Qwen3.5-122B-A10B (MTP)
benched · production incumbent · 122B / 10B active, MoE
Verdict
Qwen3.5-122B-A10B-MTP is the production incumbent, and on this hardware it earns the
label. It passes its guard clean at every depth tested, and a single cherry-picked
KV-dequantisation patch recovers up to 70.3% of the decode speed a stock build was
leaving on the table at 204,800 tokens — production’s own context
(clm-0022 measured-here high). Its real τ²-bench airline score
is 0.545 at n=22, not the 1.00 that early five-task arms reported, which sampled only the
easy end of the domain (clm-0037 measured-here medium). On
tasks it does complete, it is fast: a median of 19 turns and 2.0 minutes to a correct
answer, well ahead of the only other model measured at comparable scale
(clm-0039 measured-here medium). Thinking is a net negative
for this model specifically — turned on, it deadlocks two of five adversarial tasks rather
than solving them, where turning it off solves all five in about ten minutes
(clm-0033 measured-here high). Quantised KV is now close to
free: the same dequant patch cuts its energy cost 42% on fixed-token throughput work and
turns a 39%-turn agentic penalty into roughly 9%
(clm-0041 measured-here medium,
clm-0045 measured-here medium). At 6.81 Wh per correct τ²
answer it is also the cheapest model measured by a wide margin
(clm-0042 measured-here medium). The open confound is the user
simulator: every comparison against another model ran this model as both agent and
simulator, which is disclosed rather than papered over
(clm-0043 measured-here high).
Key numbers
| Metric | Value | Claim |
|---|---|---|
| Decode @ 0 tokens | 21.90 tok/s | clm-0022 measured-here high |
| Decode @ 32,768 tokens (f16 KV) | 18.16 tok/s | clm-0022 measured-here high |
| Decode @ 204,800 tokens (q8_0 KV, patched) | 9.69 tok/s | clm-0022 measured-here high |
| Decode @ 204,800 tokens (q8_0 KV, stock) | 5.69 tok/s | clm-0022 measured-here high |
| τ²-bench airline reward | 0.545 ± 0.208 (n=22) | clm-0037 measured-here medium |
| Turns to a correct answer, median | 19 turns / 2.0 min | clm-0039 measured-here medium |
| Energy per correct answer (f16 KV) | 6.81 Wh / 0.21 p | clm-0042 measured-here medium |
| Thinking, τ² airline | OFF 5/5 @ 1.00 · ON 3/5 (2 deadlock) | clm-0033 measured-here high |
Decode figures are stock UD-Q4_K_M unless noted “patched” (KV-dequant cherry-pick applied). τ² and energy figures are thinking-off, stock build, f16 KV unless noted.
Guard status
Clean, 4/4. Capability guard at depth 8,000 — coherence, tool-call, isolation, needle
retrieval — passes in full on the current benchmark config
(run-0005, cfg-0006, aihydra, stock commit 3653e6d). No
retraction or open guard failure attaches to this model; the failure mode this class of
guard is designed to catch (gpt-oss-120b’s stale-answer bug) does not appear here.
Levers that matter for this model
- KV-dequantisation patch (
ce7689f, cherry-picked onto stock3653e6d) — the single highest-leverage lever on this model. +70.3% decode at 204,800 (clm-0022); cuts fixed-token energy 42% at 200k context and makes patched q8_0 cheaper than f16 (clm-0041); closes the agentic turn penalty from +39% to +9.3% (clm-0045). Adopting it means carrying a second fork divergence alongside the.ckptsidecar already in production. - Thinking — off. On this model, thinking is a pure liability: no measured accuracy
gain and total failure on 40% of an adversarial task set
(
clm-0033). This is model-specific — the same lever helps two of the three other models measured — so it must not be set as a global default. - f16 vs q8_0 KV, turn cost — the metric that actually moves. Reward barely shifts
(1.000 vs 0.889 on 9 paired tasks) but q8_0 takes +39% more turns on the stock build,
which a coarse binary reward metric hides entirely
(
clm-0038measured-heremedium). Patched, the same paired design shows +9.3% (clm-0045). - MTP (draft-mtp speculation) — production’s shipped config runs
--spec-type draft-mtp --spec-draft-n-max 6, delivering roughly 1.45x on this model (clm-0026measured-herehigh, citing sibling work toclm-0022). The shipped--spec-draft-n-max 6sits ~18% below the measured optimum of 2-3 — an untuned setting, not a broken one (clm-0022note).
