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@@ -128,6 +128,7 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
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+| [Z-Image-Turbo on a 5090: few-step distillation, measured](reports/z-image-turbo.md) · [chart](reports/z-image-turbo.png) · [samples](reports/assets/zimage-montage.png) | First RTX-5090/sm_120 numbers for Z-Image-Turbo, an open-weights (Apache-2.0) 6B text-to-image DiT with a Qwen3-4B text encoder, measured out-of-box (bf16, SDPA, no torch.compile, no quant). A 1024px image takes 3.18s (~19/min), a 512px one 0.83s. The "turbo" is distillation from the ~50 denoising steps a normal diffusion model runs down to 8, and compute is exactly linear in the step count (each step is one DiT forward): 1.69s at 4 steps, 3.20s at 8, 6.23s at 16, with 4 steps near-indistinguishable from 8 on portraits. Resolution is the real cost, not memory: 0.83/3.18/9.99/23.36s at 512/1024/1536/2048, super-linear because attention scales with pixel count squared. The whole model fits a 32GB card at every tested size (true per-image peak 20.4/21.7/24.6/28.8GB), so 2048px keeps ~4GB of headroom: a time wall, never a VRAM wall. The classically-hard cases hold at 8 steps, where it renders exact text on a sign ("WITCHEER", letters correct) and draws exactly-N objects on request, with colours and spatial relations landing too (sample grid attached). Measurement note: per-image VRAM comes from torch.cuda.max_memory_allocated() with reset_peak_memory_stats() each iteration, because nvidia-smi over-reports. PyTorch's caching allocator retains its high-water mark and never releases it between generations, flattening a naive VRAM curve to a wrong constant. Speed and footprint only; a GenEval quality pass (its mmcv/mmdet detector needs a from-source sm_120 build) is the natural follow-up. Companion to the ACE-Step music bench, the other few-step distilled generator on this rig. |
 | [Nemotron-TwoTower autopsy: a 2.4x speedup that costs a second 30B model](reports/nemotron-twotower-autopsy.md) · [chart](reports/nemotron-twotower-autopsy.png) | NVIDIA's Nemotron-TwoTower is a diffusion LM adapted from a frozen autoregressive Nemotron-3-Nano-30B that generates 2.42x faster than plain AR at 98.7% quality (arXiv 2606.26493) — clever, and datacenter-only by construction. The speedup comes from running two full 30B backbones co-resident: a frozen context tower plus a trained denoiser that unmasks several tokens per diffusion step. The checkpoint ships both stacks (126GB bf16, ~63B params) and the card requires 2x80GB (~59GB/GPU). The towers can't be shared (the paper's own ablation: tying is "substantially worse") or run sequentially (the denoiser cross-attends to and seeds Mamba state from the live context tower every block-step), so the 2.42x is bought by doubling the model's resident memory. The comparison that matters: on one RTX 5090, speculative decoding already delivers the same multiplier for a draft head in single-digit GB — from this rig's t036 spec-decode run on Gemma-4-26B-A4B (sm_120): MTP 2.13x, DFlash 2.19x, EAGLE-3 1.69x, for ~0-2GB of drafter (MTP ships inside the model). Same ~2.2x speedup, ~60x the memory. Two honest caveats: different mechanisms on different models (an architectural argument, not a controlled A/B — TwoTower won't run on the rig), and TwoTower's 2.42x is measured vs plain AR with no speculative-decoding baseline, so against a production AR server already at ~2.2x via spec-decode the marginal win mostly disappears while still costing a second 30B backbone. Doesn't run on consumer hardware at all: 126GB exceeds a 96GB box even fully offloaded, no quant of the custom trust_remote_code arch exists, and the mamba_ssm/causal_conv1d kernels are the usual Blackwell build wall. The two HF repos are the same checkpoint (config + shard sha256 identical; -Labs- adds inference.py). Arithmetic autopsy from the published artifact, no 5090 run. Companion to the GLM-5.2 datacenter-only autopsy and the spec-decode three-way. |
 | [Making music on a gaming GPU: ACE-Step 1.5 writes a 4-minute song in 1.75s](reports/ace-step-music.md) · [chart](reports/ace-step-music.png) | First RTX-5090/sm_120 numbers for ACE-Step 1.5, an open-weights text-to-music model. One gaming GPU generates a full 4-minute song in 1.75s of compute (2B turbo, DiT-only, bf16, 8-step, batch 1) — level with the model authors' own A100 claim (~1-2s) and ~6x past the RTX 3090 (<10s), at 137x real-time. The higher-quality XL 4B tier costs ~1.65x the time (2.9s, 83x) and ~60% more VRAM (14.8 vs 9.4GB); both fit far inside 32GB, and XL would run on a 16GB card. Real-time factor RISES with length — 81x at 30s to 137x at 4min — because the turbo model's step count is fixed at 8 (distilled from ~50), so a 4-minute track is ~5x the compute of a 30-second one, not 8x. Measured out-of-box with no torch.compile and no quantization: a floor, not a ceiling. Speed only — audio quality is left to the ear (paired 2B-vs-XL samples), a prompt-alignment score the natural follow-up. Companion to the DiffusionGemma AR-vs-diffusion null. |
 | [Bias-only steering: nothing moves at bounded budget, and random rewards match correct ones](reports/bias-only-steering.md) · [chart](reports/steering-claimed-vs-measured.png) | Bias-Only Reasoning Steering (arXiv 2505.18706, EMNLP 2025) claims RL-training one bias vector per layer (~0.0016% of params, added to mlp.down_proj) matches full RL fine-tuning: Qwen2.5-Math-7B MATH500 52.2 to 79.9 (steering even beats full-FT). Their pinned stack is dead on arrival on consumer Blackwell — torch 2.6.0+cu124/vllm 0.8.5 fails its first kernel launch on sm_120 — so the recipe was reimplemented from their own configs (RLOO, steering lr 1e-3, qwen_math template, DeepScaleR) at a matched bounded budget (20 steps x 8 prompts x 8 generations, ~1,280 rollouts vs their ~645K), plus the controls neither paper reports: their-own-config LoRA (r4, down_proj only), random-reward steering (Spurious-Rewards protocol), and a zero-training 'To'-prefix probe of their companion paper's first-token-substitution mechanism. A five-arm null: base 54.6 MATH500 / 45.0 AMC23 (reproduces their 52.2/45.8 starting point), steering 54.4/45.0, LoRA 53.8/40.0, random-reward steering 54.2/45.0, 'To'-prefix 53.0 — every arm is the base. Correct rewards buy nothing over coin flips at this budget, and the claimed ~10-11pt 'To'-prefix gain lands at -1.6 on the standard template, where the base's generations already open with 'To'. Wall-clock decomposition (identical across arms): rollouts 75%, backward+update 25%, grading under 1% — the '34s vs 52m' headline counts only the optimizer sliver, and the slice that shrinks with trainable-param count is ~none of a step; on 32GB the real bias-only win is memory (full-param 7B RL does not fit at all; ~100K bias params train comfortably). Bounds where the gain is not (early), does not refute their full-recipe endpoint. Steering checkpoints served in stock vLLM via a Qwen2-to-Llama re-badge (mlp_bias=true), fp32-verified logit-identical. |