serverAppConfigs/serverAppConfigs-docs/LLM/dankserver-qwen38-fp8-expert-offload-tuning.md

---
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<N>.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 (2026-10-01: 5 x 262144, fp8, no spec)

```
FRANK_PRIMARY_MAXLEN=262144    x SEQS 4 (1,048,576 tokens of contract)
FRANK_PRIMARY_KV_BYTES=8311013376   PER RANK (fp8, pool scales with windows)
FRANK_PRIMARY_KV_DTYPE=fp8    --kv-cache-dtype fp8 (K and V together)
FRANK_PRIMARY_MNBT=1024       --max-num-batched-tokens 1024
R4D_LRU_GATHER_CHUNKS=32 / R4D_LRU_GATHER_LANES=64  (container env)
NO --speculative-config, NO --expert-prefetch
```

Boot-probed 2026-10-01 (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 × 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 × 245760, bf16 | **OOM identically** |
| 3 × 237568, bf16 | PASS — KV 718,606 tokens |
| **3 × 241664, bf16** | **PASS — KV 732,359 tokens, 3.03x** (max possible at 3 contexts) |
| 6 × 241664, fp8 | **OOM** — tried 10.70 GiB against 9.18 GiB free: six full-length contexts do NOT fit |
| 5 × 262144, fp8 | PASS on the first rung — KV 1,350,182 tokens, 5.15x |
| **4 × 262144, fp8** | **DEPLOYED.** Five slots booted but left 0 bytes free at runtime: a real client request (995-token prompt, `max_tokens=261149`) killed the worker (`Tried to allocate 50.00 MiB ... 0 bytes free`). Dropping one slot returns ~1.93 GiB per rank for activations and graph capture |

Boot is NOT proof of runtime survivability: the five-slot shape passed every
boot gate and still died on the first long-generation request. After any
knife-edge shape change, send a request with a large `max_tokens` before
calling it done. The deployed shape is **four contexts of the model's full
native 262,144 window**. fp8 KV is what buys the capacity —
7,926 B/token against bf16's ~13,900-14,047 — at the cost of bf16's ~4 %
decode edge. 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).

## 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