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gemma-4-26B-A4B-it (Gemma 4 MoE; 26B total, ~4B active) via speculative decoding.
The public RedHat draft is English-only (Magpie + UltraChat) and accepts Korean
tokens poorly, so this draft was retrained on Korean prompts with on-policy
responses regenerated by the verifier.vllm-project/speculators)RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic (FP8 MoE; serving + hidden-state extraction)RedHatAI/gemma-4-26B-A4B-it-speculator.eagle3instruction column only). Answers regenerated on-policy by the verifier (text-only).1VLLM_USE_FLASHINFER_SAMPLER=0 vllm serve RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic -tp 1 \
2 --max-model-len 4096 \
3 --limit-mm-per-prompt '{"image":0,"audio":0,"video":0}' \
4 --speculative-config '{
5 "model": "<this-repo-id>",
6 "num_speculative_tokens": 3,
7 "method": "eagle3",
8 "draft_tensor_parallel_size": 1
9 }'num_speculative_tokens (3–6) on measured acceptance / TPS. The draft uses the
verifier's tokenizer and chat template. The 26B-A4B is multimodal; for text workloads
pass --limit-mm-per-prompt to disable image/audio/video.out of resource: shared memory error, use a vLLM MoE
fallback config or the NVFP4 path.RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic. Pairing
with a different target (or a base, non--it model) will change results.