docs: DiffusionGemma research + first-hand smoke test on 3090 Ti
google/diffusiongemma-26B-A4B-it (released 2026-06-10) — Google's first open-weight text-diffusion LLM. Does NOT run in Ollama (unknown arch 'diffusion-gemma'); built llama-diffusion-cli from ggml-org/llama.cpp PR #24423 and smoke-tested Q4_K_M on steel141's 3090 Ti. - New reference doc with specs, build recipe, throughput, and gotchas - CORPUS_ollama_variants.md: "not an Ollama variant" callout - README index line for the reference doc - scripts/diffusiongemma-smoketest/ harness + raw result logs Findings: ~106 tok/s effective / ~2030 tok/s in-step-parallel; correct code + coherent reasoning; <|channel>thought CoT eats the 256-tok canvas so strict short formats need block budgeting. nvidia-smi index != CUDA index on steel141 (select 3090 Ti by UUID). Experimental research artifact, not homelab-deployable until diffusion arch merges to llama.cpp mainline. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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0.00.896.419 W load: control-looking token: 212 '</s>' was not control-type; this is probably a bug in the model. its type will be overridden
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0.00.897.085 W load: control-looking token: 50 '<|tool_response>' was not control-type; this is probably a bug in the model. its type will be overridden
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0.00.933.523 W load: special_eog_ids contains '<|tool_response>', removing '</s>' token from EOG list
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0.04.856.194 I diffusion: -n 128 -> 1 blocks, n_ubatch=2304 n_batch=2304 n_ctx=2304 (canvas_length=256)
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0.04.856.197 I diffusion: --fit has no effect here; context is sized from -n and the canvas. Set -ngl / --n-cpu-moe to control device memory.
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0.04.864.961 W llama_context: n_ctx_seq (2304) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
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0.04.869.425 W sched_reserve: layer 5 is assigned to device CUDA0 but the Flash Attention tensor is assigned to device CPU (usually due to missing support)
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0.04.869.427 W sched_reserve: Flash Attention was auto, set to disabled
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0.07.226.382 I diffusion_params: steps=128 schedule=0 algorithm=4 temperature=0.800 eps=0.001000 mask_token=4
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0.07.226.626 I diffusion_eb: max_steps=48 t=[0.400,0.800] entropy_bound=0.1000 stability=1 confidence=0.0050 kv_cache=on gpu_sampling=on sample_reduce=on
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0.07.228.370 W init: embeddings required but some input tokens were not marked as outputs -> overriding
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0.07.721.777 I
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diffusion step: 0/48 [ ] 0%0.07.822.673 I
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diffusion step: 1/48 [= ] 2%0.07.924.966 I
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diffusion step: 5/48 [===== ] 10%0.08.346.392 I
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diffusion step: 6/48 [====== ] 12%0.08.452.589 I
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diffusion step: 8/48 [======== ] 16%0.08.663.759 I
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diffusion step: 9/48 [========= ] 18%0.08.770.846 I
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diffusion step: 11/48 [=========== ] 22%0.08.984.389 I
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diffusion step: 12/48 [============ ] 25%0.09.090.492 I
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diffusion step: 13/48 [============= ] 27%0.09.198.211 I
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diffusion step: 14/48 [============== ] 29%0.09.304.460 I
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