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ginigini
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VKAE Accelerated
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Adding a GPU Without Building One
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๐ 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 โ 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 โ 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 โ 601 tok/s (23.4ร) Darwin-36B-Opus (in-house MoE): 25.0 โ 280.8 (11.2ร) 10,000+ tok/s peak aggregate under concurrency The key: it's reproducible โ model + serving shipped as one container. docker pull vidraft/qwen35-vkae:601 Don't take our word for it โ run it yourself. The mechanism will be released as a paper. ๐ Leaderboard & demo ๐ https://huggingface.co/spaces/VIDraft/vkae Articles ๐ https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard
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A Family of Open Sourced Music Foundation Models
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