vLLM says it now supports a hybrid Mamba-Transformer mixture-of-experts model paired with a C-RADIOv4 vision encoder. The serving stack includes multi-token prediction (MTP) for speculative decoding, continuous batching and prefix caching. The post says this brings visual understanding and long-context reasoning to an OpenAI-compatible endpoint for document, research and video workflows.
Part of a thread · earlier post
Under the hood: a hybrid Mamba–Transformer MoE paired with a C-RADIOv4 vision encoder.
vLLM supports the model's multi-token prediction (MTP) path for speculative decoding, alongside continuous batching and prefix caching.
That brings visual understanding and long-context reasoning to an OpenAI-compatible serving endpoint for document, research and video workflows.
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Source: vLLM · x.comPublished · added here