Energy
| Workload | KV | Wh | pence | Claim |
|---|---|---|---|---|
| τ² per correct answer, matched tasks | f16 | 6.81 | 0.21 p | clm-0042 measured-here medium |
| τ² per correct answer, matched tasks | q8_0, stock | 9.48 | 0.29 p | clm-0042 measured-here medium |
| Fixed-token, 200k context | f16 | 89.55–90.43 | ~2.7 p | clm-0041 measured-here medium |
| Fixed-token, 200k context | q8_0, patched | 85.25 | 2.58 p | clm-0041 measured-here medium |
| Fixed-token, 200k context | q8_0, stock | 146.08 | 4.43 p | clm-0041 measured-here medium |
Wall-metered at the smart plug, whole-box, at 30.3 p/kWh grid import. Idle floor is
10.1 W; under τ² load this model draws ~154 W mean
(clm-0040 measured-here low, content/idle-baselines/idle-aihydra-2026-08).
The two workloads invert on KV quant for the same reason: fixed-token work pays for wall
time, and the patch cuts wall time; agentic work pays for turns, and unpatched q8_0 takes
more of them (clm-0041). Self-play overhead (this model generating
both agent and user-simulator turns) inflates the per-task figures by an estimated
20–25%; correcting for it widens the gaps rather than changing the ranking
(clm-0042).
Open questions for this model
- Is the patched +9.3% turn residual real or noise? n=11 pairs, one seed — needs
repeats before it is a number rather than a direction
(
clm-0045). - Every comparison against another model is provisional. The user simulator was never
pinned — this model played both agent and simulator in each arm — so the 122B-vs-Nemotron
and thinking-on/off results need re-running under an independent, pinned simulator before
they are load-bearing (
clm-0043). - MTP draft verification may pass corrupted tokens. A community PSA reports this as an
unresolved hazard on gfx1151 HIP, “not even a depth of 1 is truly safe.” Production runs
MTP on this model; nothing in this project’s records addresses MTP output-correctness
directly — a correctness question, not a performance one, flagged for investigation
before any production restore (
clm-0044communitymedium). - Vision is one download away, unverified. The parent is natively multimodal and the
staged artifact carries the matching MRoPE sectioning, but an open upstream issue reports
CLIP-graph operator gaps with this model+mmproj pairing causing OOM on CUDA; unverified
on ROCm/Vulkan (
candidates/qwen35-122b, upstream issue #21268). --spec-draft-n-max 6is untuned. The measured optimum is 2-3; the shipped value costs roughly 18% of the available speculation gain and has not been corrected in production (clm-0022note).
Provenance
- Build (stock):
ggml-org/llama.cpp@3653e6d6d547ec763317d9ecd0ace334a7e21359(2026-08-07), ROCm 7.1.0,-fa on. - Build (patched): same base commit, cherry-picking
ce7689f(Nathanw1014’s upstream2a24abc, “CUDA: dequantize KV on load in the tile FA kernel, use it for quantized decode”). - Quant: weights UD-Q4_K_M throughout; KV f16 or q8_0/q8_0 as stated per row.
- Host: aihydra (current benchmark host, ~128 GiB GTT) for every figure above unless
noted. Production’s shipped config (
cfg-0002) ran on aibeast (96 GiB GTT, fork carrying the.ckptsidecar, no KV-dequant patch) — that box is currently down (board fault,inc-0005) and its data has been archived. - Dates measured: 2026-08-08 through 2026-08-11.
- Page reflects the record as of: 2026-08-11.