reports/keye-vl-2-autopsy.md

Keye-VL-2.0-30B on a consumer GPU: an autopsy — five measured walls between "open weights" and a running model

Rig: one RTX 5090 32GB · 64GB RAM · CUDA 12.8 (sm_120) · transformers 4.57.1 through 5.11 + 5.0.0rc0-rc3 tested Model: Kwai-Keye/Keye-VL-2.0-30B-A3B (Apache 2.0, 30B/3B-active MoE, 62.3GB bf16) — Kuaishou's long-video VLM (arXiv 2606.10651): DeepSeek-style sparse attention adapted to a GQA multimodal stack, claiming "lossless" 256K context / hour-level video. Released ~2 weeks ago; zero community local-run reports existed before this attempt. This is what happened when one tried.

Wall 1: the 256K claim is dead on arrival at 32GB — by arithmetic

DSA here is compute sparsity, not KV compression: the full GQA KV cache is stored. 48 layers x 2 x 4 KV heads x 128 dim x bf16 = 96 KiB/token → 25.8GB at 256K, before a single weight. With 62.3GB of bf16 weights and no quant path (wall 3), no configuration of this model reaches 256K on any consumer card. The headline claim is datacenter-only.

Wall 2: the "sparse" attention is O(N²)-memory on consumer stacks — measured

The official fast path (forked SGLang + DeepGEMM + custom kernels, H800 x2 reference config) does not build on sm_120 — DeepGEMM has no consumer-Blackwell support. The shipped fallback is a pure-PyTorch reference implementation of DSA, and its indexer materializes the full N x N score matrix (16 heads, bf16 = 32·N² bytes) before top-k:

context	indexer scores	KV cache
8K	2.1GB	0.8GB
32K	32.9GB — measured: one 30.65GiB allocation, OOM	3.2GB
256K	2.1TB	25.8GB

A 60-second video prompt (~32K tokens) OOMs a fully-drained 32GB card on the indexer scoring step alone. The sparse-attention model is more memory-hungry than dense attention everywhere its custom kernels don't exist.

Wall 3: 4-bit quantization reaches 4.7% of the model — measured

The experts are stored fused — 3D nn.Parameter tensors ([128, 2048, 1536] per layer), not nn.Linear — so every consumer quantizer (bnb, torchao) walks right past them: 452 Linear modules = 1.46B of 31.12B params (4.7%) is all that 4-bit can touch. No official quants exist, llama.cpp has no arch support (and no GGUFs), vLLM/SGLang mainline don't register KeyeVL2, so the AWQ route is closed too. The only community conversions that reportedly run are MLX 4-bit on Apple silicon (untested here). On CUDA, bf16 + CPU offload is the only load that exists: 58GB footprint, 0.5 tok/s.

Wall 4: the code targets a one-week transformers API window

The trust_remote_code modeling file requires, simultaneously: OutputRecorder (removed in 5.2), factory-form check_model_inputs (absent in 4.57), SlidingWindowCache (removed in 5.0.0rc3), and the pre-flip fused-expert layout (changed in 5.0.0rc2). The intersection: transformers 5.0.0rc0 / rc1 — two release candidates — and nothing else, before or since. On stable 5.0/5.11 it took six compatibility shims (a pure-PyTorch Hadamard stand-in for the unbuildable fast-hadamard-transform, a metadata-complete flash_attn stub to pass a module-level availability assert, dead-import cache classes, a rope-init function, config attribute patches, a rotary-class method) plus a 62GB expert-tensor re-layout just to reach a forward pass.

Wall 5: it still doesn't work — and the diagnosis trail is exhaustive

On rc1, with the untouched original checkpoint and native semantics, generation is incoherent noise. The per-layer trace shows healthy hidden-state norms through all 48 layers with garbage logits — well-scaled but semantically destroyed computation. Systematically exonerated: the DSA path (the built-in dense fallback is equally broken), expert orientation (dimensionally provable — gate_up must map 2048→1536, no transpose ambiguity exists), gate/up chunk order (both tested), rope theta (10M, confirmed loaded), M-RoPE text positions (explicit equals default), causal-mask alignment. Untried: pure-CPU inference, transformers git-commit archaeology, and the official Hopper-only docker — each past the point of reasonable cost.

Verdict: thirteen run attempts deep, Keye-VL-2.0-30B does not produce coherent output on consumer hardware through any reachable configuration. For contrast, on this same rig: HRM-Text-1B ran after one missing tensor was supplied; LocateAnything-3B ran after one config knob. The gap between "open weights" and "runnable weights" has never measured wider.

Honest caveats

• A subtle interaction with our compat shims cannot be 100% excluded as the noise source — though the text path exercises none of their math (their attention is hand-rolled eager, the Hadamard rotation only feeds top-k selection, and the dense fallback bypasses it entirely and is equally broken).
• "Lossless 256K" itself was never benchmarkable here; the paper publishes no needle-in-video curves either — the claim rests on benchmark aggregates.
• The model may be excellent inside its intended habitat (H800 pairs, their docker, their SGLang fork). Every wall above is about the release engineering, not the research.

Reproduce

scripts/keye_probe.py (shims + smoke + quant verdict) · scripts/keye_convert.py (expert re-layout, unnecessary on rc0/rc1) · scripts/keye_diag.py (layer-norm trace) · scripts/chart_keye_walls.py. Environment: uv venv, torch 2.11+cu128, transformers==5.0.0rc1, bf16, device_map=auto (28GiB GPU / 42GiB CPU).