Metacognition Adapters
Collection
Per-model metacognition adapters from VIDRAFT Darwin/Chimera platform + AETHER metacognition-emergence technology. ā¢ 11 items ā¢ Updated ā¢ 22
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cutechicken
AI & ML interests
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reacted to SeaWolf-AI's post with š 5 days ago
š Adding a GPU without building one
AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere ā how efficiently you use the GPUs you already have.
Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU ā the effect of plugging in one more "virtual GPU."
VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):
Qwen3.5-35B-A3B (MoE): 25.7 ā 601 tok/s (23.4Ć)
Darwin-36B-Opus (in-house MoE): 25.0 ā 280.8 (11.2Ć)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible ā model + serving shipped as one container.
docker pull vidraft/qwen35-vkae:601
Don't take our word for it ā run it yourself. The mechanism will be released as a paper.
š Leaderboard & demo š https://huggingface.co/spaces/VIDraft/vkae
Articles š https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard liked a model 5 days ago
FINAL-Bench/Darwin-36B-Opus-VKAE liked a model 5 days ago
FINAL-Bench/Qwen3.5-35B-A3B-VKAEOrganizations
upvoted a collection 9 days ago
upvoted an article 24 days ago
Article
FINAL-Bench Quantum: An Open, Neutral Benchmark for Quantum-Computing Methods
FINAL-Bench
ā¢ ā¢ 17 upvoted a paper about 2 months ago
upvoted an article 5 months ago
Article
FINAL Bench: The Real Bottleneck to AGI Is Self-Correction
FINAL-Bench
ā¢ ā¢ 20 upvoted 2
collections about 1 year ago