One signal

vLLM says its serving software now supports Ling-3.1-flash from day one. The model has about 560 billion total parameters, around 25 billion active per token and a context window of up to one million tokens, according to vLLM.

That gives teams another route to serve the model on their own infrastructure, rather than waiting for later support in the inference stack. A sizeable model, then, with a rather less sizeable active slice.

Discuss: What matters more for self-hosted models: day-one serving support or dependable performance on real workloads?

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