned there). ## Parallelism options on this box (2026-10-01 survey) Surveyed from the running image's own CLI surface. Verdicts are specific to dankserver: 2x R9700 (gfx1201), a ~172.8 GiB expert-offload MoE, and a model with only 2 KV heads. | option | flag | verdict on this box | |---|---|---| | TP | `--tensor-parallel-size` | **DEPLOYED, =2.** All-reduce dispatch shows `R4D_AR` then `PYNCCL` | | DCP | `--decode-context-parallel-size` | **REJECTED at TP=2** — see below | | PP | `--pipeline-parallel-size` | supported; untried (stage handoff plus a TP-keyed expert plan) | | DP | `--data-parallel-size` (+`-rank`, `-start-rank`) | impossible: every replica holds a full model copy (172.8 GiB vs 31.86 GiB/card) | | EP | `--enable-expert-parallel` + `--all2all-backend` (+`--enable-eplb`, `--enable-dbo`, `--enable-elastic-ep`) | supported (`naive`, `allgather_reducescatter`, `deepep_high_throughput`, `deepep_low_latency`, `pplx`, `mori`, `nixl`); would replace the fork's tiered-offload LOCAL gather with a per-layer PCIe all-to-all | | multi-node | `--nnodes`, `--node-rank`, `--master-addr/port`, `--distributed-executor-backend` | supported; the actual path to more cards | ### DCP is structurally unavailable here — do not re-test on 2 GPUs `--decode-context-parallel-size 2` fails `VllmConfig` validation at boot: ``` Decode context parallelism for GQA/MQA requires `--tensor-parallel-size` (2) to be greater than the model's total number of KV heads (2). Increase `--tensor-parallel-size` or set `--decode-context-parallel-size 1`. ``` DCP shards the KV along the *sequence* dimension after KV heads have been spread across the TP ranks, so it requires **TP > num_kv_heads**. This model has 2 KV heads (24 Q heads, GQA 12:1), so TP must be >= 4 — four GPUs, not two. Related fork facts: DCP *reuses* the TP ranks rather than multiplying them (it is not `TP x DCP`), and with PCP enabled the valid DCP sizes are `{1, pcp, tp x pcp}` (`vllm/config/parallel.py`). The KV memory a DCP win would have bought is real — each rank would hold half the sequence's KV — but it is unreachable without two more cards. ### Measuring decode: the harness lives INSIDE the container `/home/dank/mtp_measure.py` imports `/bench_iter.py` by absolute path, so it must run in the engine's namespace and the files must be copied in first: ``` podman cp /home/dank/mtp_measure.py frank-primary-qwen:/ podman cp /home/dank/bench_iter.py frank-primary-qwen:/ podman exec frank-primary-qwen python3 /mtp_measure.py ``` Without the copy the exec dies with `can't open file '/mtp_measure.py': [Errno 2] No such file or directory` — measured 2026-10-01, which silently cost a whole arm's numbers. A window script must do the copy, not assume the path exists. ### The then-deployed shape, measured again (2026-10-01 A/B window) A clean production-environment boot of the config deployed at that time (4 x 262144 fp8, MNBT 2048 in that window) reported **`GPU KV cache size: 1,079,582 tokens, Maximum concurrency for 262,144 tokens per request: 4.12x`** — the same figures as the deployed MNBT-1024 shape, i.e. MNBT does not move the pool, and the capacity is reproducible across boots. ## See Also - [[dankserver-frontier-moe-feasibility]] — the sibling feasibility page (which frontier MoE fits this box; the same PCIe-expert-stream wall at V4.1 scale) - [[frank-primary-r9v-radiance-qwen38-q8]] — RETIRED 2026-09-30 (the R9V fork era; production has been the tcclaviger fork since) - [[x4-pair-experiment]] — the same residency law measured on a narrower link: streaming over PCIe loses, computing in RAM does not care - [[Dual-R9700-Cooperation]] — the board swap that made TP=2 legal here