reports/ornith-1-5-35b.md

Ornith 1.5 35B-A3B on one RTX 5090: dense-27B-class answers at 3.8x the decode speed

TL;DR. Ornith 1.5 35B-A3B (MoE, ~3B active) at the vendor's first-party Q4_K_M fits an RTX 5090 in 21.4 GiB and decodes at 303 tok/s — ~3.8x the dense Qwen3.8-27B rung on the same card. It pays ~3.8 points on the five-task board (89.4 vs 93.2) but holds the harder floor: GPQA-diamond 52.0 think-off / 81.8 think-on, level with the dense rung in both regimes (50.5 / 80.8). The NVFP4 build reproduces the board within noise (89.3) at ~300 tok/s via vLLM, so llama.cpp and vLLM are both live serving paths. One negative result: the shipped MTP speculative head measures slower than base decode on vLLM 0.25.1 (245-254 vs ~300 tok/s).

Setup

• Hardware: RTX 5090 32GB (sm_120), single card, single stream.
• GGUF leg: vendor first-party Q4_K_M, 20.21 GiB on disk, llama.cpp b9653, fully VRAM-resident (peak 21.4 GiB). Speed via llama-bench; quality via llama-server chat completions on the standing five-task harness (MMLU/HellaSwag 50% stratified sample seed 42, ARC-C/GSM8K/HumanEval full), greedy, thinking off.
• NVFP4 leg: compressed-tensors NVFP4 build (23.4 GB), vLLM 0.25.1 on the native cutlass sm_120 FP4 path, same harness over /v1.
• GPQA-diamond: standing second-tier metric (198 items, zero-shot, deterministic option shuffle, greedy). One item is ~0.5 pts; gaps under ~3 pts are noise.
• Ornith 1.5 is a reasoning model; every quality number here is think-off for board parity. Think-off scores understate what the model does with its reasoning channel on, and none of these numbers are comparable to think-on sampled vendor-card figures.

Speed (Q4_K_M, llama.cpp)

test	tok/s
pp512	8,952.96 ± 52.92
pp16384	8,436.28 ± 29.60
tg128	303.21 ± 2.16
tg128 @ d8192	282.61 ± 3.38
tg128 @ d32768	258.20 ± 1.16

Depth rows are from the day-0 sweep (same build, tg128 measured 295.6 that day; the two runs bracket ~300). Holding 87% of empty-context decode at 32k depth is a flat curve for this class, consistent with the hybrid-attention design.

Same-GPU MoE context: Qwen3.6-35B-A3B UD-Q4_K_M does 271 tok/s, the smaller Nemotron A3B pair 351-364. The dense contrast is the story: Qwen3.8-27B Q4_K_M decodes at 78.9 tok/s on this card, so the MoE is ~3.8x faster.

Quality (think-off, greedy)

task	Ornith 1.5 35B Q4_K_M	Ornith 1.5 35B NVFP4	Qwen3.8-27B Q4_K_M (dense)
MMLU	82.24	81.80	85.01
ARC-Challenge	94.11	94.88	96.76
HellaSwag	91.00	90.94	94.30
GSM8K	91.66	92.27	97.12
HumanEval	87.80	86.59	92.68
q_avg	89.36	89.30	93.17
GPQA-diamond	52.02	not run	50.51

Two reads:

1. The MoE trade costs easy-board points, not reasoning floor. ~3.8 q_avg under the dense rung, spread across all five tasks — but GPQA-diamond is level with it (52.0 vs 50.5 is inside the 3-pt noise floor). This is the mirror image of an aggressive-quant trade measured the same week on this rig, where the easy board barely moved and GPQA paid heavily. The board and the second-tier metric keep disagreeing in informative ways; run both.
2. NVFP4 is quality-parity here. 89.30 vs 89.36, every per-task delta ≤ 1.2. Same pattern as the other NVFP4 pairs on this board.

Long-context retrieve-and-use (15 tasks/depth): 97.78 overall — 100 @16K, 93.33 @32K, 100 @64K. The dense rung scores 100 flat; the 32K dip is one task, single run.

Think-on addendum (added same day)

GPQA-diamond re-run with the reasoning channel on (max_tokens 16384, ctx 24576, zero-shot, greedy — the identical recipe used for the Qwen3.8-27B think-on calibration legs):

model	GPQA think-off	GPQA think-on	delta
Ornith 1.5 35B Q4_K_M	52.02	81.82	+29.8
Qwen3.8-27B Q8_0 (dense)	47.0	80.81	+33.8
Qwen3.8-27B Q6_K (dense)	48.99	79.29	+30.3
Qwen3.8-27B UD-IQ3_XXS (dense)	45.0	78.79	+33.8

Thinking is worth ~30 GPQA points on this model, the same band the dense ladder measured. Ornith lands at the top of the think-on field (81.8 vs 79.3-80.8), though the spread is inside the 3-pt noise floor — the honest read is level with the dense 27B rungs, at 3.8x their decode speed. Parse failures: 14/198, in line with the dense think-on legs (14-19). One regime note: these are greedy zero-shot numbers on the 198-item set; vendor-card GPQA figures are typically sampled with larger budgets and are not directly comparable.

NVFP4 speed, and the MTP surprise

Chat-server convention (decode_tps = completion/(total-ttft), single stream — not llama-bench tg128; medians of 3):

shape	base	MTP spec-decode
short-in / long-out	300.7	248.0
long-in / short-out	300.1	245.5
8k prompt depth	297.6	253.9
32k prompt depth	284.7	248.9

Base NVFP4 decode is ~300 tok/s and nearly flat with depth. The shipped MTP speculative head is a net loss on vLLM 0.25.1: every shape measures 15-18% below base. At ~300 tok/s base on an MoE with ~3B active parameters, the draft-and-verify overhead has almost no headroom to pay for itself; this mirrors the Nemotron-Lightning spec-decode result on this rig, where both shipped drafters also measured negative on the 5090 at batch 1. Spec-off is the right serve config on this stack today. Worth retesting on newer vLLM.

Worth it?

Worth it if you want dense-27B-class answers at almost 4x the decode speed on one consumer card: 21.4 GiB resident leaves ~10 GiB headroom, depth decay is mild, and both llama.cpp and vLLM serve it well. Not worth it if you need every point on knowledge-heavy short-context tasks — the dense 27B rung still wins the board by ~4 points, and the smaller Nemotron A3B pair is 16-19% faster if raw MoE speed is the only axis (at ~5 board points below Ornith).

Honest limits

• The five-task board is think-off for comparability with the banked rows; GPQA now carries both regimes (52.0 off / 81.8 on). A think-on board pass is the remaining gap.
• The 9B sibling is now benched: Ornith 1.5 9B report — the 35B dominates it on every measured axis on this card.
• No Qwen3.6-35B-A3B depth sweep yet for a like-for-like depth curve.
• GPQA-diamond is 198 items; treat sub-3-pt gaps as noise.
• Long-context and depth rows are single-run.

Sources

results/ornith-35b/{speed,quality,gpqa,longcontext_detail}.json, results/ornith-35b-nvfp4/{quality_nvfp4,speed_nvfp4,speed_mtp}.json, day-0 depth sweep 2026-08-20. llama.cpp b9653; vLLM 0.25.1; rig harness as pinned in this repo.